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PART II
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Population, Land Use, and Environment: Research Directions PART II PAPERS

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Population, Land Use, and Environment: Research Directions 3 Global and Case-Based Modeling of Population and Land Use Change Günther Fischer and Brian C. O’Neill INTRODUCTION Much of the recent literature on interactions between population and land use has focused on case studies at subnational geographic scales, from individual villages and districts (Walsh et al., Chapter 6) to larger regions, such as the Brazilian Amazon (Moran, Brondizio and VanWey, Chapter 5) and the U.S. Great Plains (Gutmann et al., Chapter 4). There is also an ongoing stream of research addressing questions at the global level, particularly as applied to assessments of biodiversity loss, climate change, and outlooks for agriculture and water. This chapter asks whether and how such global analyses can be informed and improved by recent case study research. Following this introduction, we review prominent global models of land use change, with a particular focus on the role of population. We conclude that the best way to bridge the gap between such models and case study research is through spatially explicit analysis at the level of large regions. We then describe one such research project, the CHINAGRO project at the International Institute for Applied Systems Analysis (IIASA), which is aimed at analyzing the potential impacts of trade liberalization and increasing incomes on the agricultural sector and on the livelihoods of the rural population that depends on agriculture in China. GLOBAL MODELING OF LAND USE Global models of environmental change simulate links among demography, economic growth, technological development, policy, and environmental outcomes to assess the state of current knowledge, help set re-

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Population, Land Use, and Environment: Research Directions search priorities, explore possible alternative futures, and test the potential effect of policies. Early, well-known attempts at global modeling—most prominently the World3 model from the Limits to Growth study (Meadows et al., 1972)—were heavily criticized for being too aggregated to be meaningful, thin on empirical or theoretical support for presumed quantitative relationships among variables, lacking in representation of price-mediated adjustment mechanisms, and insufficiently accounting for the role of technological change (O’Neill, 2001). A new generation of global models was developed in the late 1980s and early 1990s under the label of integrated assessment, primarily to assess the climate change issue and future scenarios for food and agriculture. These models rest on a much more rigorous framework than the earlier work, owing in part to a separate heritage in energy-economic models, which were then combined with simplified versions of well-developed disciplinary models of other components of global issues (Parsons and Fisher-Vanden, 1997). They have played prominent roles in recent assessments by the Intergovernmental Panel on Climate Change (e.g., Nakicenovic et al., 2000; McCarthy et al., 2001), the United Nations Environment Programme’s Global Environmental Outlook (United Nations Environment Programme, 2002), and scenarios for future ecological changes being produced for the Millennium Ecosystem Assessment. They have also figured prominently in prospective agricultural studies, including those carried out for the International Food Policy Research Institute (Rosegrant, Cai, and Cline, 2002a, 2002b) and the United Nations (Fischer, Shah, and van Valthuizen, 2002). Questions Addressed by Global Models Global models are used to address a number of types of questions (e.g., Alcamo, Leemans, and Kreileman, 1998): What is the outlook for global environment, or for some aspect of it? What might the costs be of achieving particular environmental goals? What is the relationship between long-term environmental outcomes and short-term action to reduce degradation? What is the relative importance of different linkages in the society-economy-environment system? What are the relative strengths of different feedbacks in this system? What are the most important sources of uncertainty in the system? What gaps in knowledge about the system exist, and how might new research fill these gaps? For example, in the climate change field, global integrated assessment

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Population, Land Use, and Environment: Research Directions models have played an influential role in both the science and policy communities (Toth et al., 2001). These models typically consist of linked subcomponents representing population; economic activity leading to demand for energy, agricultural products, and wood products; technological and other factors that determine how these products are supplied; emissions of radiatively active gases associated with this production; resulting change in atmospheric composition and climate; and impacts of climate change on ecosystems and society. Not all models include all of these components, and the level of detail with which any single component is represented varies widely, but this scheme represents the overarching framework in which all integrated assessment models of climate change either explicitly or implicitly operate. These models are the primary tools informing estimates of how much greenhouse gas emissions from human activity might increase, and therefore how much climate might change, in the absence of emissions reduction policies (Nakicenovic et al., 2000). They have also been used extensively to estimate the costs of various types of climate change policy (Weyant and Hill, 1999) and the potential damages from the impacts of future climate change (Ahmad et al., 2001). While global model analyses are not typically aimed at investigating land use per se, land use change can play an important role in analyses of climate change, biodiversity loss, and agricultural production. For example, land use and land use change are estimated to account for about 30 percent of total greenhouse gas emissions, primarily carbon dioxide (CO2) from deforestation, nitrous oxide (N2O) from the application of fertilizers, and methane (CH4) from livestock and rice production. Global model analyses have addressed such land use–related questions as: What is the potential for commercial biomass as a fuel source—especially if there are future constraints on carbon emissions—and how might it compete for land use with production of food and feed crops? What might future greenhouse gas emissions be from land use change, and how important might they be relative to emissions from energy production and use? What options for reducing greenhouse gas emissions from land use are there, and how large a role could they play in greenhouse gas reduction strategies? How much land might have to be devoted to agriculture in order to meet future demand? How might climate change affect future agricultural productivity, and which regions appear to be most vulnerable to these effects?

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Population, Land Use, and Environment: Research Directions Role of Population in Global Models of Land Use For this chapter we reviewed nine global models (Table 3-1) that simulate land use change to varying degrees: AgLU: Agriculture and Land Use AIM: Asian-Pacific Integrated Model ASF: Atmospheric Stabilization Framework BLS/AEZ: Basic Linked System/Agro-ecological Zones EPPA: Emissions Prediction and Policy Analysis FARM: Future Agricultural Resources Model IMAGE: Integrated Model to Assess the Greenhouse Effect IMPACT: International Model for Policy Analysis of Agricultural Commodities and Trade MARIA: Multi-regional Approach for Resource and Industry Allocation These models include all of those used to produce long-term scenarios of greenhouse gas emissions for the Intergovernmental Panel on Climate Change (IPCC; models are AgLU, AIM, ASF, IMAGE, MARIA), as well as those used to produce scenarios of changes in future ecosystem goods and services for the forthcoming Millennium Ecosystem Assessment (MA; models are AIM, IMAGE, and IMPACT). In addition, we reviewed three models (BLS/AEZ, FARM, IMPACT) that have been used extensively to explore scenarios of future agricultural demand and supply, and one prominent model (EPPA/IGSM) used in climate change analyses with a highly simplified land use component that is representative of a much larger number of models in which land use is not a primary focus. Table 3-1 lists these models along with key references, the modeling approach each one takes, and the level of regional disaggregation they employ. Population enters these models principally as a determinant of the demand for goods that may require land for production and as a factor of production. In these models land use change is typically driven by demand for agricultural products, commercial wood products, and biofuels (traditional or modern or both). Land is used to produce these products, and some of the models simulate the allocation of land to different uses and the consequences for the environment. The models vary in their approach to projecting demand and supply and in geographical and commodity detail. For example, the EPPA model (Babiker et al., 2001) is representative of a relatively simple approach to land use within integrated assessments (although it has a relatively detailed representation of the energy sector). It is a 12-region, 8-sector general equilibrium model in which crops, livestock, and forest products are represented by a single composite good. Production

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Population, Land Use, and Environment: Research Directions TABLE 3-1 Global Models Model Institution Key References Approacha Geographic Regions AgLU (Agriculture and Land Use) Joint Global Change Research Institute (JGCRI), Pacific Northwest National Laboratory, USA Sands and Leimbach, 2003 PE 11 AIM (Asian-Pacific Integrated Model) National Institute for Environment Studies (NIES), Japan Masui et al., 2001; Jiang et al., 2000 S 17 ASF (Atmospheric Stabilization Framework) U.S. Environmental Protection Agency Lashof and Tirpak, 1990 GEb 34 BLS/AEZ (Basic Linked System/ Agro-ecological Zones) International Institute for Applied Systems Analysis (IIASA), Austria Fischer et al., 1988, 2002b GE 34 EPPA (Emissions Prediction and Policy Analysis) Massachusetts Institute of Technology (MIT), USA Babiker et al., 2001 GE 12 FARM (Future Agricultural Resources Model) U.S. Department of Agriculture Darwin et al., 1995 GE 8 IMAGE (Integrated Model to Assess the Greenhouse Effect) National Institute of Public Health and the Environment (RIVM), the Netherlands Alcamo et al., 1998 Sb 17 IMPACT (International Model for Policy Analysis of Agricultural Commodities and Trade) International Food Policy Research Institute (IFPRI), USA Rosegrant et al., 2002b PE 36 MARIA (Multi-regional Approach for Resource and Industry Allocation) Science University of Tokyo, Japan Mori and Takahashi, 1998 S 8 aGE = general equilibrium; PE = partial equilibrium; S = simulation. bBased on the 1990 version of the Basic Linked System model; simulation approach is taken beyond 2050.

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Population, Land Use, and Environment: Research Directions of this good treats land as a fixed factor. The current version of the model is not aimed at producing realistic scenarios of land use change or shifts in use of land among various categories, but rather at reasonable scenarios of emissions resulting from the production of goods requiring land as an input. The relation between emissions and production of these goods is treated exogenously, capturing the potential effects of shifts in land use only implicitly. In contrast, the IMAGE model (Alcamo et al., 1998) has the most detailed simulation of spatially explicit land use. Production of agricultural and forest goods uses land based on assumptions about changes in the productivity of land over time. Spatially explicit land use is simulated using a global grid of 0.5 × 0.5 degree geographic cells. Grid cells are ranked according to suitability for a particular land use based on specified rule set, and demand is satisfied using the cells with highest suitability first. Thus IMAGE produces scenarios not only of the total amount of land devoted to alternative uses (and calculates the emissions associated with these uses), but also of where land is being put to each use. The three models that have been used primarily in agricultural studies (BLS/AEZ, FARM, and IMPACT) are, not surprisingly, the models with the greatest commodity detail in sectors that use land as an input. IMPACT, for example, is a 36-region partial equilibrium model with 16 agricultural commodities. FARM and BLS/AEZ include an explicit accounting of available land within each region, using spatially explicit biophysical information in calculating potential yields. FARM defines a baseline allocation of land to six different classes that implicitly captures variations in soil types and climate conditions, and climate change scenarios from general circulation models can be used to revise this allocation based on changed temperature and precipitation patterns. BLS/AEZ combines a spatially explicit biophysical model of potential productivity of global land resources with a 34-region, 10-sector general equilibrium model. The spatial component allows a more detailed and realistic accounting for available land, its potential productivity, and the effect of future climate change on productivity. While a variety of approaches are taken by these global models to land use change in general, the role of population is conceptually similar across all models. On the demand side (Table 3-2), population size acts as a simple scale factor for demand for food, wood products, and energy (which can be satisfied in part by traditional or modern biomass or both). Demand for these goods and services in turn drives land use. Demand is typically a function of population size, income, and prices (although see Table 3-2 for exceptions). Higher income leads to a shift in demand toward more animal products, a shift that is modeled either with commodity-specific income elasticities of demand, by exogenous scenario assumption, or both.

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Population, Land Use, and Environment: Research Directions On the supply side (Table 3-3), population enters through labor force as a factor of production. Only BLS/AEZ takes age structure into account in specifying labor force. Demographic factors generally do not affect the allocation of land across uses. Most of the models use a general or partial equilibrium framework, so that market-clearing prices allocate land among competing uses. IMAGE is a notable exception. Its rule set for spatially explicit land allocation uses population density as one factor among several others in decisions to convert uncultivated land in particular grid cells to agriculture. Population density itself is projected based on current spatially explicit population data sets (Center for International Earth Science Information Network, 2000) scaled up by projected future population growth. Global models use exogenous population projections, typically scenarios produced by the United Nations, IIASA, or the World Bank. A few global models incorporate their own cohort component population projection modules, enabling modelers to produce their own population projections if desired. Typically there are no explicit feedbacks onto demographic processes in these models; future fertility, mortality, and migration are specified exogenously. However, demographic assumptions are often chosen to be consistent (in the modeler’s judgment) with assumptions about other socioeconomic trends, such as economic growth rates. In addition to the direct effects of population on land use, population size can have an indirect effect on land use through price feedbacks. A larger population size will, all else being equal, raise the price for agricultural goods due to greater demand and the consequent need for increased production. These price changes may lead to a shift in demand for commodities with a lower land use intensity of production. The simple manner in which population affects land use in global models, and the high level of aggregation at which actors affect consumption or production, imply that the gulf between such modeling and research on population and land use at the subnational level is wide. Insights gained from case studies of small, subnational areas have typically emphasized the importance of context rather than possibilities for the kind of generalization of relationships to large spatial scales and longer time horizons necessary for use in global modeling. For example, lessons drawn from analysis of land use at the village level (Walsh et al., Chapter 6), or in the context of particular land reserves (Liu et al., Chapter 9) are difficult to scale up to the level of countries or larger world regions, which are used in global modeling, given their dependence on context. Some attempts have been made to draw more general conclusions by carrying out meta-analyses of the large number of case studies at the national or subnational level. While this work has led to insights and caveats about the complexity and context-dependence of the relationship between demography and land use change (Lambin et al., 2001), putting

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Population, Land Use, and Environment: Research Directions TABLE 3-2 Summary of Demand for Commodities Requiring Land Use in Global Models Model Total Commodities Agriculturea Forest Products Demand Methodology Crops Animal Products Industrial Wood Comm. Biomass Trad. Biomass AgLU (Agriculture and Land Use) 5 1 1 1 1 1 Calculate market clearing prices; demand a function of own price and income, scaled with popuation; agricultural products also include exogenous path of per capita calorie consumption AIM (Asian-Pacific Integrated Model) 6 2 2 1 1 N/A Exogenous demandb ASF (Atmospheric Stabilization Framework) 12 6 3 1 1 1 For agriculture, similar to Basic Linked System through 2050c BLS/AEZ (Basic Linked System/ Agro-ecological Zones) 9 6 3 N/A N/A N/A Maximize per capita utility based on prices, income; scale with population. Shift diet from staples to vegetable and livestock products according to calorie intake limitations EPPA (Emissions Prediction and Policy Analysis) 1 1 (composite agriculture and forest products) Maximize per capita utility based on prices, income; scale with population FARM (Future Agricultural Resources Model) 7 4 2 1 N/A 1d Maximize per capita utility based on prices, income; scale with population

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Population, Land Use, and Environment: Research Directions IMAGE (Integrated Model to Assess the Greenhouse Effect) 13 12 1 2e 1e 1e Maximize utility based on exogenous preferred consumption pattern for composite crops and composite animal products, land availability, and land requirements. Demand disagreggated to 13 commodities based on exogenous specifications IMPACT (International Model for Policy Analysis of Agricultural Commodities and Trade) 16 10 6 N/A N/A N/A Calculate market clearing prices; demand a function of prices and income, scaled with population MARIA (Multi-regional Approach for Resource and Industry Allocation) 6 1 3 N/A 2 N/A Calculate income scenario with multiregion GE model; demand a region-specific function of incomef aAgricultural commodities include sectors with indirect land use requirements, such as processed foods and dairy products. bDemand appears to be defined exogenously by scenarios, but documentation obtained to date is insufficient to confirm this characterization. cRegions representing the United States, China, the former Soviet Union, and India differ from the basic general equilibrium structure of other regions. The U.S. model contains an econometric supply model; in China, demand is specified exogenously, the former Soviet Union model reflects centrally planned policies rather than being based on balanced supply and demand; and the India model uses a different aggregation scheme. A simulation approach is taken for forest products. dFuelwood is part of a single composite forestry good. eDemand for industrial wood products is calculated separately based on income and available forest land; demand for commercial and traditional biomass is calculated separately in the IMAGE energy module. fRelationship between food consumption and income is bidirectional; thus scenarios that encounter food production constraints due to land scarcity also suffer losses in income.

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Population, Land Use, and Environment: Research Directions regions (Toth, Cao, and Hizsnyik, 2003). Such regionally disaggregated population projections are needed for estimating regional food demand and regional labor supply. They can also serve as background information for modeling development-induced migration, if migration processes are to be explicitly modeled. Province-Level Population Projections With a view to the above circumstances, our modeling strategy for producing province-level population projections entails the incorporation of relevant data from various sources, their harmonization to ensure consistency, and the preparation of detailed projections by using the maximum amount of information available about the relevant features of the Chinese population. The core building blocks of the projection model are the national-level projections of urban and rural populations by age groups prepared by Cao (2003) and the population distribution across provinces in rural and urban areas by age groups reported by the 2000 census (National Bureau of Statistics of the People’s Republic of China, 2002). Additional information sources include provincial projections of birth rates and death rates; projections of provincial urbanization rates; and the magnitude, direction, and age structure of interprovincial migration. Cao (2000, 2003) prepared a series of multistate population projections for China at the national level by distinguishing demographic patterns (fertility, mortality, migration) in the future according to education achievement and the place of residence (rural or urban) in addition to the usual male-female and age differentiation. Cao clustered her assumptions in a scenario matrix along two groups of attributes: fertility, mortality, educational achievements, and migration on one hand, and convergence of fertility levels in educational categories and in the urban and rural regions on the other. For the national total, the population projections start from a level of 1.275 billion in 2000. There is relatively little variation among projections of total population for 2010 (about 1.36 billion) and 2020 (1.41-1.43 billion), the range becoming somewhat wider thereafter; in year 2030 a projected range of 1.43-1.47 billion people is estimated. The range of scenarios in CHINAGRO is somewhat narrower than population levels projected by the United Nations. The IIASA projections used here are based on a carefully defined range of fertility assumptions: a total fertility rate in 2030 of between 1.42 and 1.64 in urban areas (total fertility rate estimated to be 1.58 in 2000) and between 1.85 and 2.11 for rural areas (estimated at 1.98 in 2000). It may be noted that the very low total fertility rates reported by the Chinese State Statistical Bureau in the statistical data of the 2000 census (total fertility rate of 1.27 in urban areas and 1.43 in rural areas)

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Population, Land Use, and Environment: Research Directions deviate from findings of Chinese and international demographers and after thorough discussion were not adopted in CHINAGRO as being unrealistically low (Toth et al., 2003). The modeling procedure for regional decomposition is based on the following assumptions: the future evolution of the population in China is properly depicted by Cao’s national projections, while the best source of the provincial distribution is the 2000 census. From this longitudinal (national population over time) and cross-sectional (provincial distribution in 2000) information, an appropriate decomposition procedure can be developed that provides the evolution of the provincial population over the next 30 to 50 years (Toth et al., 2003). The decomposition procedure can be enhanced and the precision of the results can be increased by drawing on information from supplementary models like statistics-based projections of regional birth rates, death rates, urbanization rates, and interprovincial migration. Urbanization The Chinese society has been going through various phases of fast urbanization and antiurbanization over the past half-century. For the CHINAGRO scenario development, Liu, Li, and Zhang (2003) present an in-depth analysis of the characteristics and trends of China’s urbanization. They conclude that the urbanization process in China has been heavily regulated and has always been under strict government control. The result of these tight policies is a relatively underurbanized Chinese society in comparison to other developing countries at a similar stage of socioeconomic development and also in comparison to the level of industrialization in China. An important component of the government policy has been rural urbanization adopted to limit rural-urban migration to cities. There are several important implications of the strong government influence on the urbanization process and on rural-urban migration in the past. First, it has suppressed at least part of the intended migration that would have taken place in the absence of government control. Second, due to the regulation, the complex permit scheme and the difficulties of obtaining permits to change place of residence (hukou) has resulted in illegal or tolerated migration, a large part of which remained unregistered. The combined implication is that statistical models trying to establish key historical patterns and relationships for use in projecting possible future trends may be somewhat misleading. Liu et al. (2003) conducted a thorough statistical analysis of the urbanization process at the national and the provincial level. They transformed the historical data series according to the 2000 definition and applied suitable assumptions about the shares of population with hukou (i.e., with

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Population, Land Use, and Environment: Research Directions registration) and urban immigrants without hukou (i.e., without registration). A series of market-oriented institutional reforms has been or is expected to be launched in China to actively promote the urbanization process, because the Chinese government has realized the serious adverse consequences on urbanization and economic growth resulting from the former urban-rural segmented institutional regulations and therefore defined active promotion of the urbanization process as one of the five strategic priorities of China’s economic development during the 10th Five Year Plan period. It includes (1) a reform of the hukou system, which will gradually relax the restrictions on farmers’ residences in cities and eventually establish a free rural-urban migration system; (2) a reform of the employment system. China’s urban and rural labor markets are still separated up to now, which means that rural laborers are still subjected to various discriminative constraints and restrictions; and (3) a reform of the rural land tenure and transfer system and improvement of the social security system in urban and rural areas. In general, land is state-owned in cities and towns and collective-owned in rural areas. Each farmer is usually allocated by contract the land use rights on a certain amount of collective-owned land in the village or has a share of benefits from leasing or selling collective-owned land. However, in the current situation, the farmer will automatically lose his rights on collective-owned land after he migrates and resides in a city or town. By law, his land use rights on his former collective-owned land are not transferable and cannot be sold. To make things worse, the employment in cities and towns is not stable and the social security system is still backward, particularly in small cities and towns. Thus, plenty of farmers, especially those in suburban or rapidly developing areas, do not want to become urban residents at the cost of losing their land use rights on collective-owned rural land. Liu et al. (2003) conclude that the rural land tenure system has to be reformed to increase its mobility and that the social security system needs to be improved to provide basic life security for both urban and rural residents. The process of urbanization is being considered as a mighty potential for economic development in the next phase. It is anticipated that urbanization will accelerate in the next decades, thus making significant contributions to economic growth. Starting from 36 percent urban population estimated by the 5th Census for 2000 and based on different assumptions on the prospects of China’s market-orientated institutional reforms, we project that China’s urbanization level will reach 42 to 45 percent in 2010, some 48 to 55 percent in 2020, and will fall in the range of 54 to 64 percent in 2030 (Liu et al., 2003). Having many more and wealthier consumers in urban conditions will have profound impacts on demand. Total direct food consumption of cere-

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Population, Land Use, and Environment: Research Directions als and other staple grains changes modestly in the CHINAGRO Baserun simulations, an increase of only 3 percent during 2003 to 2030. The explanation lies in two factors: first, the food consumption level in China is already high, and there is a low or even negative propensity to spend extra income on food grains, especially for urban consumers. Second, there are significant differences between rural and urban consumption patterns, with lower per capita consumption of cereals by urban residents compared with rural diets. As a consequence, there is a 10 percent decline in average per capita consumption of cereals, even though per capita incomes are much higher in 2030 than in the base year. While urbanization is slowing down cereal consumption, it is likely to accelerate increases in meat consumption. Urban diets include higher consumption of meat, and per capita meat consumption is responding strongly to income growth. An important change is a rise in meat consumption per capita, from 49 kg per capita in 2003 to 86 kg in 2030, which still falls a few kilograms below the present-day average of industrialized countries and almost 30 kg below the figures for United States. In the Baserun simulations, these factors combine to result in a doubling of total meat consumption between 2003 and 2030. Transport Flow Model For the analysis of an economy, specifically also for the agricultural sector and land use change, accounting for transportation is important because people have to commute between home and work, and goods have to be shipped from producers to markets to consumers. In the case of a small country, considering transport as a special type of service may be a reasonable abstraction. For a large country like China, however, the very difficult additional aspect comes to the fore that transport deeply affects price transmission and market structure, as the abstraction becomes untenable of all goods being traded on a single market with one transport cost irrespective of distance and weight and disregarding availability and quality of transportation infrastructure. It is clear that for the project, and more generally for the line of research that seeks to embed Chinese agriculture in an economy-wide setting, the challenge is to find a level of representation that on one hand is sufficiently detailed to reflect underlying densities of resources and farm characteristics, and on the other hand distinguishes sufficient market nodes, to provide a realistic picture of trade flows and price transmission mechanisms. The transport flow model builds on work of Ermoliev, Keyzer, and Norkin (2001) and Keyzer and Ermoliev (1999) on aggregation in a spatial continuum. The transport flow model is a partial equilibrium welfare model, which maximizes money metric consumer utility minus transport costs and is subject to site-specific commodity balances.

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Population, Land Use, and Environment: Research Directions It uses the formal infrastructure of highways, railways, and waterways (applying statistically recorded expenditures at the provincial level on transport in each category per ton-kilometer) and the less formal infrastructure of secondary roads and pathways, which provide the link between the farmer’s field and the main transport infrastructure. The combined cost of the formal and informal infrastructure is shown in Plate 1. To solve this problem, Keyzer (2003a) has formulated a new algorithm. Its basic idea is that of imposing a gravity ordering inspired by hydrological modeling. At given prices, optimal transport flows never run from a destination with a high price to one with a lower price, just as gravity ensures that water never flows to a higher location. This makes it possible to develop a computational scheme that runs recursively from one site to the next, solving the local welfare problem (for a given price ordering) and proceeding with adjusting the price ordering (i.e., the price landscape) to reflect marginal values of the commodity inflows. The algorithm is monotonically converging for any initial price ordering, improving from one price ordering to the next. Consequently, it ends after a finite number of orderings. To illustrate the operation of the algorithm, we present an application for rice, based on a population database and a production and consumption representation on a 10 by 10 km grid for some 94,000 markets, coupled through flow possibilities in 8 so-called union jack directions. Commodity flows can leave and enter the country at a limited number of seaports and inland border crossings. The introduction of these rest-of-the-world units makes it possible to deal with a model of the entire world, in which only China is represented in full detail and the other regions in a stylized way. Spatial data for this model were collected (most data sources were for 1997) for transport cost, and for production, consumption, and prices of rice and wheat. The combined maps of consumption, production, flows, and price are quite informative (Plate 2), highlighting a few well-known points. For rice consumption, which obviously is largest in regions with high population density, it appears that consumption is generally not located far from production. The production and the consumption map do not differ too much. Prices are lower in supply zones, especially in distant ones, and flows are largely domestic. International trade plays only a limited role, and the country is so large that local production is reasonably well protected against foreign imports, which can enter at seaports. Sequential Downscaling Methods for Spatial Estimation of Production Values and Flows The analysis of global change processes requires the development of methods that deal in a consistent manner with data on a multitude of spatial and temporal scales. Although GIS provides detailed geophysical informa-

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Population, Land Use, and Environment: Research Directions tion, the socioeconomic data often exist only at aggregate levels. Therefore, the adequate treatment and assessment of spatial heterogeneity calls for the development of appropriate downscaling procedures. In particular, this brings up a number of new estimation problems for recovering information on uncertain partially observable or even unobservable variables. For example, although we can estimate total departures or arrivals of passengers in transportation systems, the estimation of passenger flows between different locations requires expensive origin-destination surveys, and in many cases the data do not exist. Similar situations occur with projections of migration flows, estimation of flows in communication systems, and trade flows. The main idea of estimation in these new problems is to rely on using an appropriate optimization principle, such as cross-entropy maximization, subject to a variety of constraints connecting observable and unobservable dependent variables. The estimation of global processes consistent with local data challenges the traditional statistical estimation methods. These methods are based on the ability to obtain observations from unknown true probability distributions. In fact, the justification of these methods, such as their consistency and efficiency, rely on asymptotic analysis requiring an infinite number of observations. For the new estimation problems, referred to as downscaling problems, we often have only very restricted samples of real observations. Additional experiments to achieve more observations may be expensive, time-consuming, dangerous, or simply impossible. The aim is to develop sequential downscaling methods, which can be used in a variety of practical situations. A main motivation initially was the spatial estimation of agricultural production values. Agricultural production and land data are available at national scale from the Food and Agriculture Organization of the United Nations and other sources, but these data give no clue as to the spatial heterogeneity of agricultural production within country boundaries. A downscaling method in this case has to achieve plausible allocation of aggregate national production values to individual spatial units, for example, pixels, by using all available evidence from observed or inferred geospatial information, such as remotely sensed land cover, soil, climate and vegetation distribution, population density and distribution, etc. These methods are especially interesting and valuable for integrating in a consistent manner geographical data with statistical sources and to provide information and fill data gaps for spatially explicit modeling approaches, such as the transport flow model and the spatially explicit agricultural production relations used in the CHINAGRO project. As a theoretical underpinning, we establish connections between the fundamental maximum likelihood principle of statistical estimation theory and a maximum entropy principle used for the downscaling. We show that

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Population, Land Use, and Environment: Research Directions the maximum entropy principle can be viewed as an extension of maximum likelihood, the so-called minimax log likelihood principle. In this sense, the convergence of downscaling methods to solutions maximizing an entropy function can be considered as an analog of the asymptotic consistency analysis in traditional statistical estimation theory. CONCLUDING REMARKS Land use change plays a key role in global environmental change, as well as in the future availability of food and water. Modeling land use and land cover change presents new scientific challenges, particularly in integrating biophysical and socioeconomic data and processes and in capturing heterogeneity in both components. Global models take a range of approaches to modeling land use change, including general and partial equilibrium economic frameworks, as well as rule-based simulation modeling, and operate at a range of spatial scales, from only a few large world regions to models operating on a raster of several thousand grid cells. The treatment of demographics in these models is more uniform, generally limited to population size acting as a scale factor on demand for agricultural and forest products and as a measure of available labor force on the production side. Spatially explicit integrated assessment models of large world regions, such as the CHINAGRO project, are developing new methodologies for addressing the key challenges of integration and heterogeneity. For example, although GIS provides rich geophysical information, the socioeconomic data often exist only at aggregate levels. The estimation of aggregate processes consistent with spatially explicit data and, conversely, local implications of aggregate trends call for the development of appropriate aggregation and downscaling procedures, respectively. The approaches of sequential downscaling, and optimal aggregation, being developed and tested as part of the CHINAGRO project, represent promising possibilities for addressing these issues. Experiences in the China project also suggest two priorities for improving the treatment of demographic factors in integrated assessment models that can be informed by case study research on population-environment interactions. First, migration—in the case of China, internal rural-urban migration in particular—plays a key role in outcomes. A better understanding of the determinants of migration and its relationship to, and effect on, spatially heterogeneous socioeconomic conditions would strengthen this key model component. Second, experience suggests that decisions at the farm level in China are strongly affected by returns to labor and off-farm income-earning opportunities. This indicates that cross-sectoral linkages and a spatially explicit context should be considered when modeling production, consumption, and investment decisions of rural households.

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Population, Land Use, and Environment: Research Directions While spatially detailed modeling undoubtedly increases the burdens associated with data collection, checking and compilation, model estimation, and analysis, our experience has been that spatial richness facilitates and fosters interdisciplinary collaboration among diverse disciplines and can provide a mutually beneficial basis for linking case studies and regional analyses. ACKNOWLEDGMENTS The research of the IIASA-LUC project is a multidisciplinary and collaborative effort. It has involved researchers at IIASA and in various collaborating institutions in China, Europe, Japan, Russia, and United States. For the work presented in this chapter, the authors are particularly grateful to the researchers who have developed and significantly contributed to the various themes: Michiel A. Keyzer, Peter Albersen, and Wim C.M. van Veen (Free University, SOW-VU, Amsterdam, The Netherlands) designed and greatly contributed to implementing the welfare optimum and transportation models applied in the land use study of China, the CHINAGRO project on “Policy Decision Support for Sustainable Adaptation of China’s Agriculture to Globalization.” This research has been supported with funds of the European Union (INCO-DEV ICA-2000-20039), the Chinese and Dutch governments, and IIASA’s national member organizations. The authors are solely responsible for the results and conclusions and do not express in any way the opinion of the European Commission. The discussion of aggregation issues uses materials and relies on recent research findings of Michiel A. Keyzer, presented at a recent CHINAGRO training course in Beijing (September 2003). The downscaling methodologies discussed in the chapter have been developed jointly with Yuri Ermoliev, Tatiana Ermolieva, and Harrij van Velthuizen (researcher scholars at IIASA) and have benefited from discussions with colleagues at FAO (Ergin Ataman and Barbara Huddleston) and IFPRI (Stanley Wood and You Liang). REFERENCES Ahmad, Q.K., T.E. Downing, S. Nishioka, K.S. Parikh, C. Parmesan, S.H. Schneider, F. Toth, G. Yohe, A.U. Ahmed, P. Ayton, B.B. Fitzharris, J.E. Hay, R.N. Jones, G. Morgan, R. Moss, W. North, G. Petschel-Held, and R. Richels 2001 Methods and tools. In Climate Change 2001: Impacts, Adaptation, and Vulnerability, A Report of Working Group II of the Intergovernmental Panel on Climate Change. Cambridge, England: Cambridge University Press. Albersen, P.J., G. Fischer, M.A. Keyzer, and L. Sun 2002 Estimation of Agricultural Production Relations in the LUC Model for China. (IIASA Research Report RR-02-03.) Laxenberg, Austria: International Institute for Applied Systems Analysis.

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Population, Land Use, and Environment: Research Directions Alcamo, J., R. Leemans, and E. Kreileman, eds. 1998 Global Change Scenarios of the 21st Century: Results from the IMAGE 2.1 Model. Oxford, England: Elsevier Science Ltd. Babiker, M.H., J.M. Reilly, M. Mayer, R.S. Eckaus, I. Sue Wing, and R.C. Hyman 2001 The MIT Emissions Prediction and Policy Analysis (EPPA) Model: Revisions, Sensitivities, and Comparisons of Results. (Report Series No. 71.) Cambridge, MA: MIT Joint Program on the Science and Policy of Global Change. Cao, G.-Y. 2000 The Future Population of China: Prospects to 2045 by Place of Residence and by Level of Education. (Report No. IR-00-026.) Laxenburg, Austria: International Institute for Applied Systems Analysis. 2003 The Future Population of China: New Projections. Model Runs. Laxenburg, Austria: International Institute for Applied Systems Analysis. Center for International Earth Science Information Network (CIESIN), Columbia University; International Food Policy Research Institute (IFPRI); and World Resources Institute (WRI) 2000 Gridded Population of the World (GPW), Version 2. Palisades, NY: CIESIN, Columbia University. Available: http://sedac.ciesin.columbia.edu/plue/gpw [2/23/05]. Darwin, R., M. Tsigas, J. Lewandrowski, and A. Raneses 1995 World Agriculture and Climate Change: Economic Adaptations. (Natural Resources and Environment Division, Economic Research Service, Agricultural Economic Report No. 703.) Washington, DC: U.S. Department of Agriculture. Ermoliev, Y., M.A. Keyzer, and V. Norkin 2001 General Equilibrium and Welfare Modeling in Spatial Continuum: A Practical Framework for Land Use Planning. (Report No. WP-01-07.) Amsterdam, The Netherlands: Centre for World Food Studies. Fischer, G., K. Frohberg, M.A. Keyzer, and K.S. Parikh 1988 Linked National Models: A Tool for International Policy Analysis. Amsterdam, The Netherlands: Kluwer Academic Publishers. Fischer, G., M. Shah, and H. van Valthuizen 2002a Climate Change and Agricultural Vulnerability. (Special Report.) Laxenberg, Austria: International Institute for Applied Systems Analysis. Fischer, G., H. van Velthuizen, M. Shah, and F.O. Nachtergaele 2002b Global Agro-Ecological Assessment for Agriculture in the 21st Century: Methodology and Results. (Research Report RR-02-02.) Laxenburg, Austria: International Institute for Applied Systems Analysis. Geist, H.J., and E.F. Lambin 2002 Proximate causes and underlying driving forces of tropical deforestation. BioScience 52(2):143-150. Ginsburgh, V., and M.A. Keyzer 1997 The Structure of Applied General Equilibrium Models. Cambridge, MA: MIT Press. Jiang, K., T. Morita, T. Masui, and Y. Matsuoka 2000 Long-term GHG emission scenarios for Asia-Pacific and the world. Technological Forecasting and Social Change 63:207-229. Keyzer, M.A. 2003a Using Gravity Constraints to Optimize Transport Flows in a Spatially Explicit Equilibrium Model. (Working Paper.) Amsterdam, The Netherlands: Free University, SOW-VU. 2003b Theoretical Background on Aggregation: Micro-Macro Debates. Notes, Lecture 2. Second CHINAGRO Training Course, Policy Decision Support for Sustainable Adaptation of China’s Agriculture to Globalization, September 23-24, Beijing. Available: http://www.sow.econ.vu.nl/downloadables.htm.

OCR for page 51
Population, Land Use, and Environment: Research Directions 2004 Towards a Spatially and Socially Explicit Agricultural Policy Analysis for China: Specification of the CHINAGRO Models. (Working Paper.) Amsterdam, The Netherlands: Free University, SOW-VU. Keyzer, M.A., and Y. Ermoliev 1999 Modeling producer decisions in a spatial continuum. In Theory of Markets and Their Functioning, J. Herings, G. van der Laan, and A.J.J. Talman, eds. Amsterdam, The Netherlands: Elsevier Science. Keyzer, M.A., and W. van Veen 2004 A Summary Description of the CHINAGRO Welfare Model. (Working Paper.) Amsterdam, The Netherlands: Free University, SOW-VU. Lashof, D.A., and D. Tirpak, eds. 1990 Policy Options for Stabilizing Global Climate. Washington, DC: Hemisphere Publishing Corp. Lambin, E.F., B.L. Turner II, H. Geist, S. Agbola, A. Angelsen, J.W. Bruce, O. Coomes, R. Dirzo, G. Fischer, C. Folke, P.S. George, K. Homewood, J. Imbernon, R. Leemans, X. Li, E.F. Moran, M. Mortimore, P.S. Ramakrishnan, J.F. Richards, H. Skånes, W. Steffen, G.D. Stone, U. Svedin, T. Veldkamp, C. Vogel, and J. Xu 2001 The causes of land use and land cover change: Moving beyond the myths. Global Environmental Change 11:261-269. Liu, S., X. Li, and M. Zhang 2003 Scenario Analysis on Urbanization and Rural-Urban Migration in China. (IIASA Land Use Change Report IR-03-036.) Laxenburg, Austria: International Institute for Applied Systems Analysis. Masui, T., Y. Matsuoka, T. Morita, M. Kainuma, and K. Takahashi 2001 Development of land use model for IPCC new emission scenarios (SRES). Pp. 441-448 in Present and Future of Modeling Global Environmental Change: Toward Integrated Modeling, T. Matsuno and H. Kida, eds. Tokyo, Japan: Terrapub. McCarthy, J.J., O.F. Canziani, N.A. Leary, D.J. Dokken, and K.S. White, eds. 2001 Climate Change 2001: Impacts, Adaptation, and Vulnerability. (Intergovernmental Panel on Climate Change.) Cambridge, England: Cambridge University Press. Meadows, D.H., D.L. Meadows, J. Randers, and W.W. Behrens III 1972 The Limits to Growth. New York: Universe Books. Mori, S., and M. Takahashi 1998 An integrated assessment model for the new energy technologies and food production—and extension of the MARIA model. International Journal of Global Energy Issues 11:1-16. Nakicenovic, N., O. Davidson, G. Davis, A. Grübler, T. Kram, E. Lebre La Rovere, B. Metz, T. Morita, W. Pepper, H. Pitcher, A. Sankovski, P. Shukla, R. Swart, R. Watson, and Z. Dadi 2000 Emissions Scenarios. A Special Report of the Intergovernmental Panel on Climate Change. Cambridge, England: Cambridge University Press. National Bureau of Statistics of the People’s Republic of China 2002 China Census 2000, Detailed Data Tables. [CD-ROM.] Ann Arbor: Michigan State University and China Data Center. O’Neill, B.C. 2001 Cassandra/cornucopian debate. Pp. 1525-1529 in International Encyclopedia of the Social and Behavioral Sciences, vol. 3, N.J. Smelser and P.B. Baltes, eds. Oxford, England: Pergamon Press. Parson, E.A., and K. Fisher-Vanden 1997 Integrated assessment models of global climate change. Annual Review of Energy and the Environment 22:589-629.

OCR for page 51
Population, Land Use, and Environment: Research Directions Rosegrant, M.W., X. Cai, and S.A. Cline 2002a World Water and Food to 2025: Dealing with Scarcity. Washington, DC: International Food Policy Research Institute. Rosegrant, M.W., S. Meijer, and S.A. Cline 2002b International Model for Policy Analysis of Agricultural Commodities and Trade (IMPACT): Model Description. Washington, DC: International Food Policy Research Institute. Sands, R.D., and M. Leimbach 2003 Modeling agriculture and land use in an integrated assessment framework. Climatic Change 56:185-210. Toth, F., M. Mwandosya, C. Carraro, J. Christensen, J. Edmonds, B. Flannery, C. Gay-Garcia, H. Lee, K. M. Meyer-Abich, E. Nikitina, A. Rahman, R. Richels, Y. Ruqiu, A. Villavicencio, Y. Wake, J. Weyan, J. Byrne, R. Lempert, I. Meyer, and A. Underdal 2001 Decision-making frameworks. In Climate Change 2001: Mitigation, A Report of Working Group III of the Intergovernmental Panel on Climate Change. Cambridge, England: University Press. Toth, F.L., G.Y. Cao, and E. Hizsnyik 2003 Regional Population Projections for China. (IIASA Land Use Change Report, IR-03-042.) Laxenburg, Austria: International Institute for Applied Systems Analysis. United Nations Environment Programme 2002 GEO: Global Environmental Outlook 3. London, England: Earthscan. Weyant, J.P., and J. Hill 1999 Introduction and overview, costs of the Kyoto Protocol: A multimodel evaluation. Energy Journal (Kyoto Special Issue): 7-44.