Substantial changes in population size, age structure, and urbanization are expected in many parts of the world this century. Although such changes can affect energy use and greenhouse gas emissions, emissions scenario analyses have either left them out or treated them in a fragmentary or overly simplified manner. We carry out a comprehensive assessment of the implications of demographic change for global emissions of carbon dioxide. Using an energyeconomic growth model that accounts for a range of demographic dynamics, we show that slowing population growth could provide 16-29% of the emissions reductions suggested to be necessary by 2050 to avoid dangerous climate change. We also find that aging and urbanization can substantially influence emissions in particular world regions.climate change | energy | integrated assessment | population | households S tatistical analyses of historical data suggest that population growth has been one driver of emissions growth over the past several decades (1-3) and that urbanization (2), aging (3), and changes in household size (2) can also affect energy use and emissions. Demographers expect major changes in these dimensions of populations over the coming decades (4). Global population could grow by more than 3 billion by mid-century, with most of that difference accounted for by growing urban populations. Aging will occur in most regions, a result of declines in both fertility and mortality, and is expected to be particularly rapid in regions like China that have recently experienced sharp falls in fertility. The number of people per household is also declining as populations age and living arrangements shift away from multigeneration households toward nuclear families.Despite these expectations, explicit analysis of the effect of demographic change on future emissions has been extremely limited (5). Early exploratory analyses considered only population size or total numbers of households (6, 7) and used simple multiplicative models (8) that did not account for important relationships between population and economic and technological factors. Furthermore, these early models used little or no regional disaggregation, an important consideration given that, with some exceptions including the United States, population growth tends to be highest where per capita emissions are lowest.More recently, a large emissions scenario literature (9) has developed that informs a wide range of climate change analysis and related policy discussions. Model sophistication and scope has increased substantially over time. Scenarios typically span timescales of decades to centuries, include emissions of multiple gases and aerosols from a range of sectors, including land use, and consider a wide range of emissions drivers (10-12). They have been used to study possible emissions in the absence of mitigation policy as well as the costs and other consequences of emissions reduction strategies. Although nearly all scenarios include assumptions about future population growth, none has explicitly investig...

Globally, tropical deforestation releases 20 to 30% of anthropogenic greenhouse gases. Conserving forests could reduce emissions, but the cost-effectiveness of this mechanism for mitigation depends on the associated opportunity costs. We estimated these costs from local, national, and global perspectives using a case study from Madagascar. Conservation generated significant benefits over logging and agriculture locally and globally. Nationally, however, financial benefits from industrial logging were larger than conservation benefits. Such differing economic signals across scales may exacerbate tropical deforestation. The Kyoto Protocol could potentially overcome this obstacle to conservation by creating markets for protection of tropical forests to mitigate climate change.

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a b s t r a c tLong-term modeling of agricultural land use is central in global scale assessments of climate change, food security, biodiversity, and climate adaptation and mitigation policies. We present a global-scale dynamic land use allocation model and show that it can reproduce the broad spatial features of the past 100 years of evolution of cropland and pastureland patterns. The modeling approach integrates economic theory, observed land use history, and data on both socioeconomic and biophysical determinants of land use change, and estimates relationships using long-term historical data, thereby making it suitable for long-term projections. The underlying economic motivation is maximization of expected profits by hypothesized landowners within each grid cell. The model predicts fractional land use for cropland and pastureland within each grid cell based on socioeconomic and biophysical driving factors that change with time. The model explicitly incorporates the following key features: (1) land use competition, (2) spatial heterogeneity in the nature of driving factors across geographic regions, (3) spatial heterogeneity in the relative importance of driving factors and previous land use patterns in determining land use allocation, and (4) spatial and temporal autocorrelation in land use patterns.We show that land use allocation approaches based solely on previous land use history (but disregarding the impact of driving factors), or those accounting for both land use history and driving factors by mechanistically fitting models for the spatial processes of land use change do not reproduce well long-term historical land use patterns. With an example application to the terrestrial carbon cycle, we show that such inaccuracies in land use allocation can translate into significant implications for global environmental assessments. The modeling approach and its evaluation provide an example that can be useful to the land use, Integrated Assessment, and the Earth system modeling communities.