![]() Results show that the effects of changes in population composition can have a significant influence on emissions in particular regions, separate from the effect of changes in population size. To reproduce key aspects of the A2 and B2 scenarios, we tune parameters that govern the effects of technical change on the productivity of labor and energy inputs in the PET model ( SI Text has details of the tuning procedure) such that regional per capita emissions, gross domestic product (GDP), and primary energy use by source match values produced by the implementation of these scenarios in the IIASA Model for Energy Supply Strategy Alternatives and their General Environmental Impact (MESSAGE) model ( 20). Using two baselines allows us to explore the effect of uncertainty in future socioeconomic conditions on our results. However, as discussed above, we do not use the population projections originally used by SRES, but rather, we substitute our own projections based on more recent projections from the UN. As in the original SRES scenarios, we assume that population growth is high in the A2 scenario and medium in the B2 scenario. To test the effects of alternative demographic futures on emissions, we begin with two different baseline scenarios of future emissions that account only for the effect of population size changes, patterned after the A2 and B2 scenarios from the Special Report on Emissions Scenarios (SRES) of the Intergovernmental Panel on Climate Change (IPCC) ( 9). In all regions, future population is further allocated into various types of households by rural/urban residence, size, and age of the household head based on projections that we carried out with an extended headship-rate household projection model ( SI Text). To account for the impact of changes in rural/urban population age structures in key countries, we supplement these projections (which do not include separate urban and rural age structures) with our own projections for China and India, using a multistate population projection model with input assumptions based on the UN population and urbanization scenarios. ![]() To project these demographic trends, we use the high, medium, and low scenarios of the United Nations (UN) 2003 Long-Range World Population Projections ( 18) combined with the UN 2007 Urbanization Prospects extended by the International Institute for Applied Systems Analysis (IIASA) ( 19) and derive population by age, sex, and rural/urban residence for the period of 2000–2100. To represent indirect effects on emissions through economic growth, the PET model explicitly accounts for the effect of ( i) population growth rates on economic growth rates ( 14), ( ii) age structure changes on labor supply ( 15), ( iii) urbanization on labor productivity ( 16), and ( iv) anticipated demographic change (and its economic effects) on savings and consumption behavior ( 17). ![]() Because different goods have different energy intensities of production, these shifts can lead to changes in emissions rates. The direct effect on emissions is represented by disaggregating household consumption for each household type into four categories of goods (energy, food, transport, and other) so that shifts in the composition of the population by household type produce shifts in the aggregate mix of goods demanded. In the PET model, households can affect emissions either directly through their consumption patterns or indirectly through their effects on economic growth in ways that up until now have not been explicitly accounted for in emissions models. To test the effect of demographic change, we develop a set of global household projections and use these to drive the PET model, computing the associated effects on emissions outcomes. We use these estimates to calibrate parameters in the PET model that represent household demand for consumer goods, wealth in the base year, and labor supply over time. We draw on data from national surveys covering 34 countries and representative of 61% of the global population to estimate key economic characteristics of our household types. To best capture the effects of future demographic change, we take an approach based on building principles from demography into a dynamic economic model by distinguishing among a large number of household types by household age (defined as age of the household head), size (number of members), and urban/rural residence in each region. The PET model is a nine-region dynamic computable general equilibrium model of the global economy with a basic economic structure that is representative of the state of the art in emissions scenario modeling ( SI Text has further description and references).
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |