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This article provides a methodological contribution to the study of the effect of changes in population age structure on carbon dioxide (CO2) emissions. First, I propose a generalization of the IPAT equation to a multisector economy with an age-structured population and discuss the insights that can be obtained in the context of stable population theory. Second, I suggest a statistical model of household consumption as a function of household size and age structure to quantitatively evaluate the extent of economies of scale in consumption of energy-intensive goods, and to estimate age-specific profiles of consumption of energy-intensive goods and of CO2 emissions. Third, I offer an illustration of the methodologies using data for the United States. The analysis shows that per-capita CO2 emissions increase with age until the individual is in his or her 60s, and then emissions tend to decrease. Holding everything else constant, the expected change in U.S. population age distribution during the next four decades is likely to have a small, but noticeable, positive impact on CO2 emissions.
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"[...] the US Department of Transportation has been making the virtually identical vehicle travel forecasts for well over a decade. All of those forecasts project rapid and incessant growth in vehicle travel for as far as the eye can see. Meanwhile, actual traffic volumes have flattened out, and may actually be falling."
"That overestimate is the equivalent of adding travel from five average-sized states to the total. And the overestimate came in the year of the release, not year 20. This is troubling in a report that is widely regarded as a gauge of the “need” for funding new highway capacity."
|What the British say||What Foreigners understand||What the British mean|
|With the greatest respect||He is listening to me||You are an idiot|
|That's not bad||That's poor||That's good|
|Quite good||Quite good||A bit disappointing|
|I would suggest||Think about the idea, but do what you like||Do it or be prepared to justify yourself|
|Very interesting||They are impressed||That is clearly nonsense|
|I almost agree||He's not far from agreement||I don't agree at all|
|I only have a few minor comments||He has found a few typos||Please rewrite completely|
In regard to the development and reform of higher education (HE), recent and projected evidence suggest that enrollment growth is likely to be slower than it is at present (or even negative) as a result of ageing populations. The case of the BRIC countries is particularly interesting for the study of the impact of demographic changes on HE because these countries show considerable diversity regarding their demographic transition. This paper explores how demographic changes are likely to affect the demand for higher education in BRIC countries. I argue that these countries are now facing a great expansion of enrollment but, given declining fertility levels, diversification of the HE clientele will become a common strategy. But diversification of the student population will place a new and complex set of demands on HE institutions, and equity in higher education in the near future will depend on how HE systems are structured in these countries.
Large-scale networks of human interaction, in particular country-wide telephone call networks, can be used to redraw geographical maps by applying algorithms of topological community detection. The geographic projections of the emerging areas in a few recent studies on single regions have been suggested to share two distinct properties: first, they are cohesive, and second, they tend to closely follow socio-economic boundaries and are similar to existing political regions in size and number. Here we use an extended set of countries and clustering indices to quantify overlaps, providing ample additional evidence for these observations using phone data from countries of various scales across Europe, Asia, and Africa: France, the UK, Italy, Belgium, Portugal, Saudi Arabia, and Ivory Coast. In our analysis we use the known approach of partitioning country-wide networks, and an additional iterative partitioning of each of the first level communities into sub-communities, revealing that cohesiveness and matching of official regions can also be observed on a second level if spatial resolution of the data is high enough. The method has possible policy implications on the definition of the borderlines and sizes of administrative regions.