Abstract
As consumption patterns differ between households depending on the age and number of household members, demographic change alters the structure of overall consumption expenditure. This chapter presents an extended input–output model, which was used to study the impact of demographically induced changes in the structure of consumption expenditure on infrastructure use. The analysis is performed for the cases of Germany, Hamburg, and Mecklenburg-Western Pomerania. Selected sustainability indicators (energy use and emissions of CO2 and NOX) are quantified to study the implications for sustainable development. The results show that although the shift in consumption patterns tends to reduce energy use and emissions, it does not achieve the required decoupling of GDP growth and emissions.
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- 1.
Strictly speaking, Eq. (4.1) reflects only those emissions that are linked to consumption. If C/Y falls, the share of other GDP components would increase by definition. Therefore, reducing C/Y is not a sensible emission reduction strategy.
- 2.
In the general case, there are n variants of consumption goods. The consumption structure can then be described by a vector of length n, in which each element represents the share of a certain consumption good in total consumption. For the simplified representation in Eq. (4.1), it was assumed that only two different goods exist. In this case, the vector describing the consumption structure contains only the elements (A/C) and (1–(A/C)). Thus, the entire consumption structure can be described by the value of (A/C), which allows for a simplified notation. Without this assumption, the mathematical presentation would have to be written in matrix form, causing problems for readers who are not familiar with matrices.
- 3.
A necessary good is defined as a good whose income elasticity is between zero and one, and a luxury good is defined as a good whose income elasticity is larger than one (Lewis, 2008, p. 230)
- 4.
Cohort effects therefore receive more attention in Chapter 9.
- 5.
This assumption can be interpreted as a successful attempt by the government to adjust all measures affecting the intergenerational distribution of income (e.g. tuition fees, pensions etc.) in such a way that, in sum, they do not alter that distribution.
- 6.
Consumption statistics record expenditure according to the COICOP classification (the EVS uses SEA, the German implementation of COICOP) and are valued at consumer prices. The German input–output tables, by contrast, follow the CPA classification and are valued at purchaser prices. Hence, the consumption expenditure had to be converted accordingly. For more details, see Kronenberg (2009b).
- 7.
At the two-digit level, COICOP reaches a disaggregation of 12 categories. Figure 7.2 uses that disaggregation with one exception by mentioning electricity, gas and other fuels as an individual category (it is normally part of the housing … category).
- 8.
The remanence effect receives more attention in Chapter 10.
- 9.
Alcoholic beverages and tobacco are necessary goods according to the economists’ definition.
References
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Appendix
Appendix
In regional economics, it is often necessary to construct an input–output table for the regional economy by means of a nonsurvey method, because the limited availability of time and money makes the construction of a survey-based table impossible. Traditional nonsurvey methods, however, generate biased results because they underestimate the importance of interregional trade. This problem can be solved by using the more advanced techniques developed by Flegg, Webber, & Elliott (1995) and Flegg and Webber (2000). However, these techniques require the modeller to select a certain value for a key parameter. Although recent work (Bonfiglio, 2009) has provided a range of “good” parameter values, the choice of this value remains to a large extent arbitrary. Therefore, an alternative approach was selected.
The CHARM approach (Kronenberg, 2009a) follows the recommendation by Richardson (1985) “to develop improved nonsurvey adjustments that correct for the effects of cross-hauling”. It is based on the observation that cross-hauling is more common when products are highly heterogeneous. For practical purposes, it is assumed that for each product i the amount of cross-hauling q i depends on the product’s degree of heterogeneity h i , regional production x i , regional intermediate use z i and regional final use d i in the following fashion:
At the national level, the quantities q i , x i , z i , and d i can be calculated from the national input–output table. This means that h i , a theoretical construct indicating the degree of product heterogeneity, can be calculated from the national data. At the regional level, the quantities x i , z i , and d i can be estimated using the traditional nonsurvey methods (or, with some luck, they could be obtained from official statistics). Assuming that the value of h i is the same in all regions, Eq. (4.1) can then be used to calculate the regional q i . Thus, the CHARM method provides an estimate of regional cross-hauling. Incorporating this into the regional input–output table leads to more realistic estimates of regional trade and output multipliers than the traditional nonsurvey methods.
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Kronenberg, T. (2011). Demographically Induced Changes in the Structure of Final Demand and Infrastructure Use. In: Kronenberg, T., Kuckshinrichs, W. (eds) Demography and Infrastructure. Environment & Policy, vol 51. Springer, Dordrecht. https://doi.org/10.1007/978-94-007-0458-9_4
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