Age-structure, urbanization, and climate change in developed countries: revisiting STIRPAT for disaggregated population and consumption-related environmental impacts

Abstract

We focus on three environmental impacts particularly influenced by population agestructure-carbon emissions from transport and residential energy and electricity consumption-as well as aggregate carbon emissions for a panel of developed countries, and take as our starting point the STIRPAT framework. Among our contributions is to further disaggregate population into three particularly key age groups: 20-34, 35-49, and 50-64, and by doing so demonstrate that population's environmental impact differs considerably across age-groups, with the older age-groups (ones typically associated with larger households) actually exerting a negative influence. Furthermore, those age-specific population influences are different (in absolute and relative terms) for the different environmental impacts we analyze. Also, we find that urbanization, in developed countries, best measures access to a country's power grid, and thus, is positively associated with energy consumption in the residential sector. Lastly, we suggest some modelling and methodological improvements to the STIRPAT framework.

Non-renewable and renewable energy consumption and CO2 emissions in OECD countries: A comparative analysis

“…This result is consistent with the results of Poumanyvong and Kaneko (2010) for high-income countries and of Zhang and Lin (2012) for China. Likewise, Liddle and Lung (2010) find a positive association between urbanisation and CO 2 emissions from transport in OECD countries. These authors state that this is a surprising result because it is expected that greater urbanisation leads to more public transport use and thus to lower emissions.…”

“…In contrast, a negative relationship between urbanisation and CO 2 emissions is found by Fan et al (2006) Similar to the study of Fan et al (2006), a study by Poumanyvong and Kaneko (2010) considers different development stages and provides evidence of positive effects of population, affluence and urbanisation on CO 2 emissions for all income groups, low, middle and high. Considering aggregate CO 2 emissions and CO 2 emissions from transport for 17 developed countries covering the period from 1960 to 2005, Liddle andLung (2010) reveal that the total population and economic growth positively influence these two types of emissions. However, urbanisation has a positive and significant impact on only CO 2 emissions from transport.…”

Untitled

“…Second, urbanization tends to increase demand for houses which also increases demand for housing materials such as cement which is an important source of CO 2 emissions. Thirdly, the increase in the demand for houses require the clearing of trees and grasslands conversion which releases the carbon stored in the trees Poumanyvong and Kaneko (2010) is also confirmed by a number of studies (Cole and Neumayer, 2004;Liddle and Lung, 2010).…”

Does urbanization lead to less energy use on road transport? Evidence from municipalities in Norway

“…Researchers applying the STIRPAT frame to carbon emissions or energy use typically include data on population, income, urbanization level, urban density, and age compositions in their analyses, summarized in the appendix A1 (Boyko and Cooper, 2011;Fan et al, 2006;Hossain, 2011;Liddle, 2004;Liddle and Lung, 2010;Martínez-Zarzoso et al, 2007;Martínez-Zarzoso and Maruotti, 2011;Menz and Welsch, 2012;Norman et al, 2006;Perkins et al, 2009;Poumanyvong and Kaneko, 2010;Poumanyvonga et al, 2012;Yang et al, 2015;Zhu and Peng, 2012). The common feature in these studies is the lack of information on the urban form which may be ascribed to the deficiency of appropriate measures of urban area level spatial structure (Lee and Lee, 2014) as well as the limited variables in the STIRPAT framework.…”

“…As the time-series dimension of four periods is much smaller than the cross-sectional dimension of 386 municipalities, it seems reasonable to assume that the non-stationarity issue is not of concern (see e.g., Menz and Welsh, 2012;Liddle and Lung, 2010 show large differences. The Hausman test (P value is 0.0000) demonstrates that the FE estimator is preferred.…”