Consumption-Driven Environmental Impact and Age Structure Change in OECD Countries: A Cointegration-STIRPAT Analysis

By Brantley Liddle. 2011

147 citations recorded by the source corpus.

This paper examines two environmental impacts for which population has a substantial demonstrated influence: transport carbon emissions and residential electricity consumption. It takes as its starting point the STIRPAT framework and disaggregates population into four key age groups: 20-34, 35-49, 50-69, and 70 and older. Population age structure’s influence was significant and varied across cohorts, and its profile was different for two dependent variables. For transport, young adults (20-34) were intensive, whereas the other cohorts had negative coefficients. For residential electricity consumption, age structure had a U-shaped impact: the youngest and oldest cohorts had positive coefficients, while the middle ones had negative coefficients.

Subfields: Economics and Econometrics, Health, Toxicology and Mutagenesis, Renewable Energy, Sustainability and the Environment

View Paper: https://www.semanticscholar.org/paper/c3c98e393da6fa20a05755ee67c4919cf475648c

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Citations in the corpus

Effect of natural resources extraction on energy consumption and carbon dioxide emission in Ghana (2020, background): “In his analysis of the effect of urbanization and affluence on carbon emission for 31 developed/OECD countries and 54 developing/non-OECD countries, Liddle (2011) reports that affluence, urbanization and share of electricity increase CO2 emission.”
Assessing the impacts of ageing and natural resource extraction on carbon emissions: A proposed policy framework for European economies (2021, background): “Liddle and Lung (2010) found people have a comparatively higher energy-intensive lifestyle in early adulthood and the “retirement age”, and have a relatively less energy-intensive lifestyle during the “middle age” (35–64).”
Assessing the impact of population, income and technology on energy consumption and industrial pollutant emissions in China (2015, methodology): “Studies using time-variant data have produced much greater variance in population elasticity estimations [12,28,30,31]; whereas cross-sectional analyses typically have estimated population elasticity near one [15].”
Impact of population, age structure, and urbanization on carbon emissions/energy consumption: evidence from macro-level, cross-country analyses (2013, unknown): “The methods used by Liddle (2011, 2013a) account for endogeneity among variables implicitly via error correction/cointegration modeling.”
What are the carbon emissions elasticities for income and population? Bridging STIRPAT and EKC via robust heterogeneous panel estimates (2015, background): “Indeed, Liddle (2011) demonstrated a substantial variation in individual STIRPAT elasticity estimations among OECD countries.”
Non-renewable and renewable energy consumption and CO2 emissions in OECD countries: A comparative analysis (2014, methodology): “Likewise, Liddle and Lung (2010) find a positive association between urbanisation and CO2 emissions from transport in OECD countries.”
Urban Density and Climate Change: A STIRPAT Analysis Using City-Level Data (2013, methodology): “Indeed, Liddle (5) The turning points had to be estimated since Romero-Lankao et al. (2009) do not report such statistics.”
Agriculture, Trade Openness and Emissions: An Empirical Analysis and Policy Options (2016, result): “Unlike the linear panel model results, the nonlinear results are in line with those provided by Fan et al. (2006), Poumanyvong and Kaneko (2010), Liddle (2011) and Shahiduzzaman and Alam (2014).”