Consumption-Driven Environmental Impact and Age Structure Change in OECD Countries

Liddle, Brantley

doi:10.4054/demres.2011.24.30

Cited by 144 publications

(110 citation statements)

References 28 publications

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“…This result demonstrates that in the long run, the total population size contributes more to increased CO 2 emissions than economic growth in developed countries. This finding is consistent with those of Fan et al (2006), Poumanyvong and Kaneko (2010) and Liddle (2011), who obtain the same results for developed countries. Liddle (2011) observes that environmental impact is more sensitive to changes in population growth than to changes in economic growth.…”

Section: Estimation Of the Long-run Elasticities Of Co 2 Emissionssupporting

confidence: 82%

“…This finding is consistent with those of Fan et al (2006), Poumanyvong and Kaneko (2010) and Liddle (2011), who obtain the same results for developed countries. Liddle (2011) observes that environmental impact is more sensitive to changes in population growth than to changes in economic growth. This greater sensitivity occurs because population growth through the acceleration of energy consumption speeds up pollutant emissions.…”

Section: Estimation Of the Long-run Elasticities Of Co 2 Emissionssupporting

confidence: 82%

“…However, urbanisation has a positive and significant impact on only CO 2 emissions from transport. When improving this study by performing unit root and cointegration tests, Liddle (2011) finds positive associations between GDP per capita and CO 2 emissions from transport and between the total population and CO 2 from transport. Using a panel of 29 provinces in China from 1995 to 2010, Zhang and Lin (2012) show that population, affluence, industrialisation and energy intensity increase CO 2 emissions for the whole sample, whereas the results are different across the different regions.…”

Section: Review Of Empirical Work Based On the Stirpat Modelmentioning

confidence: 99%

See 2 more Smart Citations

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

2014

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Section: Estimation Of the Long-run Elasticities Of Co 2 Emissionssupporting

confidence: 82%

Section: Estimation Of the Long-run Elasticities Of Co 2 Emissionssupporting

confidence: 82%

Section: Review Of Empirical Work Based On the Stirpat Modelmentioning

confidence: 99%

See 1 more Smart Citation

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

2014

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“…For example, Liddle (2011) divided the population into five age groups (< 20, 20-34, 35-49, 50-69, and 70 ≥) whereas Zhu and Peng (2012) integrated the working population into one group. Further, Cao and Yang (2017) classified the age of 44 as an important node (16-24, 25-34, 35-44, 45 >).…”

Section: Data and Modelsmentioning

confidence: 99%

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

Liu

Huang

Kaloudis

et al. 2017

Transportation Research Part D: Transport and Environment

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The relationship between urbanization, energy use, and CO2 emissions has been extensively studied in recent years, however little attention paid to the differences in urban forms. Previous studies implicitly assume that the urban form is homogenous across different urban areas. Such an assumption is questionable as urban form can have many different facets. This paper investigates the effects of urbanization on the road transport energy use by considering different urban forms from a dataset of (3) there is a non-linear (quadratic) relationship between road energy use per capita and urban population. This implies that an increase in total municipality population over a specific turning point can result in a decrease in road energy use per capita. However, (4) the ratio of urban residential buildings with private gardens has a negative and significant influence on road transport energy use.This implies that there may be a trade-off between compact and sprawl city development strategies, highlighting that sustainable energy use requires further investigation.

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“…Liddle [5] found that the effect of population age structure on carbon emission from transport energy consumption presented a change tendency as an inverted "U" type, while Okada [6], and Menz et al [7] believed that aging populations would reduce the overall carbon emission from energy consumption. Zhu et al [8], Knight et al [9], and many other scholars found that the level of urbanization significantly affected overall carbon emission.…”

Section: Introductionmentioning

confidence: 99%

Effect of Population Structure Change on Carbon Emission in China

2016

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This paper expanded the Logarithmic Mean Divisia Index (LMDI) model through the introduction of urbanization, residents' consumption, and other factors, and decomposed carbon emission changes in China into carbon emission factor effect, energy intensity effect, consumption inhibitory factor effect, urbanization effect, residents' consumption effect, and population scale effect, and then explored contribution rates and action mechanisms of the above six factors on change in carbon emissions in China. Then, the effect of population structure change on carbon emission was analyzed by taking 2003-2012 as a sample period, and combining this with the panel data of 30 provinces in China. Results showed that in 2003-2012, total carbon emission increased by 4.2117 billion tons in China. The consumption inhibitory factor effect, urbanization effect, residents' consumption effect, and population scale effect promoted the increase in carbon emissions, and their contribution ratios were 27.44%, 12.700%, 74.96%, and 5.90%, respectively. However, the influence of carbon emission factor effect (´2.54%) and energy intensity effect (´18.46%) on carbon emissions were negative. Population urbanization has become the main population factor which affects carbon emission in China. The "Eastern aggregation" phenomenon caused the population scale effect in the eastern area to be significantly higher than in the central and western regions, but the contribution rate of its energy intensity effect (´11.10 million tons) was significantly smaller than in the central (´21.61 million tons) and western regions (´13.29 million tons), and the carbon emission factor effect in the central area (´3.33 million tons) was significantly higher than that in the eastern (´2.00 million tons) and western regions (´1.08 million tons). During the sample period, the change in population age structure, population education structure, and population occupation structure relieved growth of carbon emissions in China, but the effects of change of population, urban and rural structure, regional economic level, and population size generated increases in carbon emissions. Finally, the change of population sex structure had no significant influence on changes in carbon emissions.

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Consumption-Driven Environmental Impact and Age Structure Change in OECD Countries

Cited by 144 publications

References 28 publications

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

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

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

Effect of Population Structure Change on Carbon Emission in China

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