The geography of urban greenhouse gas emissions in Asia: A regional analysis

Marcotullio, Peter J.; Sarzynski, Andrea; Albrecht, Jochen; Schulz, Niels

doi:10.1016/j.gloenvcha.2012.07.002

Cited by 61 publications

(34 citation statements)

References 50 publications

(66 reference statements)

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“…The coefficient of 0.21 indicates that emissions might increase by 0.21 % for every 1 % increase in our income proxy. Incidentally, this income effect for European cities is weaker than what was previously found within Asia (Marcotullio et al 2012), perhaps because the variation in income is narrower across Europe and at a higher level on average than across Asia. Further, we included mean-centered quadratic and cubic income terms to our base model to test for possible nonlinearities in the relationship between income and emissions as predicted by ecological modernization theory.…”

Section: Multiple Regression Resultscontrasting

confidence: 73%

A Top-Down Regional Assessment of Urban Greenhouse Gas Emissions in Europe

Marcotullio

Sarzynski

Albrecht

et al. 2013

AMBIO

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This paper provides an account of urban greenhouse gas (GHG) emissions from 40 countries in Europe and examines covariates of emissions levels. We use a ''top-down'' analysis of emissions as spatially reported in the Emission Dataset for Global Atmospheric Research supplemented by Carbon Monitoring for Action from 1153 European cities larger than 50 000 population in 2000 (comprising [81 % of the total European urban population). Urban areas are defined spatially and demographically by the Global Rural Urban Mapping Project. We compare these results with ''bottom-up'' carbon accounting method results for cities in the region. Our results suggest that direct (Scopes 1 and 2) GHG emissions from urban areas range between 44 and 54 % of total anthropogenic emissions for the region. While individual urban GHG footprints vary from bottom-up studies, both the mean differences and the regional energy-related GHG emission share support previous findings. Correlation analysis indicates that the urban GHG emissions in Europe are mainly influenced by population size, density, and income and not by biophysical conditions. We argue that these data and methods of analysis are best used at the regional or higher scales.

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Section: Multiple Regression Resultscontrasting

confidence: 73%

A Top-Down Regional Assessment of Urban Greenhouse Gas Emissions in Europe

Marcotullio

Sarzynski

Albrecht

et al. 2013

AMBIO

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“…Lafrance 1999), and (iii) lower levels of greenhouse gas emissions (e.g., Marcotullio et al 2012) by studies employing city-level data, few national-level studies have considered population density. Among those few studies, Hilton and Levinson (1998) found a significant, negative relationship between national population density and gasoline use in a study of 48 (developed and developing) countries.…”

Section: Population Densitymentioning

confidence: 99%

Impact of population, age structure, and urbanization on carbon emissions/energy consumption: evidence from macro-level, cross-country analyses

2013

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This review summarizes the evidence from cross-country, macro-level studies on the way demographic factors and processes-specifically, population, age structure, household size, urbanization, and population density-influence carbon emissions and energy consumption. Analyses employing time-variant data have produced great variance in population elasticity estimations-sometimes significantly greater than one, sometimes significantly less than one; whereas, cross-sectional analyses typically have estimated population elasticities near one. Studies that have considered age structure typically have used standard World Bank definitions, and mostly have found those variables to be insignificant. However, when researchers have considered levels of disaggregation that approximate life-cycle behavior like family size, they have uncovered relationships that are complex and nonlinear. Average household size has a negative relationship with road energy use and aggregate carbon emissions. Urbanization appears positively associated with energy consumption and carbon emissions. Higher population density is associated with lower levels of energy consumption and emissions.

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“…Specifically, since CO 2 emissions are not only driven by demographic factors, economic level and structure, local biophysical conditions such as temperature, elevation and urban form are also reported as important determinants [60][61][62]. In the future, with the emergence and development of big data science, it might be possible to obtain detailed spatial data of the aforementioned variables through the Internet, social media, commercial data companies, and so on, for enhancing the performance of NTL.…”

Section: Limitations and Potential Usesmentioning

confidence: 99%

An Improved Vegetation Adjusted Nighttime Light Urban Index and Its Application in Quantifying Spatiotemporal Dynamics of Carbon Emissions in China

2017

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Nighttime Light (NTL) has been widely used as a proxy of many socio-environmental issues. However, the limited range of sensor radiance of NTL prevents its further application and estimation accuracy. To improve the performance, we developed an improved Vegetation Adjusted Nighttime light Urban Index (VANUI) through fusing multi-year NTL with population density, the Normalized Difference Vegetation Index and water body data and applied it to fine-scaled carbon emission analysis in China. The results proved that our proposed index could reflect more spatial variation of human activities. It is also prominent in reducing the carbon modeling error at the inter-city level and distinguishing the emission heterogeneity at the intra-city level. Between 1995 and 2013, CO 2 emissions increased significantly in China, but were distributed unevenly in space with high density emissions mainly located in metropolitan areas and provincial capitals. In addition to Beijing-Tianjin-Hebei, the Yangzi River Delta and the Pearl River Delta, the Shandong Peninsula has become a new emission hotspot that needs special attention in carbon mitigation. The improved VANUI and its application to the carbon emission issue not only broadened our understanding of the spatiotemporal dynamics of fine-scaled CO 2 emission, but also provided implications for low-carbon and sustainable development plans.

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The geography of urban greenhouse gas emissions in Asia: A regional analysis

Cited by 61 publications

References 50 publications

A Top-Down Regional Assessment of Urban Greenhouse Gas Emissions in Europe

A Top-Down Regional Assessment of Urban Greenhouse Gas Emissions in Europe

Impact of population, age structure, and urbanization on carbon emissions/energy consumption: evidence from macro-level, cross-country analyses

An Improved Vegetation Adjusted Nighttime Light Urban Index and Its Application in Quantifying Spatiotemporal Dynamics of Carbon Emissions in China

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