The impact of urbanization on carbon emissions in developing countries: a Chinese study based on the U-Kaya method

By Yuzhe Wu, Jiahui Shen, Xiaoling Zhang, M. Skitmore, Weisheng Lu. 2016

217 citations recorded by the source corpus.

Urbanization results in a considerable economic disparity between urban and rural areas in developing countries, which has had a consequent significant impact on CO2 emissions. To accommodate this directly, this paper presents a modified version of the Kaya Identity formula, U-Kaya, that includes a direct urbanization factor to determine the relationship between energy consumption intensity, population growth, urbanization rates, urban and rural GDP per capita and the volume of carbon emissions, for application in the dynamic forecasting of carbon emissions by Monte Carlo simulation. The method is demonstrated in forecasting China’s likely carbon emissions in 2020 by three different modes of urbanization policy: the government-dominant mode, market-dominant mode and a hybrid government market-dominant mode. The results indicate that a higher urbanization rate, energy carbon emission coefficient and energy intensity will lead to increased carbon emissions. Finally, three policy implications are identified, comprising the narrowing of the economic gap between urban and rural areas, adjustments to the energy structure and technical innovation, which will provide a valuable reference for developing countries in their efforts to reduce carbon emissions.

Subfields: Economics and Econometrics, Environmental Engineering, Health, Toxicology and Mutagenesis

View Paper: https://www.semanticscholar.org/paper/97157a31b3427588a302e659c0f3aab0678fbe11

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Economics and Econometrics

1. Ya Wu
2. Christian Brand
3. P. Zhao
4. Liyin Shen
5. Peilin Li
6. C. Shuai

In Environmental Engineering

1. C. Shuai
2. Ya Wu
3. Liyin Shen
4. Christian Brand
5. P. Zhao
6. Peilin Li

In Health, Toxicology and Mutagenesis

1. Christian Brand
2. P. Zhao
3. Peilin Li

Citations in the corpus

• Identifying the key impact factors of carbon emission in China: Results from a largely expanded pool of potential impact factors (2018, methodology): “Employing STIRPATmodel with panel data analysis, the research by Poumanyvong and Kaneko (2010) identified the critical KIFs of carbon emission in 99 countries from eight nominated factors.”
• Future energy use and CO2 emissions of urban passenger transport in China: A travel behavior and urban form based approach (2018, background): “Although in recent decades there has been discussion on the saturation of car use in developed countries and some found a decoupled pattern with economy [46-48], it might still be reasonable to expect continuous PV ownership and usage growth in Chinese cities before reaching equivalent levels of urb”