Estimation, characteristics, and determinants of energy-related industrial CO2 emissions in Shanghai (China), 1994–2009

Shao, Shuai; Yang, Lili; Yu, Ming-bo; Yu, Miao

doi:10.1016/j.enpol.2011.07.049

Cited by 225 publications

(101 citation statements)

References 42 publications

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“…When estimating CO 2 emissions from industrial processes, methodologies recommended by the IPCC are widely used [33][34][35][36][37][38]. In the Guidelines for National Greenhouse Gas Inventories 2006 [23], three basic methodologies are proposed to estimate process emissions from the lime industry: an output-based approach that uses default values (Method 1); an output-based approach that estimates emissions from CaO production and employs country-specific information for correction factors (Method 2); and an input-based carbonate approach (Method 3).…”

Section: Process Emissionsmentioning

confidence: 99%

CO2 emissions from China’s lime industry

Liu²,

2016

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Section: Process Emissionsmentioning

confidence: 99%

CO2 emissions from China’s lime industry

Liu²,

2016

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“…Studies about Shanghai, such as Shao et al (2011), Chen et al (2013), Zhao and Tan (2010), Li et al (2011), Liu et al (2012), Steffen (2013) and so on, also considered a relatively small number (four or fewer) of types of energy. This decreases the accuracy of accounting, and only creates a coarse resolution.…”

Section: Introductionmentioning

confidence: 98%

Energy-Related Carbon Emissions in Shanghai: Driving Forces and Reducing Strategies

Chun-zeng

Jiang

2015

Low-Carbon City and New-Type Urbanization

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This paper calculated the energy-related carbon emissions from production, household and energy transformation sectors in Shanghai and decomposed the effects of their changes in carbon emissions resulting from 11 causal factors of reflecting the changes in socioeconomic activity, intensity of energy and the structure by logarithmic mean divisia index. The results show that the changes of economic activity (EA), population size (PS), total energy consumption in transformation and energy consumption per capita (ECPC) increase CO 2 emissions obviously. The changes of energy intensity (EI), urban and rural population distribution structure, energy mix of household and mix of energy in transformation drive the decrease of CO 2 emissions. The changes of economic structure (ES), energy mix of production, and energy transformation structure (ETS) can't increase or decreased CO 2 emissions continuously in 3 periods respectively. Therefore, adjusting ES, ETS, energy mix of transformation and decreasing the EI of each production sector will be the main routes to reduce CO 2 emissions. Developing clean energy to substitute fossil energy and enforcement of carbon capture will be necessary in the future.

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“…York et al [16] pointed out that additional conceptually appropriate factors can be added to the basic STIRPAT model to improve its multiplicative specification. Subsequently, corresponding improvements are often made to the original model in a variety of empirical studies according to their specific research aims and requirements [24]. Most studies added additional variables to the basic STIRPAT model.…”

Section: Extended Stirpat Modelmentioning

confidence: 99%

Effects of Population and Land Urbanization on China’s Environmental Impact: Empirical Analysis Based on the Extended STIRPAT Model

2017

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Abstract:China has been undergoing a very rapid but unbalanced urbanization, characterized by under-urbanization of its population and faster urbanization of the land. In such a situation, the urbanization of the population and the land may produce different effects on the natural environment. In addition, due to substantial inter-regional differences, the influence of urbanization on the environment is likely to vary across regions at different stages of economic and social development. This article expands the basic STIRPAT-Stochastic Impacts by Regression on Population, Affluence and Technology, model by adding industrialization level, foreign trade degree, population urbanization level and land urbanization level. Based on panel data from 2006 to 2014 and using this extended STIRPAT model, the article analyses the effects of the driving forces, especially population urbanization and land urbanization, on the environmental impact for the whole of China as well as on its eastern, middle and western regions. The results indicate that for the whole of China, population urbanization produces a significant negative effect on the environmental impact, while land urbanization has a small, but not statistically significant, positive effect. The effects of population urbanization and land urbanization vary across the eastern, middle and western regions, which are at different stages of economic and social development. Population urbanization and land urbanization have no significant influences on environmental impact in the eastern and middle regions, while in the western region population urbanization has a significant negative influence on environmental impact. The main driving factors of environmental impact remain population, affluence and energy intensity. This study also quantitatively calculates the actual contribution rate of each driving force for the 2006-2014 period. It contributes to understanding the characteristics and key driving forces in each region, allowing for appropriate policy recommendations.

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Estimation, characteristics, and determinants of energy-related industrial CO2 emissions in Shanghai (China), 1994–2009

Cited by 225 publications

References 42 publications

CO2 emissions from China’s lime industry

CO2 emissions from China’s lime industry

Energy-Related Carbon Emissions in Shanghai: Driving Forces and Reducing Strategies

Effects of Population and Land Urbanization on China’s Environmental Impact: Empirical Analysis Based on the Extended STIRPAT Model

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