Analysis of Carbon Emission Caused by Food Consumption in City and Rural Inhabitants in China

Zhi, Jing; Gao, Jixi

doi:10.1109/icbbe.2009.5162345

“…As the direct GHG emissions from food consumption by human metabolism will overlap the GHG emissions from wastewater treatment, here only the indirect GHG emissions from food processing and supply are calculated using the following formulas:Where EF i is indirect GHG emissions from food consumption; EF d is direct GHG emissions from food consumption; K is proportion of EF i to EF d and refers to the proportion of indirect GHG emissions to direct GHG emissions from Chinese residential food consumption in 2006 [32]. EF d is estimated as follows: Where W i is consumption amount of food i ; R i is carbon content of food i ; C p i , C fi , and C ci are contents of protein, fat, and carbohydrate of food i respectively; P i , F i , and C i are the carbon content of protein, fat, and carbohydrate respectively; C pi , C f i , and C ci refers to China food composition [33].…”

Section: Methodsmentioning

confidence: 99%

Greenhouse Gas Emissions Accounting of Urban Residential Consumption: A Household Survey Based Approach

Lin

¹

,

Yu

²

,

Bai

³

et al. 2013

View full text Add to dashboard Cite

Devising policies for a low carbon city requires a careful understanding of the characteristics of urban residential lifestyle and consumption. The production-based accounting approach based on top-down statistical data has a limited ability to reflect the total greenhouse gas (GHG) emissions from residential consumption. In this paper, we present a survey-based GHG emissions accounting methodology for urban residential consumption, and apply it in Xiamen City, a rapidly urbanizing coastal city in southeast China. Based on this, the main influencing factors determining residential GHG emissions at the household and community scale are identified, and the typical profiles of low, medium and high GHG emission households and communities are identified. Up to 70% of household GHG emissions are from regional and national activities that support household consumption including the supply of energy and building materials, while 17% are from urban level basic services and supplies such as sewage treatment and solid waste management, and only 13% are direct emissions from household consumption. Housing area and household size are the two main factors determining GHG emissions from residential consumption at the household scale, while average housing area and building height were the main factors at the community scale. Our results show a large disparity in GHG emissions profiles among different households, with high GHG emissions households emitting about five times more than low GHG emissions households. Emissions from high GHG emissions communities are about twice as high as from low GHG emissions communities. Our findings can contribute to better tailored and targeted policies aimed at reducing household GHG emissions, and developing low GHG emissions residential communities in China.

show abstract

“…Table 1 shows the carbon emission coefficients of agricultural material inputs [4,59]. The calculation of carbon emissions is expressed as follows:…”

Section: Carbon Emissions From Agricultural Materials Inputmentioning

confidence: 99%

Investigating Low-Carbon Agriculture: Case Study of China’s Henan Province

Su

¹

,

Jiang

²

,

Li

³

2017

View full text Add to dashboard Cite

Developing low-carbon agriculture requires investigating the trajectory, decoupling statuses, and driving forces of agricultural carbon emissions. This study explored the evolution of agricultural carbon emissions based on 18 kinds of major carbon emission sources in Henan Province of China, which produces approximately one-tenth of China's total grain output. We then analyzed the relationship between carbon emissions and economic growth using the decoupling elasticity model, and identified the factors driving the decoupling status. This analysis was done with a decoupling elasticity model, using the Logarithmic Mean Divisia Index technique. There were three key results: (1) Agricultural carbon emissions totaled 16.61 million tons in 1999, and increased by 7.99% to 17.93 million tons in 2014, with an average growth rate of approximately 0.65%; (2) The decoupling relationship between agricultural carbon emissions and economic output was dominated by weak decoupling during the study period; (3) Agricultural labor productivity was the leading contributor to changes in agricultural carbon emissions, followed by farming-animal husbandry carbon intensity, labor, and agricultural structure.

show abstract

“…Where EF i is indirect GHG emissions from food consumption; EF d is direct GHG emissions from food consumption; K is proportion of EF i to EF d and refers to the proportion of indirect GHG emissions to direct GHG emissions from Chinese residential food consumption in 2006 [32]. EF d is estimated as follows:…”

Section: Qualitative Variables Transform Standardsmentioning

confidence: 99%

Greenhouse Gas Emissions Accounting of Urban Residential Consumption: A Household Survey Based Approach

Lin¹,

Yu²,

Bai³

et al. 2015

Urban Development for the 21st Century

View full text Add to dashboard Cite

Devising policies for a low carbon city requires a careful understanding of the characteristics of urban residential lifestyle and consumption. The production-based accounting approach based on top-down statistical data has a limited ability to reflect the total greenhouse gas (GHG) emissions from residential consumption. In this paper, we present a survey-based GHG emissions accounting methodology for urban residential consumption, and apply it in Xiamen City, a rapidly urbanizing coastal city in southeast China. Based on this, the main influencing factors determining residential GHG emissions at the household and community scale are identified, and the typical profiles of low, medium and high GHG emission households and communities are identified. Up to 70% of household GHG emissions are from regional and national activities that support household consumption including the supply of energy and building materials, while 17% are from urban level basic services and supplies such as sewage treatment and solid waste management, and only 13% are direct emissions from household consumption. Housing area and household size are the two main factors determining GHG emissions from residential consumption at the household scale, while average housing area and building height were the main factors at the community scale. Our results show a large disparity in GHG emissions profiles among different households, with high GHG emissions households emitting about five times more than low GHG emissions households. Emissions from high GHG emissions communities are about twice as high as from low GHG emissions communities. Our findings can contribute to better tailored and targeted policies aimed at reducing household GHG emissions, and developing low GHG emissions residential communities in China.

show abstract

Analysis of Carbon Emission Caused by Food Consumption in City and Rural Inhabitants in China

Cited by 13 publications

References 3 publications

Greenhouse Gas Emissions Accounting of Urban Residential Consumption: A Household Survey Based Approach

Greenhouse Gas Emissions Accounting of Urban Residential Consumption: A Household Survey Based Approach

Investigating Low-Carbon Agriculture: Case Study of China’s Henan Province

Greenhouse Gas Emissions Accounting of Urban Residential Consumption: A Household Survey Based Approach

Contact Info

Product

Resources

About