Analysis of spatio-temporal patterns of carbon emission from energy consumption by rural residents in China

Wenyu, 万文玉 Wan; Xueyan, 赵雪雁 Zhao; Weijun, 王伟军 Wang; Bing, 薛冰 Xue

doi:10.5846/stxb201607191466

Cited by 4 publications

(3 citation statements)

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“…Research conducted by [27] indicates that the geographical locations of Chinese villages greatly influence energy consumption patterns and subsequently lead to regional disparities in corresponding carbon emissions. This finding is further supported by the results from [28], which examined life-related carbon emission data in Chinese villages from 2001 to 2013. Additionally, the transportation sector within China also exhibits evident regional disparities in terms of carbon emissions, as demonstrated in [29].…”

Section: Regional Difference In Carbon Emissions Of Chinese Villagessupporting

confidence: 65%

“…However, other factors were also considered. Based on the categorization of life-related carbon emissions [28], these villages fall within the high-to-middle range compared to other regions in China. Additionally, according to agricultural emission data analyzed in [3], the examined area in this paper ranks at a high level due to its elevated economic status, greater reliance on commercial energy resources and increased presence of agricultural residuals.…”

Section: Selection Of Investigated Villages In This Studymentioning

confidence: 99%

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Approach for Calculating and Analyzing Carbon Emissions and Sinks of Villages: A Case Study in Northern China

Du,

Jiao,

Zhang

et al. 2024

Energies

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Despite a gradual decline in rural population due to urbanization, as of 2022, approximately 35% of China’s total population still resides in villages. Over a span of 40 years, carbon emissions from villages have significantly surged, with a sevenfold increase from energy consumption and a 46% rise from agriculture. Consequentially, the development of low-carbon villages is imperative. A comprehensive understanding of the primary sources of carbon emissions in villages is crucial for implementing practical and effective strategies towards low-carbon development. However, limited research has been conducted on quantifying carbon emissions and sinks for Chinese villages. This study aims to address this gap by proposing a methodology for assessing carbon emissions in villages, including the emissions of CO2, CH4 and N2O. Inspired by the IPCC standard methodology for greenhouse gas emissions at national levels and provincial greenhouse gas inventory guidelines customized for China’s context incorporating localized characteristics, this approach has been applied to seven villages in Northern China based on field investigations. Employing a range of methods including field surveys, questionnaires, statistical records and big-data platforms, we collected the carbon emission activity levels of the seven villages using the most up-to-date carbon emission factors. Subsequently, the collected data and facts are quantitatively processed to generate results that are compared among the seven villages. These findings are also compared with those from other studies. The analysis indicates that the primary industries in these villages significantly influence the total carbon emissions. Moreover, the study reveals that energy consumption in buildings, agriculture, transportation and waste disposal are the most influential emission sources. These findings provide valuable insights into the carbon emission landscape of villages and can serve as a guide for implementing strategies and policies aimed at promoting low-carbon development in the rural areas of Northern China.

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Section: Regional Difference In Carbon Emissions Of Chinese Villagessupporting

confidence: 65%

Section: Selection Of Investigated Villages In This Studymentioning

confidence: 99%

Approach for Calculating and Analyzing Carbon Emissions and Sinks of Villages: A Case Study in Northern China

Du,

Jiao,

Zhang

et al. 2024

Energies

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“…Domestic scholars such as Yan et al found, through methods such as the Gini coefficient and Thiel coefficient, that there are significant differences in carbon emission intensity among different provinces, cities, and autonomous regions in China, and the differences gradually expand with the passage of time [13]. In terms of studying the influencing factors of carbon emissions, by using geographic detectors, geographic weighted regression models (GWRs), LMDI factor decomposition methods, and STIRPAT model methods, scholars have explored the impact of economic growth, energy structure, urbanization, industrial structure, population size, and other factors on carbon emissions [17][18][19][20][21][22]. Scholars Shi et al applied the LMDI model to analyze the contribution rate of driving factors such as economic growth and population size to carbon emissions [19].…”

Section: Introductionmentioning

confidence: 99%

Spatiotemporal Pattern and Driving Factors of Carbon Emissions in Guangxi Based on Geographic Detectors

Guo,

Lai,

Jia

et al. 2023

Sustainability

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Analysis of the spatiotemporal distribution pattern and driving factors of carbon emissions has been a focus of research in recent years. However, at the county level, analyses of the driving factors of carbon emissions are still relatively few. This study selected the Guangxi Zhuang Autonomous Region as the research subject, selecting the normalized difference vegetation index (NDVI), nighttime light index (NLI), gross domestic product (GDP), and population density (POP) as driving factors. Based on the geographic detector method, the spatiotemporal distribution pattern and driving factors of carbon emissions at the county level in Guangxi were investigated. The results show the following: (1) There are significant regional differences in the degree of change in carbon emissions. From 2005 to 2020, the total carbon emissions in Guangxi show an upward trend, presenting a “high in the south and low in the north” distribution characteristic, gradually forming a high-level region in the capital city of Nanning, the city of Liuzhou, and some coastal cities (such as the cities of Qinzhou, Beihai, and Fangchenggang) as the core of carbon emissions; (2) NDVI, NLI, GDP, and POP have a relatively high impact on the carbon emission pattern in Guangxi, and the impact of human activity intensity on carbon emissions is higher than that of the influencing factors of NDVI; (3) The interaction between NDVI, NLI, GDP, and POP has a significant impact on the carbon emission pattern. The aforementioned results can provide decision-making suggestions for the social and economic development of Guangxi, as well as the formulation of carbon sequestration policies.

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Research on the Coupling Coordination and Driving Factors of “Carbon Emissions-Population-Economy” in National Key Ecological Functional Areas

张

2023

GSER

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Analysis of spatio-temporal patterns of carbon emission from energy consumption by rural residents in China

Cited by 4 publications

References 0 publications

Approach for Calculating and Analyzing Carbon Emissions and Sinks of Villages: A Case Study in Northern China

Approach for Calculating and Analyzing Carbon Emissions and Sinks of Villages: A Case Study in Northern China

Spatiotemporal Pattern and Driving Factors of Carbon Emissions in Guangxi Based on Geographic Detectors

Research on the Coupling Coordination and Driving Factors of “Carbon Emissions-Population-Economy” in National Key Ecological Functional Areas

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