Coastal villages are vital centers of human activity, where traditional lifestyles combine with the demands of modern life. With increasing attention to reducing carbon footprints, these communities face unique challenges and opportunities. Their strategic location and diverse industrial patterns make coastal villages important contributors to carbon emissions and promising candidates for emissions reduction. Understanding the specific factors that influence emissions in these areas is crucial to developing effective strategies to minimize their environmental impact.
As global climate change concerns intensify, attention on carbon reduction strategies has expanded to various sectors, including rural areas. Coastal villages, due to their unique geographic and economic characteristics, have significant potential for carbon emission optimization. Led by Dr. Guanhua Qu together with Dr. Zejun Yu and Dr. Lei Ren from Tianjin University, Dr. Zhixin Li and Dr. Yao Wang from Tsinghua University, the team has investigated the carbon emission composition and reduction potential of coastal villages in China. Their findings, published in the journal Buildings, offer valuable insights into sustainable development in these regions.
Zejun Yu and his team conducted a comprehensive study focusing on coastal villages in Hebei Province, China. The study aimed to categorize carbon emissions, identify key influencing factors, and propose tailored reduction strategies. The researchers collected data through field investigations, questionnaires, and remote sensing maps, enabling a comprehensive analysis of carbon emissions from various sources, including agricultural activities, residential life, transportation, and waste management.
The researchers employed a k-means clustering algorithm to classify the villages based on their carbon emission characteristics. This method enabled the identification of three main clusters, each with unique emission profiles. The results revealed that energy consumption from buildings and transportation were the largest contributors to carbon emissions, followed by agricultural activities and waste management. Notably, energy consumption from buildings and waste emissions were highest in one cluster, while agricultural and transportation emissions dominated in another.
“Our research shows that different types of villages require tailored approaches to effectively reduce carbon emissions,” Qu said. The study’s categorization provides a basis for developing specific carbon reduction strategies for each type of village. The study also explored the correlation between various factors and carbon emissions using regression analysis. Key factors such as population size, cultivated area, number of livestock, and energy consumption were analyzed to understand their impact on overall emissions. This analysis provided a detailed understanding of the driving forces behind carbon emissions in different types of villages.
The research team’s findings underscore the critical role of low-carbon planning in coastal villages. With improved infrastructure and economic foundations, these villages have significant potential for carbon emission reduction. By focusing on optimizing energy consumption, improving waste management practices, and promoting sustainable agricultural practices, coastal villages can substantially contribute to national and global carbon reduction goals.
In summary, the study by Zejun Yu, Guanhua Qu and colleagues provides a comprehensive analysis of carbon emissions in coastal villages and proposes specific reduction strategies. The research highlights the importance of tailored approaches to address the unique challenges and opportunities of different types of villages. As the global climate crisis continues to evolve, these studies provide valuable insights and practical solutions for sustainable rural development.
Journal reference
Yu, Z., Qu, G., Li, Z., Wang, Y., and Ren, L. Carbon emission composition and carbon reduction potential of coastal villages in a low-carbon context. Buildings, 2023, 13, 2925. DOI: https://doi.org/10.3390/buildings13122925
About the authors
Doctor Guanhua Qu He earned his PhD in Architecture from Tianjin University, where he currently works. He holds the LEED AP: BD+C certification from USGBC and the WELL AP certification from IWBI. In addition, he serves as a reviewer for several journals and is a member of the editorial board of international conferences. His main research areas include the impact of human factors on visual physiological characteristics, the physiological impact of indoor environments on humans, and environmental assessment. In these fields, he has published more than 30 articles, applied for more than 10 invention patents, and led or participated in more than 10 research projects.
Zejun Yu is a PhD candidate at the Institute of Architectural Technology, School of Architecture, Tianjin University. His main research interests are building light ecology and building energy conservation. At present, he is mainly engaged in exploring the status quo of rural carbon emissions in China and designing the optimal development path for rural carbon emissions, and has studied and published several important papers in this field. His scientific focus is on the status quo and optimal development path for carbon emissions in the Beijing-Tianjin-Hebei region of China.
Yao WangA researcher at Tsinghua University, she focuses on the research of intelligent construction and BIM technology. She serves as a scientific research director of the Scientific Research Management Department, a member of the Intelligent Construction Branch of China Construction Standardization Association (CECS), an engineer, a contracted author of the new construction journal “Housing and Real Estate”, and an expert of the Zoominglian Standards Acquisition Committee. She has participated in the compilation of 2 livable housing and intelligent construction standards, published more than 30 academic papers, 6 utility model patents, authored 2 industry monographs, and participated in more than 10 research topics.
Lei Ren is a PhD candidate at the School of Architecture, Tianjin University. She holds the LEED AP: BD+C certification from USGBC and the WELL AP certification from IWBI. Her main research interests focus on human factors affecting indoor environments and the detection and prediction of environmental factors. She has worked intensively in these fields for more than six years, publishing more than 20 articles and applying for more than 10 patents. Her main achievement is revealing the impact of visual physiological factors on visual perception.
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