Carbon footprint and carbon emission intensity of grassland wind farms in Inner Mongolia

Liu, Pengtao; Liu, Luyao; Xue, Xiangxin; Zhao, Yanyun; Niu, Jianming; Zhang, Qing

doi:10.1016/j.jclepro.2021.127878

Cited by 39 publications

(12 citation statements)

References 42 publications

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“…Inner Mongolia is an important energy producing region in China, not only rich in fossil energy reserves, but also rich in nonfossil energy resources such as wind and solar energy, with the background of carbon peaking and carbon neutral in China in recent years, the energy structure of Inner Mongolia is undergoing transformation. Non-fossil fuels are becoming an increasingly important part of energy consumption [9]. In the 14th Five-Year Plan, Inner Mongolia's renewable energy generation capacity reached 135 million kilowatts, including 89 million kilowatts of wind power [10].…”

Section: Sampling Areamentioning

confidence: 99%

Multi-topography dataset for wind turbine detection from remote sensing image

Mi,

Zhao,

Fang

et al. 2024

Fourth International Conference on Geology, Mapping, and Remote Sensing (ICGMRS 2023)

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Owing to the global energy conservation and emission reduction, the wind power industry is developing rapidly in China because of abundant wind energy resources. Wind power has become the third largest power source and remain growing rapidly in China. Efficient and accurate measurement of the location of wind turbine is important for assessing local wind power development. Remote sensing and deep learning technologies offer technical possibilities for wind turbine identification. However, until now there is few publicly available land remote sensing wind power turbine object detection dataset. Aiming at this question, this paper designs a land remote sensing wind turbine dataset, which includes 4459 individual wind turbines in various environmental backgrounds, including sandy land, woodland, grassland, snowy land, and wasteland. In addition, the dataset is composed of YOLO and VOC markup formats to meet the experimental needs of researchers. This paper also evaluates the dataset under the various mainstream object detection frameworks and reach 92% in mAP. The dataset can be obtained from Zenodo website https://doi.org/10.5281/zenodo.7808269.

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Section: Sampling Areamentioning

confidence: 99%

Multi-topography dataset for wind turbine detection from remote sensing image

Mi,

Zhao,

Fang

et al. 2024

Fourth International Conference on Geology, Mapping, and Remote Sensing (ICGMRS 2023)

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“…Replacing fossil fuel electricity with electricity generated by wind power and solar energy can reduce GWP100 by 91% and 78%, respectively. The carbon emission factor of wind power generation in China is taken an average value of 0.00517 tCO 2 /MWh from three published studies [27], [28], [29]. The carbon emission factor of solar power generation is taken from from a whole life cycle assessment study made on photovoltaic systems [30].…”

Section: Sensitivity Analysismentioning

confidence: 99%

Life Cycle Assessment for Industrial Gas Production in China

Zhang,

Cheng,

Zhao

et al. 2024

Preprint

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Purpose Industrial gases play an essential role in a wide range of sectors, including chemicals, metals, energy, and healthcare industry. There has been a strong push for disclosing environmental footprints of industrial gas products due to their high energy intensity production processes and the urgent need for industry decarbonization. This article aims for a comparative life cycle assessment of three main industrial gas products, oxygen, nitrogen and argon with sensitivity analysis for identifying major contribution of environmental impacts. Methods Existing production routes of industrial gases were reviewed. The life cycle assessment (LCA) method was used to study and evaluate the environmental footprints of three major industrial gas products produced by air-separation process. Life cycle inventory of each production stage was conducted based on the process unit, national standards and literature. Results The assessment results showed that life cycle global warming potential (GWP100) of oxygen, nitrogen and argon per 1,000 normal cubic meter are 460, 91 and 1,550 kg CO2 eq, respectively. Argon has the highest environmental and resource use impacts among all three gas products. Sensitivity analysis showed that electricity and steam consumption are the two most important factors affecting life-cycle carbon emissions and contributes more than 90% of the overall emissions. Conclusions The environmental footprint of oxygen can be decreased by 91% and 78% via replacing fossil fuel electricity with wind and solar power, respectively. As grid decarbonization taking place nation-wide, such interesting characteristics endowed industrial gases with enormous potential to achieve the decarbonization goal.

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“…In terms of the geographical scope of the carbon footprint study, it involves global (Shi & Yin, 2021), countries (Jack & Ivanova, 2021; Marinelli et al, 2021; Yan et al, 2022; F. Yu, Dong, et al, 2022; J. Yu, Saydaliev, et al, 2022), urban areas (Liu et al, 2021; Xia et al, 2022; Xing et al, 2022) and rural areas (Jia et al, 2022). In terms of the industry scope of carbon footprint research, it involves agriculture (Cui et al, 2022; Feng et al, 2022), industry (Leclerc et al, 2022; Wu et al, 2022), construction industry (Labaran et al, 2022), energy industry (P. Li et al, 2022), medical industry (McAlister et al, 2022), catering industry (Lohmann et al, 2022; Silva & Silva, 2022; Wróbel‐Jędrzejewska & Polak, 2022), and internet industry (Hoffmann et al, 2022; Sarkodie & Owusu, 2022) etc.…”

Section: Literature Reviewmentioning

confidence: 99%

Impact of solar energy generation on carbon footprint: Evidence from China

Hui-qing

2023

Geological Journal

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To investigate the impact of solar energy on the carbon footprint, to find effective measures to reduce the carbon footprint and slow global warming as soon as possible, this paper takes 30 provinces in China as an example. First, the inter‐regional input–output model is used to calculate the carbon footprint of each province. Then, the panel quantile regression model is used to investigate the impact of solar energy generation on different quantiles of carbon footprint. The results show that from 2012 to 2020, China's carbon footprint is at a high level, which is not conducive to the achievement of the goal of peaking carbon dioxide emissions. The eastern region has the highest carbon footprint, followed by the western region, the central region has the lowest carbon footprint, and its carbon footprint proportion continues to decline. The increase of solar energy generation can significantly reduce the carbon footprint of high quantile location, but has no significant impact on the carbon footprint of the middle and low quantile locations. Except for the negative impact of afforestation area on carbon footprint, the others control variables are all positive. Therefore, to reduce the carbon footprint of China, it is necessary to upgrade solar technology, increase solar energy generation, reduce energy consumption per unit of GDP (Gross Domestic Product) and the share of coal consumption, strengthen forestry management and reduce reliance on private vehicles.

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Carbon footprint and carbon emission intensity of grassland wind farms in Inner Mongolia

Cited by 39 publications

References 42 publications

Multi-topography dataset for wind turbine detection from remote sensing image

Multi-topography dataset for wind turbine detection from remote sensing image

Life Cycle Assessment for Industrial Gas Production in China

Impact of solar energy generation on carbon footprint: Evidence from China

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