4.7 Article

Greenhouse gas emission of pastoralism is lower than combined extensive/intensive livestock husbandry: A case study on the Qinghai-Tibet Plateau of China

Journal

JOURNAL OF CLEANER PRODUCTION
Volume 147, Issue -, Pages 514-522

Publisher

ELSEVIER SCI LTD
DOI: 10.1016/j.jclepro.2017.01.126

Keywords

GHG emission intensity; Pastoralism; Combined extensive/intensive system; Life cycle assessment; Qinghai-Tibet Plateau

Funding

  1. National Natural Science Foundation of China [41171428]

Ask authors/readers for more resources

The increasing demand of livestock products and production efficiency of livestock husbandry, and restoration of grassland ecosystem have been inducing the rapid transition of livestock husbandry systems from pastoralism into intensive systems. Such transition has been resulted in changes in the greenhouse gas (GHG) emissions, though it is rarely studied, especially in the pastoral area of China. Aimed to address this question, on the Qinghai-Tibet Plateau we selected Chanaihai village as the pastoralism system, and Guinan Grassland Development Limited Company as the combination of extensive and intensive livestock husbandry system, to compare the GHG emission between the two systems using life cycle assessment method. Our results showed that the GHG emission intensity both in per unit of area and per unit of carcass weight in the combined extensive/intensive livestock husbandry were higher than the pastoralism, indicating that the shift into the combined extensive/intensive livestock husbandry system increased the GHG emission. Such results could be attributed to the lower soil carbon uptake and higher GHG emission derived from the external inputs such as seed, diesel, and electricity in the combined extensive/intensive system. These findings demonstrated that the ongoing transition in the pastoral area of Qinghai-Tibet Plateau may be inappropriate Under the background of global GHG mitigation. As suggestions, we argued that reduction in the manure combustion and increase in soil carbon uptake could be effective measures to reduce the GHG emission intensity of livestock husbandry. (C) 2017 Elsevier Ltd. All rights reserved.

Authors

I am an author on this paper
Click your name to claim this paper and add it to your profile.

Reviews

Primary Rating

4.7
Not enough ratings

Secondary Ratings

Novelty
-
Significance
-
Scientific rigor
-
Rate this paper

Recommended

No Data Available
Article Green & Sustainable Science & Technology

Relative evaluation of probabilistic methods for spatio-temporal wind forecasting

Lars odegaard Bentsen, Narada Dilp Warakagoda, Roy Stenbro, Paal Engelstad

Summary: This study investigates uncertainty modeling in wind power forecasting using different parametric and non-parametric methods. Johnson's SU distribution is found to outperform Gaussian distributions in predicting wind power. This research contributes to the literature by introducing Johnson's SU distribution as a candidate for probabilistic wind forecasting.

JOURNAL OF CLEANER PRODUCTION (2024)

Article Green & Sustainable Science & Technology

Comparison of ethane recovery processes for lean gas based on a coupled model

Xing Liu, Qiuchen Wang, Yunhao Wen, Long Li, Xinfang Zhang, Yi Wang

Summary: This study analyzes the characteristics of process parameters in three lean gas ethane recovery processes and establishes a prediction and multiobjective optimization model for ethane recovery and system energy consumption. A new method for comparing ethane recovery processes for lean gas is proposed, and the addition of extra coolers improves the ethane recovery. The support vector regression model based on grey wolf optimization demonstrates the highest prediction accuracy, and the multiobjective multiverse optimization algorithm shows the best optimization performance and diversity in the solutions.

JOURNAL OF CLEANER PRODUCTION (2024)

Article Green & Sustainable Science & Technology

A novel deep-learning framework for short-term prediction of cooling load in public buildings

Cairong Song, Haidong Yang, Xian-Bing Meng, Pan Yang, Jianyang Cai, Hao Bao, Kangkang Xu

Summary: The paper proposes a novel deep learning-based prediction framework, aTCN-LSTM, for accurate cooling load predictions. The framework utilizes a gate-controlled multi-head temporal convolutional network and a sparse probabilistic self-attention mechanism with a bidirectional long short-term memory network to capture both temporal and long-term dependencies in the cooling load sequences. Experimental results demonstrate the effectiveness and superiority of the proposed method, which can serve as an effective guide for HVAC chiller scheduling and demand management initiatives.

JOURNAL OF CLEANER PRODUCTION (2024)

Article Green & Sustainable Science & Technology

The impact of social interaction and information acquisition on the adoption of soil and water conservation technology by farmers: Evidence from the Loess Plateau, China

Zhe Chen, Xiaojing Li, Xianli Xia, Jizhou Zhang

Summary: This study uses survey data from the Loess Plateau in China to evaluate the impact of social interaction on the adoption of soil and water conservation (SWC) technology by farmers. The study finds that social interaction increases the likelihood of farmers adopting SWC, and internet use moderates this effect. The positive impact of social interaction on SWC adoption is more pronounced for farmers in larger villages and those who join cooperative societies.

JOURNAL OF CLEANER PRODUCTION (2024)

Article Green & Sustainable Science & Technology

Study on synergistic heat transfer enhancement and adaptive control behavior of baffle under sudden change of inlet velocity in a micro combustor

Chenghua Zhang, Yunfei Yan, Kaiming Shen, Zongguo Xue, Jingxiang You, Yonghong Wu, Ziqiang He

Summary: This paper reports a novel method that significantly improves combustion performance, including heat transfer enhancement under steady-state conditions and adaptive stable flame regulation under velocity sudden increase.

JOURNAL OF CLEANER PRODUCTION (2024)