4.7 Article

Impacts of agricultural industrial agglomeration on China's agricultural energy efficiency: A spatial econometrics analysis

Journal

JOURNAL OF CLEANER PRODUCTION
Volume 260, Issue -, Pages -

Publisher

ELSEVIER SCI LTD
DOI: 10.1016/j.jclepro.2020.121011

Keywords

Industrial agglomeration; Energy efficiency; Spatial econometrics; Agriculture in China

Funding

  1. National Natural Science Foundation of China [71703159]
  2. Fundamental Research Funds for Central Non-profit Scientific Institution [JBYW-AII-2019-08]

Ask authors/readers for more resources

The rapid development of traditional agriculture in China was achieved at the expense of high energy consumption and investments. However, the global green development trend made it necessary for the country to transform its agricultural energy utilization. Energy efficiency changes are affected by many factors, particularly industrial agglomeration. In recent years, the Chinese government has introduced a series of policies, including setting major producing regions for grains and advantageous regions for characteristic agricultural product. These have caused significant changes to the spatial layout of the agriculture industry. However, there is still a lack of research on the impact of these changes on agricultural energy efficiency (AEE). In this study, panel data of 30 Chinese provinces from 2000 to 2016 were entered into stochastic frontier models to measure the country's AEE at the provincial level. A series of spatial econometric models were also used to analyze the impact of agricultural industrial agglomeration on China's AEE. The results indicated that the country's AEE exhibited obvious spatial gradients and correlations. After controlling the impacts of spatial correlation and other factors in the models, agricultural industrial agglomeration was found to have an overall positive impact on China's AEE. In the future, policies should be formulated to increase AEE by establishing agricultural functional areas, strengthening the innovation and sharing of green development technologies at the farm level, and promoting the optimization of energy structures in agricultural and rural areas. (C) 2020 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)