Efficiency assessment and scenario simulation of the water-energy-food system in the Yellow river basin, China

Chenjun Zhang, Xiangyang Zhao and Changfeng Shi

Energy, 2024, vol. 305, issue C

Abstract: Comprehending the efficiency of the Water-Energy-Food system within basins is crucial to enhance their resource utilization and facilitate high-quality development. This research evaluates the efficiency of the Water-Energy-Food (WEF) system in the Yellow River Basin between 2005 and 2021 using the super efficiency network Data Envelopment Analysis (DEA) model, addressing the limitation of traditional DEA models by considering both input and output within system. A multi-scenario combination is established under different development modalities, and an innovative attempt is made to introduce machine learning into efficiency prediction. The GA-LSTM (Genetic Algorithm-Long Short-Term Memory) model is constructed to forecast the efficiency values across multiple scenarios and identify the optimal scenario namely the scenario with the highest efficiency value, to carry out the spatial-temporal analysis. The results indicate that: (1) The WEF system efficiency in the Yellow River Basin exhibited a variable rising trajectory from 2005 to 2021, with the economic scale playing a significant role as an influencing element; (2) The trained GA-LSTM model can accurately predict the efficiency value and change trend of WEF system in the Yellow River Basin; (3) Under scenario 4 when water and food subsystems are maintained in the baseline scenario, while the energy subsystem is regulated in the general saving scenario, the overall average efficiency value of WEF system in the Yellow River Basin in 2022–2035 is the highest, reaching 1.28, which is the optimal enhancement path; (4) Under the optimal scenario, the WEF system efficiency in the Yellow River Basin increases by 6.89 % annually, with Shandong having the greatest efficiency level and Qinghai the lowest.

Keywords: W-E-F nexus; Network DEA; Super-SBM; Scenario analysis; GA; LSTM (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S036054422402053X
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:305:y:2024:i:c:s036054422402053x

DOI: 10.1016/j.energy.2024.132279

Access Statistics for this article

Energy is currently edited by Henrik Lund and Mark J. Kaiser

More articles in Energy from Elsevier
Bibliographic data for series maintained by Catherine Liu ().