Wang et al. (2025) Hybrid Gaussian process regression-based harmony assessment in a water–land–energy–food–carbon-emission coupled system

Identification

• Journal: Journal of Hydrology
• Year: 2025
• Date: 2025-10-11
• Authors: Chunqing Wang, Liangliang Zhang, Dong Liu, Mo Li, Muhammad Abrar Faiz, Tianxiao Li, Song Cui, Muhammad Imran Khan
• DOI: 10.1016/j.jhydrol.2025.134408

Research Groups

• School of Water Conservancy & Civil Engineering, Northeast Agricultural University, Harbin, Heilongjiang 150030, China
• Joint International Research Laboratory of Habitat Health of Black Soil in Cold Regions, Ministry of Education, Northeast Agricultural University, Harbin, Heilongjiang 150030, China
• Key Laboratory of Effective Utilization of Agricultural Water Resources of Ministry of Agriculture and Rural Affairs, Northeast Agricultural University, Harbin, Heilongjiang 150030, China
• Heilongjiang Provincial Key Laboratory of Water Resources and Water Conservancy Engineering in Cold Region, Northeast Agricultural University, Harbin, Heilongjiang 150030, China
• Research Center for Eco-Environment Protection of Songhua River Basin, Northeast Agricultural University, Harbin, Heilongjiang 150030, China
• Department of Irrigation and Drainage, University of Agriculture, Faisalabad, Pakistan

Short Summary

This study developed an improved Gaussian process regression model (AOA-L-BFGS-GPR) to assess the dynamic harmony of water–land–energy–food–carbon-emission (WLEFC) coupled systems. Applied to Heilongjiang Province, China, the research identified key obstacles and projected future harmony under different SSP pathways, demonstrating the framework's utility for sustainable agricultural development.

Objective

• To examine the harmony of water–land–energy–food–carbon-emission (WLEFC) coupled systems using harmony theory.
• To develop a harmony analysis framework and enhance its evaluation index system.
• To evaluate the dynamic development of WLEFC harmony in Heilongjiang Province, China, identify key challenges, and analyze trends.

Study Configuration

• Spatial Scale: Heilongjiang Province, China, including specific regions like Daxing’anling and Heihe.
• Temporal Scale: Historical analysis from 1994 to 2023; future projections up to 2055.

Methodology and Data

• Models used: Harmony theory, improved Gaussian process regression model (AOA-L-BFGS-GPR) combining an arithmetic optimization algorithm (AOA) with a limited-memory BFGS algorithm (L-BFGS). Scenario analysis using Shared Socioeconomic Pathways (SSP126, SSP245, SSP370, SSP585).
• Data sources: Statistical data related to water, land, energy, food production, carbon emissions, and rural population.

Main Results

• The WLEFC system harmony in Heilongjiang Province increased from 3.085 to 3.527 between 1994 and 2023.
• The water resource subsystem consistently remained the primary obstacle, with its barrier degree remaining above 10%.
• Rural population decline (e.g., Daxing’anling and Heihe with agricultural population barrier degrees of 20.99% and 17.43%, respectively) and slow progress in energy conservation emerged as significant hindering factors later in the study period.
• Northeastern cities faced challenges of low energy efficiency and high carbon emissions.
• Scenario analysis indicated that the SSP126 pathway offered the best potential for green agricultural development, projecting system harmony to reach 3.923 by 2055 under this pathway.

Contributions

• Developed a novel harmony analysis framework and an enhanced evaluation index system for WLEFC coupled systems.
• Introduced an improved Gaussian process regression model (AOA-L-BFGS-GPR) demonstrating superior accuracy, stability, and reasonability for WLEFC harmony assessment.
• Provided new insights and methods for analyzing WLEFC systems, particularly in the context of sustainable agricultural development in cold regions.
• Identified specific obstacles and trends in WLEFC harmony in Heilongjiang Province, offering practical implications for regional policy-making.

Funding

• Not explicitly mentioned in the provided text.

Citation

@article{Wang2025Hybrid,
  author = {Wang, Chunqing and Zhang, Liangliang and Liu, Dong and Li, Mo and Faiz, Muhammad Abrar and Li, Tianxiao and Cui, Song and Khan, Muhammad Imran},
  title = {Hybrid Gaussian process regression-based harmony assessment in a water–land–energy–food–carbon-emission coupled system},
  journal = {Journal of Hydrology},
  year = {2025},
  doi = {10.1016/j.jhydrol.2025.134408},
  url = {https://doi.org/10.1016/j.jhydrol.2025.134408}
}