In an article published in the journal Nature, researchers proposed a comprehensive evaluation model for land resource carrying capacity (LRCC) based on entropy weight and normal cloud similarity.
The carrying capacity levels of each system during the 12th Five-Year Plan and 13th Five-Year Plan periods. Image Credit: https://www.nature.com/articles/s41598-024-59692-2
Through empirical analysis using an asphalt pavement experiment and a case study of China's Chongqing city, the model demonstrated effectiveness in assessing LRCC. Findings suggested improvements in LRCC over the past decade, with implications for land planning, particularly focusing on soil and water resources and economic and technological systems.
Background
The evaluation of LRCC holds significant importance in land management and urban development, particularly amidst the rapid pace of industrialization and urbanization globally. While existing research has made strides in assessing LRCC using techniques like the technique for order preference by similarity to an ideal solution (TOPSIS), error backpropagation (EBP), and single-factor evaluation, challenges persist due to uncertainties and variations in evaluation indicators.
Previous methods, often relying on fuzzy numbers or complex weight construction, have struggled to address ambiguity and subjectivity in determining indicator weights accurately. Additionally, cloud model evaluation primarily based on membership degree lacks robustness, with limited research on cloud evaluation using similarity degree. This paper aimed to bridge these gaps by proposing a comprehensive evaluation model for LRCC based on entropy weight and normal cloud similarity.
By combining the objective characteristics of entropy weight with cloud similarity measurement, the proposed model offered a more robust and objective approach to determining index weights and assessing LRCC. Utilizing Chongqing, China, as a case study area, the paper conducted empirical analysis and comprehensive evaluation to validate the effectiveness and feasibility of the proposed model. Through this approach, the paper sought to provide a novel method for LRCC evaluation that overcame the limitations of existing techniques and contributed to advancing land research and planning practices.
Theory, Methods, and Evaluation Model for LRCC
The paper introduced a comprehensive evaluation model for LRCC based on entropy weight and normal cloud similarity. This model addressed the uncertainties and variations inherent in LRCC evaluation by integrating the objective characteristics of entropy weight with cloud similarity measurement. The cloud model, defined by its three numerical characteristics (expectation, entropy, and hyper-entropy), offered a robust framework for handling uncertainty in qualitative concepts and transforming them into quantitative representations.
By leveraging the Wasserstein distance, the paper proposed a novel method for calculating normal cloud similarity, enhancing the accuracy and stability of the evaluation process. The entropy weight method provided an objective approach to determining index weights, minimizing the impact of human subjectivity on the evaluation results.
Through a systematic evaluation process, including establishing factor domains, calculating index weights, and constructing cloud models, the proposed model offered a comprehensive and rigorous assessment of LRCC. Validation analysis conducted using an asphalt pavement performance evaluation experiment demonstrated the feasibility and effectiveness of the proposed method. By comparing the results with existing literature and actual data, the model's accuracy and reliability were affirmed.
The comprehensive evaluation model, guided by entropy weight and normal cloud similarity, yielded consistent and meaningful results, aligning with both theoretical expectations and practical observations. The research contributions extended beyond theoretical advancements to practical applications in land management and urban development.
By providing a systematic and objective approach to LRCC evaluation, the proposed model offered valuable insights for policymakers, urban planners, and land resource managers. It empowered decision-makers with a reliable tool for assessing the utilization of urban land resources and guiding sustainable development practices. Overall, the authors filled critical gaps in existing LRCC evaluation methods by proposing a comprehensive model that combined entropy weight and normal cloud similarity.
A Comprehensive Analysis
The authors discussed the practical application of the comprehensive evaluation model for Chongqing's LRCC, shedding light on its unique characteristics and providing valuable insights for local land planning and development strategies. With land serving as the bedrock of human progress, its sustainable management was paramount in the face of escalating population growth and resource depletion.
Against this backdrop, LRCC evaluation became increasingly pivotal, especially within the context of national development plans prioritizing ecological preservation and economic advancement. Situated in southwest China, Chongqing's strategic significance as a major metropolitan hub necessitated a nuanced understanding of its LRCC dynamics.
Leveraging data spanning from 2011 to 2020, the researchers constructed a comprehensive index system, encompassing water and soil resources, ecological environment, social and cultural aspects, and economic and technological dimensions. This meticulous approach ensured a holistic assessment of LRCC, facilitating informed decision-making.
By applying the developed evaluation model to Chongqing's LRCC, notable trends emerged, highlighting shifts in carrying capacity levels across different systems. Notably, improvements were observed in ecological resilience and economic vitality, signaling a balanced approach to development. The analysis underscored Chongqing's commitment to harmonizing urban growth with environmental stewardship, thereby charting a sustainable trajectory for future land management endeavors.
Visual representations of LRCC trends during distinct planning periods offered stakeholders valuable insights into system-level dynamics, guiding targeted interventions aimed at bolstering LRCC. From enhancing soil and water management practices to fostering innovation in economic sectors, Chongqing's journey toward sustainable development was both dynamic and multifaceted.
Ultimately, this research served as a roadmap for policymakers and urban planners, facilitating evidence-based strategies for optimizing LRCC while safeguarding environmental integrity in Chongqing's evolving landscape.
Conclusion
In conclusion, the proposed comprehensive evaluation model for LRCC based on entropy weight and normal cloud similarity demonstrated effectiveness in assessing LRCC, as evidenced by empirical analysis and case studies. Findings suggested improvements in LRCC over the past decade, particularly in Chongqing, with implications for land planning, focusing on soil and water resources and economic and technological systems.
Further research could enhance the evaluation model by expanding the index system and incorporating both subjective and objective methods for determining index weights. Overall, this study offered valuable insights into sustainable land management and urban development practices.
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