| 引用本文: | 杨泽国,邹秋霞,段华波,杨家宽.全球城市生活垃圾产生量智能算法与非线性演化特征[J].哈尔滨工业大学学报,2026,58(6):80.DOI:10.11918/202512009 |
| YANG Zeguo,ZOU Qiuxia,DUAN Huabo,YANG Jiakuan.Intelligent algorithm and non-linear evolutionary characteristics of global municipal solid waste generation[J].Journal of Harbin Institute of Technology,2026,58(6):80.DOI:10.11918/202512009 |
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| 摘要: |
| 准确估算城市生活垃圾（MSW）产生量并识别其关键关联特征,是实现全球城市环境精细化管控及差异化治理的重要前提。然而,全球特别是发展中国家长时序数据的匮乏,以及传统模型对非线性关联特征解析能力的不足,导致现有研究难以精准还原全球MSW的历史产生规模与演化规律。为此,构建了基于XGBoost和K-Means++算法的全球MSW产生量智能估算与聚类分析框架。结果表明:XGBoost表现出优异的泛化能力,对未知国家MSW产生量的决定系数（R2）达0.91±0.07；交叉验证策略对比证实,模型成功捕捉了社会经济特征对MSW产生量预测的内在关联,而非单纯依赖时间趋势,具备良好的空间泛化可靠性。SHAP解释分析证明,人口规模与经济体量是影响MSW产生量的核心关联因素,并揭示了人均GDP(按购买力平价,PPP)与产生量之间呈现独特的“N型”非线性关联轨迹,突破了传统线性外推模型的局限。测算结果表明,全球MSW产生总量由1991年的（12.9±0.4）亿t攀升至2021年的（21.4±1.6）亿t,年均增长1.7%。聚类分析识别出全球各国的农业生存型、新兴工业型、成熟稳定型及高耗富裕型四类典型演化模式,其中,新兴工业型国家是全球MSW增量的主要来源。构建的预测框架具备良好的普适性,可为全球固废精细化管理、区域差异化政策制定及基础设施规划提供科学依据。 |
| 关键词: 生活垃圾 机器学习 SHAP解释 聚类分析 固废管理 |
| DOI：10.11918/202512009 |
| 分类号:X7 |
| 文献标识码:A |
| 基金项目:国家重点研发计划重点专项(2023YFC3902803) |
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| Intelligent algorithm and non-linear evolutionary characteristics of global municipal solid waste generation |
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YANG Zeguo1,2,ZOU Qiuxia1,2,DUAN Huabo2,YANG Jiakuan2
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(1.School of Artificial Intelligence and Automation, Huazhong University of Science and Technology, Wuhan 430074, China; 2.School of Environmental Science & Engineering, Huazhong University of Science and Technology, Wuhan 430074, China)
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| Abstract: |
| Accurate estimation of municipal solid waste (MSW) generation and identification of its key associative characteristics are important prerequisites for achieving refined global urban environmental management and differentiated governance. However, the scarcity of long-time-series data globally, especially in developing countries, and the insufficient capability of traditional models in resolving non-linear associative characteristics make it difficult for existing studies to accurately reconstruct the historical generation scale and evolutionary laws of global MSW. To this end, this paper established an intelligent estimation and cluster analysis framework for global MSW generation based on XGBoost and K-Means++ algorithms. The results indicate that XGBoost exhibits excellent generalization capability, with a coefficient of determination (R2) of 0.91±0.07 for MSW generation prediction in unknown countries; the comparison of cross-validation strategies confirms that the model successfully captures the intrinsic associations of socio-economic characteristics for MSW generation prediction, rather than merely relying on time trends, showing good spatial generalization reliability. SHAP interpretation analysis proves that population scale and economic volume are the core associative factors affecting MSW generation and reveals a unique "N-shaped" non-linear associative trajectory between GDP (in terms of purchasing power parity, PPP) per capita and generation, breaking the limitations of traditional linear extrapolation models. The estimation results indicate that the total global MSW generation has climbed from (1.29±0.04) billion t in 1991 to (2.14±0.16) billion t in 2021, with an average annual growth of 1.7%. Cluster analysis identifies four typical evolutionary patterns among global countries: agricultural subsistence, emerging industrial, mature stable, and high-consumption affluent patterns, among which emerging industrial countries are the main source of global MSW increment. The constructed prediction framework possesses good universal applicability and can provide a scientific basis for global solid waste′s refined management, regionally differentiated policy formulation, and infrastructure planning. |
| Key words: municipal solid waste machine learning SHAP interpretation cluster analysis solid waste management |
