Evaluating the impact of soft management policies on construction and demolition waste recycling efficiency: A hybrid simulation-machine learning approach

IF 11.2 1区 社会学 Q1 ENVIRONMENTAL STUDIES
Zhikun Ding , Xinping Wen , Yue Teng , Huanyu Wu
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引用次数: 0

Abstract

Construction and demolition waste (CDW) recycling plays a critical role in sustainable development, yet the sector faces a low recycling rate and ineffective management practices. While current CDW recycling management predominantly employs government-led rigid measures, the potential of soft management approaches remains critically underexplored. To address this, this study evaluates the impact of soft management policies and identifies optimal strategies through an empirical study. An agent-based model embedded with back-propagation neural networks was innovatively developed and validated using data from 1005 residential projects in Shenzhen, China. Results reveal that soft management policies significantly enhance recycling rates and generate greater environmental, economic, and social benefits compared to rigid policies. Among soft policies, guidance policy performs best, increasing recycling rates by 14.13 %, followed by voluntary (7.24 %) and incentive (4.32 %) policies, while mandatory policy shows minimal improvement (0.24 %). However, a hybrid policy combining soft and rigid measures delivers the highest benefits, nearly three times those of the baseline policy. This study provides empirical support for integrating system simulation and machine learning to address CDW recycling management. More importantly, these findings advance scholarly understanding of the effects of soft management on CDW recycling and offer valuable insights for governments to refine CDW recycling policies, promoting a more sustainable future in the built environment.
评估软管理政策对建筑和拆除垃圾回收效率的影响:一种混合模拟-机器学习方法
拆建废物循环再造在可持续发展中扮演重要角色,但拆建废物循环再造率低,管理不善。虽然目前的CDW回收管理主要采用政府主导的刚性措施,但软管理方法的潜力仍未得到充分开发。为了解决这一问题,本研究通过实证研究评估了软管理政策的影响,并确定了最优策略。利用中国深圳1005个住宅项目的数据,创新性地开发并验证了嵌入反向传播神经网络的基于智能体的模型。结果表明,与刚性政策相比,软管理政策显著提高了回收率,并产生了更大的环境、经济和社会效益。在软政策中,引导政策表现最好,回收率提高了14.13%,其次是自愿政策(7.24%)和激励政策(4.32%),而强制性政策的改善幅度最小(0.24%)。然而,软硬结合的混合政策带来了最高的效益,几乎是基准政策的三倍。本研究为系统模拟与机器学习相结合解决CDW回收管理提供了实证支持。更重要的是,这些发现促进了对软管理对CDW回收影响的学术理解,并为政府完善CDW回收政策提供了有价值的见解,促进了建筑环境中更可持续的未来。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
CiteScore
12.60
自引率
10.10%
发文量
200
审稿时长
33 days
期刊介绍: Environmental Impact Assessment Review is an interdisciplinary journal that serves a global audience of practitioners, policymakers, and academics involved in assessing the environmental impact of policies, projects, processes, and products. The journal focuses on innovative theory and practice in environmental impact assessment (EIA). Papers are expected to present innovative ideas, be topical, and coherent. The journal emphasizes concepts, methods, techniques, approaches, and systems related to EIA theory and practice.
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