Landscape Character Assessment (LCA) in Historic Coal Mining Settings for Landscape Conservation: A Systematic Review

被引:0
|
作者
Liu, Qi [1 ]
Abidin, Nor Arbina Zainal [1 ]
Maliki, Nor Zarifah [1 ]
Zhang, Kailai [2 ]
Li, Zhi [3 ,4 ]
Liu, Sha [1 ]
机构
[1] Univ Sains Malaysia, Sch Housing Bldg & Planning, Main Campus, Gelugor 11700, Penang, Malaysia
[2] Univ Sains Malaysia, Ctr Policy Res, Main Campus, Gelugor 11700, Penang, Malaysia
[3] Guilin Univ Aerosp Technol, Sch Media & Art Design, Guilin 541004, Peoples R China
[4] Univ Putra Malaysia, Fac Educ Studies, Serdang 43400, Selangor, Malaysia
关键词
landscape character assessment (LCA); heritage; mining; coal mining; landscape conservation; HERITAGE;
D O I
10.3390/land13091396
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
Landscape character assessment (LCA) is a crucial tool for conserving an area's unique character. However, in our literature review, we found no data linking LCA to historic coal mining settings. This systematic review explores the ways in which the landscape character assessment (LCA) methodology has been applied, as well as the factors that influence it, in the conservation of historic coal mine landscapes. It focuses on three areas: analyzing the ways in which LCA has been applied in landscape conservation, proposing recommendations for the application of LCA in historic coal mine setting landscapes, and summarizing the factors that influence LCA in landscape conservation in historic coal mine settings. Methods: This study used the Meta-Analyses (PRISMA) method to perform the systematic review. The whole review was selected from 2030 potential articles; a total of 21 articles were included. Results: This study demonstrates that the LCA approach can be operationalized in the conservation of environmental landscapes in historic coal mines by combining cluster analysis and multi-scale assessment and incorporating other theories. The quality of the results can be affected by factors such as the accuracy and completeness of the data and the complexity and tractability of the model. Conclusions: Future research should focus on improving the data capture technology, model complexity, and design of actionable models. Additionally, we recommend the strategies of enhancing stakeholder engagement and raising public awareness.
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页数:20
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