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Single traditional multi-criteria decision-making (STMCDM) methods are weak in evaluating consistent criteria weights for industrial site selection. As a result, unfavourable industrial locations could not attract new industries, giving rise to brownfields with high carbon emissions. To solve the constraints of STMCDM, integrated multicriteria decision-making (IMCDM) method was developed. The interaction of the analytic network process (ANP) and the triangular fuzzy numbers of the fuzzy-analytic hierarchy process (F-AHP) were integrated to create the Network Fuzzy-hierarchy Analytic Process (NFh-AP). Tanjung Langsat Industrial Area spatial criterion data for 2009 and 2019 were collected using GIS to test for the weighting consistency of the ANP, F-AHP, and NFh-AP. The Euclidean distance, raster layer reclassification, and land use and land cover data collected from PLANMalaysia were prepared. The weights and spatial data were submitted to weighted overlay analyses by the GIS. With the 2009 dataset, the ANP, F-AHP, and NFh-AP identified all water bodies as suitable EIP sites, highlighting the constraints of the methods with sparse criteria. The 2019 data with ANP, F-AHP, and NFh-AP identified 2 %, 3 %, and 25 % as the best sites with well-defined boundaries. Due to the poor best suitable areas produced, even with dominant criteria, single approaches have inconsistencies in criterion weighting. The NFh-AP algorithm's weighting consistency is due to networking and fuzzy logic interaction, and it is presented to assist evaluate consistent criteria weights and a simple modelling approach for brownfield-EIP (BF-EIP) site selection. Using spatial criteria for BF-EIP site selection collected elsewhere, the NFh-AP consistency can be tested. [ABSTRACT FROM AUTHOR]

Inconsistencies of the single multi-criteria decision making (SMCD) methods in criteria weight assessment make them unreliable and have led to the wrong siting of industrial parks, which are often abandoned as brownfields that emit GHG. Eco-industrial parks (EIPs) are replacing brownfields but require robust decision-making tools to weigh and rank suitable locations for industry clusters' synergies. Integrated multi-criteria decision making (IMCDM) to address the weaknesses and strengthen the advantages of SMCDM methods, and a model to overlay criteria weights and spatial data easily and accurately were developed. The spatial criteria data for 2009 and 2019 from Tanjung Langsat Industrial Area were collected and prepared by GIS to test the SMCDM and IMCMD consistency weighting and the model resilience. The SMCDM (AHP, ANP and F-AHP) and the IMCDM weights with the 2009 criteria data identified the entire water bodies around the brownfield as suitable sites, making them inconsistent. The 2019 data with the SMCDM weights identified tiny sites as best, also making them inconsistent. The integrated hierarchy network fuzzy analytic process (HN-FAP) and the hierarchy network analytic process (HNAP) with the 2019 criteria data identified part of the water bodies as suitable making it inconsistent. The hierarchy fuzzy analytic process (H-FAP) and the network fuzzy hierarchy analytic process (NFh-AP) identified larger suitable sites without overlaps making them consistent algorithms. The H-FAP and NFh-AP procedures eliminate the weaknesses and consolidate the strengths, giving optimally consistent criteria weights. The two algorithms' consistency and the model efficiency can use different criteria weights and spatial data inputs from elsewhere for 4IR-driven EIP modelling to help brownfield-EIP stakeholders. Future research would address the reverse ranking of MCDM methods when alternatives are added or removed. • Single choice-making criteria assessment inconsistency led to wrong industrial site. • Unfavourable industrial sites have been abandoned to brownfields that emit GHG. • EIP is replacing brownfields for industry clusters, synergy and cleaner production. • Combined decision-making consistent tools and an overlay analysis model are created. • The integrated tools show clear suitable EIP sites using concentrated spatial data. [ABSTRACT FROM AUTHOR]

Globally, most industrial parks that have not gone through a proper site selection process have been neglected or underutilised and such active sites tend to burn a lot of fuels and generate greenhouse gases. Technology must be used to conduct on-site investigations of the spatial and non-spatial factors of industrial parks, especially the emerging Eco-Industrial Park (EIP). The paper seeks to appraise the global application, capability and acceptability of Geographic Information System (GIS) and Multi-Criteria Decision-Making (MCDM) tools in the EIP sites selection and planning. Also, to identify the benefits of mixing MCDM tools when integrating with GIS. There have been some reviews on the use of GIS and MCDM methods, but so far, there is none on the role of GIS and MCDM methods in EIP site selection. Geospatial and MCDM tools can be used to carefully select EIP sites, where materials and energy can be shared to achieve cleaner production, reduce environmental pollution and promote sustainable development. To understand the usage of these tools and their role in the EIP site selection process, a critical review of twenty-two papers published from 2007 to 2020 indexed by Google Scholar, Springer, IEEE Xplore and Web of Science have been developed for the first time. The articles are classified based on country/region, journal of publication, year and citations of publication, the authors and technologies applied. The results show that most studies on EIP site selection come from Iran and China. The Journal of Environmental Monitoring and Assessment has the highest number of articles published at 28%. The highest number of articles was published in 2014 with 312 citations. GIS and Analytic Hierarchy Process (AHP) topped the technologies used, accounting for 42% and 24% respectively, and hybrid tools 34%. As categorised by criteria content, there was an overlap of criteria in most journals. Though, water bodies, utilities, infrastructure, and natural characteristics were prominent. Renewable energy resources were not given attention by most researchers, and risk assessment was absent from the site selection literature. A blend of traditional MCDM tools constitutes the state-of-the-art tools, and when these are combined with GIS in EIP site surveys, they execute to obtain actual results. Future EIP site selection that is integrated with risk assessment is envisioned to be industry 4.0 (4IR)-driven through a combination of GIS and artificial intelligence, and the recently developed MCDM hybrid modules. [Display omitted] • Poor site selection has led to industrial park being neglected or underutilised. • Abandoned/underutilised active industrial parks can burn fuels to cause GHG. • First time of critical review of role of GIS and MCDM tools in EIP site selection. • GIS and AHP currently mainly used for large-scale EIP site criteria selection. • EIP site choice risk study using GIS, AI and MCDM hybrid can sustain industry 4.0 [ABSTRACT FROM AUTHOR]