Building a Complete Framework for Sustainable Contaminated Site Redevelopment

Across the world’s industrial cities, hundreds of thousands of former factories, gasworks, mines, and landfills sit idle—patches of land too contaminated to build on, yet too valuable to ignore. Now, a research team led by C. Mahammedi and M.K.S. Al-Mhdawi of Teesside University, working with Abroon Qazi of the American University of Sharjah and AM. Mahamadu of University College London, has built a comprehensive decision-making model designed to cut through the complexity that has long stalled the redevelopment of these sites. Published in the journal Environmental Management, the study offers what the authors describe as a structured roadmap for policymakers, developers, and planners trying to convert polluted liabilities into sustainable urban assets.

Contaminated sites—often referred to as brownfields—represent a paradox in modern urban planning. On one hand, redeveloping them is widely recognised as a pillar of sustainable development: it relieves pressure on greenfield land, curbs urban sprawl, and can inject new economic and social life into neglected districts. On the other hand, these sites carry deep technical uncertainty, hidden remediation costs, fragmented regulatory oversight, and a web of stakeholders with conflicting priorities. Previous research has catalogued these barriers and drivers individually, but the new study argues that treating them in isolation is precisely why so many redevelopment projects stall before ground is ever broken.

To build the new framework, the team began with an extensive literature review combined with direct consultations with experts in the field. From this process, they identified the key barriers, drivers, and strategies relevant to contaminated site redevelopment, organising them across regulatory, financial, and socio-economic dimensions. The barriers span financial obstacles such as uncertain remediation costs and unattractive return on investment; regulatory and policy hurdles including ambiguous liability rules and inconsistent planning frameworks; environmental and human wellbeing concerns tied to residual contamination; and social challenges such as negative public perception of formerly polluted land. The drivers, by contrast, include economic incentives, environmental benefits, supportive government policy, and social momentum, while the strategies range from financial and market-based incentives to policy enhancement, community engagement, better information access, technological advancement, and even tourism promotion.

The methodological backbone of the study is a structured questionnaire administered to 36 experts drawn from relevant professional and academic domains. These specialists were asked to rank the identified factors by importance, producing a rich dataset of expert judgements. Rather than simply averaging the responses, the researchers subjected the rankings to rigorous statistical scrutiny. They calculated Kendall’s coefficient of concordance (W), a non-parametric measure of how much agreement exists among multiple raters, and applied the Kruskal-Wallis H test to check whether differences in how groups of respondents ranked factors were statistically meaningful. This dual approach allowed the team to confirm that a genuine consensus had emerged, rather than a coincidental alignment of individual opinions.

With consensus established, the researchers then deployed the Voting Analytic Hierarchy Process, or VAHP, to convert the ranked expert preferences into a prioritised hierarchy of factors. The VAHP is a hybrid of classical multi-criteria decision analysis and group voting logic: instead of requiring experts to fill out pairwise comparison matrices, it derives priority weights directly from the frequency with which each factor receives a particular rank across the panel. The approach, first developed for supplier selection problems, is particularly well suited to situations where many decision-makers must weigh many alternatives, and it reduces the cognitive burden and inconsistency risks associated with traditional Analytic Hierarchy Process questionnaires. In the contaminated land context, this means the model can objectively order which barriers deserve the most urgent attention and which strategies offer the highest leverage.

The output is a comprehensive model that walks stakeholders through a systematic process: identifying the barriers specific to a given site and jurisdiction, mobilising the drivers already present in the local context, and selecting the strategies most likely to neutralise the obstacles. What distinguishes the framework, the authors argue, is its integration. Regulatory levers such as clearer liability allocation and streamlined permitting sit alongside financial instruments like tax incentives, grants, and public-private partnerships, all mapped against the socio-economic realities of the communities surrounding the site. Because the model is structured and transparent, it can be used by actors with very different technical backgrounds—from a municipal planner to a private investor—providing a common language that improves communication and collaboration across the entire project ecosystem.

The practical implications could be substantial. Brownfield redevelopment projects routinely collapse under the weight of uncertainty: remediation cost estimates can swing dramatically based on soil and groundwater conditions discovered mid-project, and the stigma of contamination depresses land value even after cleanup. Studies cited in the paper highlight how asymmetric information drives risk premia into brownfield investments, how financing gaps hamper affordable housing on remediated land, and how communities in disadvantaged neighbourhoods are sometimes bypassed by the benefits of redevelopment—or, conversely, displaced by gentrification that follows successful cleanup. A prioritised, transparent model does not eliminate these uncertainties, but it gives decision-makers a defensible basis for sequencing interventions and allocating scarce public funding where it will do the most good.

The study also arrives at a moment when the environmental stakes of land reuse are rising. Global attention on circular economy principles has intensified pressure to recycle land the same way materials are recycled, and researchers have documented unexpected ecological dividends of brownfields, from biodiversity refuges in post-industrial landscapes to the carbon savings of remediating and reusing existing urban land rather than paving countryside. Meanwhile, legacy contamination—from heavy metals in former industrial districts to leachate from old landfills—continues to pose risks to human health and ecosystems when sites are left unmanaged. The authors position their model as a tool to unlock the potential of underutilised land while ensuring environmental protection remains non-negotiable, facilitating socio-economic revitalisation that does not simply transfer pollution burdens to future generations.

It is worth noting that the framework does not prescribe a single remediation technology or a one-size-fits-all policy package. Instead, it functions as a decision architecture: the expert-derived priorities provide a starting point, but the model is designed to accommodate the tailoring of responses to individual sites. This flexibility addresses a common criticism of earlier brownfield frameworks, which critics argued were either too abstract to guide practice or too narrow to transfer across national contexts. By grounding the factor rankings in an international expert panel and validating agreement statistically, the authors have sought to build something general enough to travel, yet specific enough to act upon.

The research also connects to a broader wave of decision support innovation in contaminated land management. Recent work in the field has produced GIS-based screening tools, risk assessment systems for preliminary brownfield evaluation, and data-driven platforms that aggregate environmental information for redevelopment decisions. The new model complements these technical systems by addressing the human and institutional dimension—because, as the study makes clear, the fate of a contaminated site is decided less by soil chemistry than by whether regulators, financiers, developers, and residents can align around a shared plan. The authors report that the underlying survey data are available on request, and the work received no external funding.

For cities facing housing shortages, climate adaptation needs, and shrinking budgets, the message of the research is quietly optimistic: the barriers to brownfield redevelopment are real, but they are identifiable, rankable, and, with the right combination of drivers and strategies, surmountable. The Teesside-led team’s model offers a way to replace intuition and improvisation with evidence and structure—a shift that could determine whether the world’s industrial legacy landscapes remain liabilities or become the sustainable neighbourhoods of the next generation.