Applying an integrated DEMATEL-ANP methodology to a sugar–paper–spirit–cement industrial ecosystem, the study finds that demand and supply risk ranks as the highest-priority external risk, infrastructure risk as the highest-priority internal risk, and buyer–supplier contracts as the most effective mitigation strategy. The work, published in Cleaner Engineering and Technology, addresses a gap the authors identify in earlier research, which tended to assess risks in industrial symbiosis networks and circular supply chains separately and largely neglected the inter-industry dependencies that define these systems.
An industrial symbiosis network works by linking traditionally separate industries in collaborative exchange. In the network studied here, sugarcane is converted to sugar and jaggery, generating two key by-products: bagasse, a fibrous residue, and molasses. Molasses is supplied to the spirit manufacturing industry as raw material for fermentation and distillation, while bagasse travels to the paper industry alongside softwood from local plantations for pulp-making. The pulp is cooked with shredded waste paper from a recycling plant in digesters heated by steam from coal-fired boilers. Those boilers produce surplus steam, reused within the plant for power generation and process heating, and fly ash, which is supplied to the cement industry as a raw material. Even wastewater finds a second life: after treatment, it is reused for sugarcane irrigation, and potash-rich kiln dust from the cement plant returns to the sugarcane fields as a soil amendment.
The researchers selected this ecosystem because of its tightly connected, bio-based operations and its exposure to regulatory pressure, since its energy-intensive, waste-producing industries face incentives to embrace circular economy practices. The network is also highly integrated, meaning disruptions in one industry trickle down across all the industries in the network—a characteristic that makes risk analysis particularly consequential.
To build the framework, the team first conducted a PRISMA-guided literature review, searching Google Scholar, Scopus and Web of Science for publications from 2017 to 2026. From an initial pool of 230 peer-reviewed references plus 12 additional references, 122 key publications were retained. Drawing on this literature and on discussions with a focus group of experts, the researchers identified an initial set of twelve risk categories, which the experts refined: supply and demand risks were merged into a single category, technology risk was folded into operational risk, and weather-related risk was renamed climate risk. The final model contains ten risk categories, five external and five internal, classified using resource-based view and institutional theory perspectives.
External risks include environmental and social risk, regulatory risk, climate risk, financial risk, and demand and supply risk. Internal risks cover operational, quality, infrastructure, partnering and information-sharing risks. The distinction matters: internal risks refer to elements within the operational parameters of individual firms and their bilateral interactions with symbiotic partners, such as technical compatibility and process synchronization, while external risks lie largely outside the administrative control of individual firms, including regulatory requirements, environmental conditions and the demand and supply of by-product output.
The expert panel comprised four specialists drawn from the industries in the network: a pulp division manager with twelve years of experience, a cement plant in-charge with thirteen years, a sugar industry operations head with twenty-one years, and an academic environmental expert with twelve years in sustainable supply chain planning. Each expert was interviewed in person to prevent non-response bias. The authors note that the panel size is consistent with previous DEMATEL and ANP studies, which employed three to six experts, and that these structured multi-criteria decision-making approaches depend on expert judgment rather than statistically representative samples. Notably, the spirit manufacturing sector was not represented; formal invitations were extended but participation could not be secured, although the authors argue the sector acts mainly as a receiver of by-products and does not generate a downstream by-product flowing back into the circular loop.
Methodologically, DEMATEL was used to analyze the inner dependencies within each risk cluster. Experts rated the influence of one criterion on another using a six-level scale from zero to five, producing direct-relation matrices that were normalized and converted into total-relation matrices. From the row and column sums, the team calculated prominence and net cause/effect values for each risk. Infrastructure risk emerged as the strongest causal driver in the entire model, with a net cause value of +2.611, reflecting underdeveloped transportation and storage infrastructure. Climate risk was the strongest cause within the external cluster at +1.479, while financial risk was the strongest external effect at −1.899 and partnering risk the strongest internal effect at −2.726. Demand and supply risk registered as a dominant external effect at −1.223.
The ANP component then evaluated the outer dependencies among clusters—goals, external risks, internal risks and mitigation strategies—using pairwise comparison matrices on a nine-point scale, with consistency ratios verified below the 0.1 acceptance threshold. Combining the DEMATEL and ANP outputs into a supermatrix raised to successive powers until convergence, the team extracted final priority weights. Demand and supply risk ranked first among external risks, followed by financial, regulatory, climate and environmental-social risks. Infrastructure risk led the internal cluster, ahead of operational, partnering, information-sharing and quality risks. Among five mitigation strategies—vertical and horizontal integration, digital technologies, buyer–supplier contracts, collaborative logistics networks with common infrastructure, and a common liaison policy with optimal pricing—buyer–supplier contracts ranked first, collaborative logistics second, and the liaison and pricing policy third.
The causal structure explains this ranking. Buyer–supplier contracts formalize demand commitments and information-sharing obligations, directly targeting the network’s dominant effect variables: demand and supply, financial, partnering and information-sharing risks. Collaborative logistics and shared infrastructure target the dominant structural cause, infrastructure risk, but require collective, longer-horizon investment across the symbiotic network. The two top strategies are therefore complementary, with contracts offering near-term relief while shared infrastructure addresses structural issues. In practical terms, the authors suggest that practitioners implement buyer–supplier contracts by mapping critical by-product flows and single points of dependency, negotiating agreements that fix supply commitments, quality specifications and pricing bands, and embedding information-sharing clauses with digital tracking where possible. They also caution that formalizing agreements between firms of different sizes and power carries high initial cost and effort, and that companies may resist sharing operational data.
A sensitivity analysis tested the robustness of the results by varying four selected weights by 10 and 20 percent in both directions across sixteen cases. The rankings remained unchanged in fifteen of the sixteen cases; the only change occurred when the infrastructure risk weight was decreased by 20 percent, causing infrastructure and operational risks to exchange first and second positions within the internal cluster. The priority order of the mitigation strategies was identical across all sixteen cases, indicating that the recommended mitigation priorities do not depend on the exact weights assigned to the risks. The authors acknowledge limitations: the outcomes rest on the subjective judgments of four experts, only one symbiotic network was studied so rankings may vary elsewhere, the English-language literature review may carry publication bias, and no economic evaluation such as a cost–benefit analysis was provided. They also note the framework’s dependence on expert opinion, which introduces the possibility of human subjectivity. Even so, the study offers a structured template—identify risks, classify them, map their causal dependencies, and prioritize mitigation strategies—that companies and government bodies in eco-industrial parks can adapt when steering their own transitions toward circular supply chains.
Subject of Research: Risk identification and mitigation prioritization in a circular industrial symbiosis network using an integrated DEMATEL-ANP framework
Article Title: Enhancing clean technologies through risk mitigation in symbiotic industrial networks: A circular economy approach
Article References: Kandasamy, J., Muralidharan, S., Mishra, R., Vimal, K. E. K., Sivakumar, K., & Jagtap, S. (2026). Enhancing clean technologies through risk mitigation in symbiotic industrial networks: A circular economy approach. Cleaner Engineering and Technology, 35, Article 101345. https://doi.org/10.1016/j.clet.2026.101345
Image Credits: AI Generated
DOI: 10.1016/j.clet.2026.101345
Keywords: industrial symbiosis, circular economy, circular supply chain, DEMATEL, ANP, risk mitigation, buyer-supplier contracts, infrastructure risk, multi-criteria decision-making, sugar-paper-spirit-cement network, supply chain risks, eco-industrial parks
News Source: Denise Maddox. (October 29, 2026). Risk Mitigation Framework Prioritizes Threats in Circular Industrial Symbiosis Networks. Scienmag.
