Biomethane can decarbonise energy uses that direct electrification does not reach, among them high-temperature industrial heat, parts of the building stock and heavy transport, since it moves through gas infrastructure that already exists. Deployment across European member states remains uneven all the same, and the barriers usually blamed for that gap, economic, regulatory and technical, are documented across literatures that rarely engage one another. This paper reports a PRISMA 2020-compliant systematic review of the Scopus database, conducted to characterise those barriers and to establish how far plant siting figures among them, and proposes an artificial-intelligence-based decision-support architecture derived from the evidence assembled.

The search returned 301 records; 235 were excluded at title and abstract screening, 66 were sought for full-text retrieval, and 65 were included after one record could not be obtained. Each study was coded against ten barrier categories built from earlier biomethane and bioenergy reviews. Policy and regulatory barriers were the most frequently reported (56 of 65 studies), followed by economic and financial barriers (50), feedstock and logistics (34), grid and infrastructure (31), technical barriers (28), social acceptance (25) and spatial or siting barriers (21). Barriers were rarely reported in isolation. Some 94% of studies described three or more interacting categories, at a mean of 4.4 per study, and policy and economic barriers co-occurred in 45 of 65 studies (69%). The literature treats economic non-viability chiefly as a consequence of the policy environment rather than as an independent market fact.

Twenty-two studies (33.8%) engaged plant siting through a location-explicit method. Siting studies were no broader than the rest of the corpus, reporting a mean of 4.4 barrier categories in both groups, which rules out the simplest explanation for any link between siting and other barriers. Two contrasts stood out instead. Policy barriers were markedly less common among siting studies than elsewhere (14 of 21 against 42 of 44), consistent with a constraint that site selection cannot reach, while permitting barriers were more common among them (5 of 21 against 3 of 44). Read together, feedstock catchment, grid proximity, land eligibility and local acceptance are each settled or shifted by where a plant is placed, and all must be satisfied at the same location. Siting is accordingly best understood not as a discrete barrier but as the point at which the spatially expressed barriers converge on a single decision, one that current practice resolves in sequence rather than jointly.

Building on this evidence, the paper specifies a decision-support architecture intended to screen candidate sites before the detailed techno-economic assessment that current practice can only afford for a few locations. Each analytical module addresses a barrier domain identified in the review, feedstock and yield estimation, grid connection cost, eligibility and permitting masks, and techno-economic pre-screening, and is mapped to an established methodological precedent in the siting, optimisation or machine-learning literatures. An evidence-derived weighting of siting criteria is also proposed, combining each barrier's prevalence in the corpus with a reasoned assessment of how far site selection can resolve it. Feedstock catchment and grid proximity weigh most under this scheme, while national policy, the most frequently reported barrier overall, weighs least, since its resolution lies above the level of an individual site.

The architecture is offered as a specification for future calibration and validation rather than as a built or tested system. For a conference addressing business and economics, the contribution is a documented account of where in the deployment process the barriers to biomethane investment actually bind, and of what an AI-supported decision-support tool would need to do to relieve the subset of them that a choice of location can address.