Introduction

Most organisations now have risk maps. Far fewer have decisions that are clearly triggered by those maps.
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That gap matters because “awareness” does not reduce losses. Decisions do: where you build, what standard you apply, how you prioritise capital expenditure, and what you fund first when money is tight.

Case material that shows the approach working

A simple, repeatable method for geospatial analysis is:

Map → Metric → Threshold → Instrument → Accountability

• Map: hazard/exposure/vulnerability layers at the right spatial unit.
• Metric: expected annual loss, service disruption value, fiscal exposure, or consumer-bill impacts.
• Threshold: explicit triggers (e.g., above X loss per annum, below Y standard of protection).
• Instrument: funding prioritisation, planning rules, building codes, connection gating, or price signals.
• Accountability: who decides; who pays; how changes are monitored over time.
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The Thames Estuary 2100 approach is unusually explicit inlinking spatial segmentation to investment logic. It divides the estuary into23 policy units; policies then direct future flood management investment and provide a common foundation for partners’ short-, medium-, and long-termactivities.

The plan’s policy choices are described as being based on social, economic, and environmental factors, and explicitly used to target investment where it is most justified.

In the electricity system, connections reform is a parallel example: spatially-informed system needs and deliverability now determines queue priority, moving from “first come” to “first ready and needed”.

In the US context, building code adoption illustrates how spatial risk knowledge becomes a standard that materially reduces loss. FEMA’s Building Codes Save advisory summarises quantitative evidence that higher building code standards reduce damage and generate large annualised avoided losses.

How to make spatial intelligence mandate-capable

A decision-ready method is to treat “maps” as inputs into economic understandings, not as outputs. Essentially using the mapping process as a process in the analysis as opposed to just a visualisation of the data. At ESIMAP we transparently follow the guidance:

• Starting with the decision, capital expenditure prioritisation, planning consent, underwriting appetite, or resilience spend.
• Define the counterfactual (what happens without intervention); appraisal frameworks require this discipline.
• Convert spatial risk into a metric that corresponds to the objective (loss, disruption, fiscal exposure, consumer cost).
• Apply distributional and place-based considerations explicitly where they matter; this is part of formal public value judgement, not an optional narrative. 
• Stress-test against uncertainty (especially where hazards and adaptation pathways are non-stationary); development appraisal methods increasingly formalise “risk stress tests” as additions to standard economic analysis. 
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Decision-relevant implications

Resilience is no longer a narrative; it is a measurable requirement. Across government, finance, infrastructure, and emergency management, expectations are converging around one principle: if risk reductionis claimed, it must be defined, quantified, and spatially evidenced.

Public appraisal frameworks already exist. Financial reporting standards are tightening. Early warning systems are increasingly benchmarked. What is changing is not the intent, but the threshold forcredibility. Vague commitments and generic risk statements are being replacedby measurable triggers.

What level of exposure requires intervention? whatbaseline is used? what method underpins the estimate? and what uncertainty remains?

Spatial intelligence becomes economically meaningful when it translates into decision thresholds. Assets above defined hazard exposure levels require funded adaptation plans. Investments are monitored against explicit metrics. Preparedness is tracked geographically, not asserted politically.

The direction is clear. Resilience is moving from aspiration to operational requirement. Institutions that adapt fastest will not simply report impacts, they will evidence them, price them, and act on them with precision, ensuring the best outcome for resources and capital.

The ESIMAP “value proposition” is strongest when framed as a decision layer:

translate spatial layers into economic metrics that are credible in business cases and regulatory narratives;

define thresholds and triggers that make decisions auditable (why this place, why this year, why this spend);

produce outputs that cansurvive challenge (from investors, regulators, auditors, and stakeholders) because assumptions and uncertainty are explicit.

Sources

HM Treasury (2026) The Green Book (PDF):https://assets.publishing.service.gov.uk/media/6984ac702df808759a7bd740/The_Green_Book_2026.pdf

World Bank (2021) A Disaster and Climate Risk Stress TestMethodology (PDF):https://documents1.worldbank.org/curated/en/844361623398590980/pdf/A-Disaster-and-Climate-Risk-Stress-Test-Methodology.pdf

World Bank (2019) Lifelines: The Resilient InfrastructureOpportunity (PDF):https://documents1.worldbank.org/curated/en/111181560974989791/pdf/Lifelines-The-Resilient-Infrastructure-Opportunity.pdf

UK Government (2023) Thames Estuary 2100 collection:https://www.gov.uk/government/collections/thames-estuary-2100-te2100

IFRS Foundation (2023) IFRS S2 Climate-related Disclosures:https://www.ifrs.org/issued-standards/ifrs-sustainability-standards-navigator/ifrs-s2-climate-related-disclosures/

Financial Stability Board (2025) Climate roadmap update(PDF): https://www.fsb.org/uploads/P140725-2.pdf

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