Operationalizing nature recovery to market outcomes

A prosperous blue economy in coastal areas requires operational models that clearly link ecological outcomes to economic returns. These models can support investment, planning, and the growth of non-extractive economies that benefit both coastal communities and the environment. Here, we focus on an Essential Ocean Variable—fish biomass—because it is a globally standardized and monitored variable with proven links to recovery and management. We connect this variable to a key economic output: scuba diving ecotourism, which is a critical component of many coastal economies. To link the two, we built a dual-model heuristic: (i) a spatial hedonic model across diving sites distributed globally relating standardized per-tank prices to reef fish biomass, and (ii) a temporal model linking fish biomass dynamics in Cabo Pulmo National Park to visitation rates. The model shows that a 10% increase in fish biomass is associated with a ∼ 3.46% increase in diving price (R 2 = 0.335, p = 0.0016). Scenario analysis assumes a degraded baseline (300 kg/ha) and recovery trajectories toward near-pristine levels (∼8000 kg/ha) and applies the global log-log price-biomass elasticity to project revenue gains of ∼30–252%, increasing pooled annual revenue by about $6.26 million (approx $1.25 million per operator versus $0.35 million in degraded conditions). This heuristic converts ecological change into finance-ready outputs—price changes, revenue projections, return on investment (ROI), and payback timelines—offering a tool to design and fund effective marine protection while aligning tourism markets with ecosystem recovery.

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Publication Details

Journal
Ecological Economics
Published
2026-01-21
DOI
https://doi.org/10.1016/j.ecolecon.2026.108934
Primary Topic
Coastal and Marine Management
Type
article
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article

Operationalizing nature recovery to market outcomes

Fabio Favoretto, Octavio Aburto Oropeza, Matthew Forrest
Ecological Economics
Coastal and Marine Management
article

Operationalizing nature recovery to market outcomes

Fabio Favoretto, Octavio Aburto Oropeza, Matthew Forrest
article en

Abstract

A prosperous blue economy in coastal areas requires operational models that clearly link ecological outcomes to economic returns. These models can support investment, planning, and the growth of non-extractive economies that benefit both coastal communities and the environment. Here, we focus on an Essential Ocean Variable—fish biomass—because it is a globally standardized and monitored variable with proven links to recovery and management. We connect this variable to a key economic output: scuba diving ecotourism, which is a critical component of many coastal economies. To link the two, we built a dual-model heuristic: (i) a spatial hedonic model across diving sites distributed globally relating standardized per-tank prices to reef fish biomass, and (ii) a temporal model linking fish biomass dynamics in Cabo Pulmo National Park to visitation rates. The model shows that a 10% increase in fish biomass is associated with a ∼ 3.46% increase in diving price (R 2 = 0.335, p = 0.0016). Scenario analysis assumes a degraded baseline (300 kg/ha) and recovery trajectories toward near-pristine levels (∼8000 kg/ha) and applies the global log-log price-biomass elasticity to project revenue gains of ∼30–252%, increasing pooled annual revenue by about $6.26 million (approx $1.25 million per operator versus $0.35 million in degraded conditions). This heuristic converts ecological change into finance-ready outputs—price changes, revenue projections, return on investment (ROI), and payback timelines—offering a tool to design and fund effective marine protection while aligning tourism markets with ecosystem recovery.

Ecological EconomicsVol. 243
Scripps Institution of Oceanography (US), University of California San Diego (US)
Life below water
Openalex Percentile: Top 86%
Coastal and Marine Management
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Operationalizing nature recovery to market outcomes — Fabio Favoretto, Octavio Aburto Oropeza, et al. · Ecological Economics (2026) | TGRS Research Map | TGRS