Papua New Guinea’s Forests as an Engine of Economic Growth and Prosperity

Papua New Guinea holds the world’s third-largest extent of tropical forest. Between 2001 and 2024, it lost two million hectares of forest cover — a powerhouse of biodiversity quietly eroding under logging and agricultural pressure. This project asks what PNG’s forests are worth as an engine of economic growth, not just a source of timber.

The question

Papua New Guinea sits at the intersection of three facts that should be in tension but rarely are in policy debate. It hosts the third-largest tropical forest on the planet. It is, simultaneously, the world’s largest exporter of unprocessed logs. And its forest loss has drawn comparatively little attention from the international development and research community — between 2001 and 2024, two million hectares of forest were destroyed, equal to 5% of PNG’s tree cover in the year 2000, with associated emissions of roughly 1.5 gigatons of CO₂ equivalent.

The conventional development narrative treats forest conversion as the price of growth. The question this analysis takes up is different: what if PNG’s standing forests are worth more as a productive asset — supplying ecosystem services that underpin agriculture, fisheries, water security, coastal protection, and climate stability — than as a one-time timber harvest?

The approach

The project applies the Integrated Economic-Environmental Modeling framework linked with land-use, land-cover change and ecosystem services modeling (IEEM+ESM) to PNG. The IEEM+ESM workflow has been built for 32 countries to date and refined through more than a dozen peer-reviewed journal publications. The PNG application brings together a country-specific Computable General Equilibrium model, a spatial land-use model derived from the 2019 Copernicus global land-cover map, and a suite of ecosystem service models covering carbon storage, sediment delivery, nutrient delivery, water yield, pollination, and coastal vulnerability.

One methodological innovation in the PNG application is the integration of explicit flood modeling, which links forest cover to flood-hazard mitigation in a way that previous IEEM applications have not. This is particularly relevant for a country where rural livelihoods, infrastructure, and population centres are highly exposed to extreme rainfall and watershed disturbance.

What we will examine

The empirical analysis will contrast a business-as-usual projection — continued deforestation and degradation, combined with climate-induced productivity losses — against a policy and investment portfolio aligned with PNG’s Enhanced Nationally Determined Contributions and the country’s National Reforestation and Afforestation Program. The portfolio under consideration would position PNG’s natural capital base as an engine of economic growth: shifting the value proposition from harvest-and-export to standing-forest productivity, with explicit attention to rural livelihoods and poverty reduction.

Results will quantify, for PNG-specific conditions, the macroeconomic case for forest conservation and the distributional consequences for rural households. Findings will be spatially explicit, identifying where in PNG the highest-return interventions are located.

Why this matters

PNG’s forests are a significant store of global carbon and an irreplaceable reservoir of biodiversity. They are also the foundation of rural livelihoods for the majority of Papua New Guineans. Yet quantitative analysis that can speak to a Ministry of Finance — translating ecological value into the language of GDP, wealth, and fiscal returns — has been scarce. This project aims to provide exactly that translation and to do so at a scale and granularity that informs both national policy and the international community’s engagement with PNG.

The findings will be released as the analysis is completed and submitted for peer review. In the meantime, interested partners — government counterparts, multilateral institutions, and research collaborators — are welcome to get in touch to discuss the work.