The Economic Footprint of Indonesian Palm Oil

Humble Beginnings

Elaeis guineensis, also known locally as Kelapa Sawit is arguably the most successful immigrant in Indonesian history. Native to West Africa, it arrived in the Dutch East Indies in the mid-19th century as four seedlings planted in the Bogor Botanical Gardens for scientific study.

From those four ancestors, palm oil cultivation spread until it defined the Indonesian landscape. Cultivation spread from the estates of Sumatra to the vast reaches of Papua, forming a unique symbiosis with human ambition. At the entrance of my office building is a quote by the poet Robert Hass: “All lands dream of becoming a forest.” It is a beautiful sentiment, but ironic when contrasted with the dream of many landowners in Indonesia’s lowlands: to turn their land into a palm plantation.

If you drive through the lowlands of Sumatra or Kalimantan, the horizon is rarely anything else but palm oil plantations. Today, Indonesian palm oil plantations already cover millions of hectares. An area that is larger than many European nations!

Conquering the Nation

My remote sensing model estimates approximately 15 million hectares of palm oil plantations in Indonesia (Figure 1). While some official estimates lean toward 16-17 million, my model is intentionally conservative; it specifically captures mature, healthy stands across both industrial estates and smallholder plots, filtering out areas of high uncertainty or early-stage growth that do not yet contribute to the national Gross Production Value.

To put that in perspective: considering standard plantation spacing, there are more than 2 billion palm oil trees alive in Indonesia right now. They vastly outnumber the human population. They are probably the most prevalent large organism in the country. Essentialy, they are the winner in the game of life.

But this dominance is not without consequence. The expansion of oil palm has been closely linked to large-scale deforestation, particularly in Sumatra and Kalimantan, where lowland forests have been systematically converted into monoculture landscapes. In many cases, this transformation involves peatland drainage, releasing significant amounts of stored carbon and increasing vulnerability to fire. What emerges is a system that is highly efficient economically, but one that comes at the cost of ecological complexity, biodiversity, and long-term landscape resilience.

Estimating Annual Gross Production Value of Crude Palm Oil (CPO)

We can map palm oil from space quite well. What we don’t see directly is its economic value. I wanted to estimate the actual Gross Production Value (GPV) of this landscape at a national level. Because we are dealing with a country as large as Indonesia, we cannot be exact. Instead, we use a Probabilistic Model. We admit what we do not know for sure some paramters, such as the specific yields or the 2026 price, and define them as ranges based on our best estimates. These are our priors. By running thousands of simulations, we account for the full spectrum of possible outcomes.

The Bayesian Formulation

To do this, we can’t consider all of palm plantation in Indonesia to be identical. We have to account for the reality that not all hectares are created equal. To simplify, we divide the palm plantations into three distinct management tiers:

1. Independent Smallholders: The frontier farmers with the highest variability.

2. Plasma Smallholders: Farmers supported by company partnerships.

3. Industrial Estates: The high-efficiency, high-yield engines of the industry.

To estimate the total value, each component is treated as uncertain rather than fixed. The national Gross Production Value (\(V\)) is the sum of the production from each management tier (\(i\)$), multiplied by the global price (\(P\)).Mathematically, the relationship for each tier is:

\[GPV_{i} = A \cdot s_{i} \cdot Y_{i} \cdot ER_{i} \cdot f_{i} \cdot P\]

Where:

• \(A\): Total national palm oil area (hectares).
• \(s_{i}\): The share of total area belonging to tier $i$ (Independent, Plasma, or Estate).
• \(Y_{i}\): Average Fresh Fruit Bunch (FFB) yield (tons/ha).
• \(ER_{i}\): Extraction Rate (the efficiency of turning fruit into CPO).
• \(f_{i}\): A management/spatial correction factor (accounting for tree health and age).
• \(P\): The market price of CPO (USD per ton).

By defining each of these as a probability distribution (e.g., \(Y_{i} \sim \text{Normal}(\mu, \sigma)\)), the model iterates through thousands of possible scenarios to find the most likely national total.

Parameters and Assumptions

The table below outlines the specific priors used in the 2025 simulation. These values are derived from a combination of BPS (Statistics Indonesia) historical data, industry benchmarks, and market trends.

Parameter	Management Tier	Prior Distribution / Range	Assumption Logic
Yield (\(Y\))	Independent	\(\text{Normal}(13, 4)\)	High variance due to varied seed quality.
	Plasma	\(\text{Normal}(17, 3)\)	Improved yields through company support.
	Industrial	\(\text{Normal}(23, 2)\)	High-input, consistent industrial management.
Extraction (\(ER\))	Independent	\(\text{Uniform}(0.18, 0.22)\)	Lower efficiency in smallholder milling links.
	Plasma	\(\text{Normal}(0.23, 0.01)\)	Standardized mill processing.
	Industrial	\(\text{Normal}(0.245, 0.01)\)	Optimized industrial-scale extraction.
Adjustment (\(f\))	All Tiers	\(\text{TruncatedNormal}\)	Corrects for non-productive/young trees. Derived empirically from the actual distribution of LANDSAT’s NDVI of palm oil plantations at national scale
Price (\(P\))	Global	\(\text{Lognormal}(\ln(950), 0.15)\)	Reflects CPO price volatility for 2025.

Figure 2 below visualize the priors for our model parameters. The tight distribution for Industrial Estates reflects a highly standardized, high-input machine designed for maximum output. Conversely, the wide spread of the Independent Smallholder prior captures the reality of a frontier economy, where factors like seed quality, infrastructure access, and local management create a massive range of performance. By defining these ranges, we trying to reduce bias by acknowledging the sturctural diversity of how different plam oil plantation is utilized.

Result

When we aggregate these hectares across the entire 15 million hectare footprint, we get the big picture. The model estimates a National Gross Production Value for 2026 with a mean of approximately $55 Billion USD (Figure 3). The 95% Confidence Interval suggests that even under conservative estimates, the value is unlikely to drop below $35 Billion, while a high yields and prices could push it toward $80 Billion. This is the economic weight of those ~2 billion trees.

Breaking the results down per hectare reveals three distinct economic profiles. As shown in Figure 4, independent smallholders generate around $2,200/ha/year, plasma $3,458, and industrial estates $5,315. These values serve as practical benchmarks when comparing the economic performance of oil palm against alternative land-use systems.

Closing: The $55 Billion Baseline

A system generating ~$55 billion a year is not easily replaced.

By mapping these values, we now understands what we gain by sacrificing our biodiversity and optimizing a unique, complex and diverse landscape for a single species at scale. Any alternative land use competing with palm oil must either match this level of economic output, or clearly justify why it falls short.

With this baseline in place, the real work begins: testing whether more sustainable, less extractive land-use systems can compete under current market conditions.