The Origins and Control of Forest Fires in the Tropics

This paper studies the economics of illegal tropical forest fires in Indonesia, framed as a modern counterpart to Pigou’s canonical externality example of sparks from railway engines. The central research question is whether private firms adjust their fire-setting behavior depending on the degree to which the costs of fire spread fall on themselves versus others, and what enforcement architecture shapes that adjustment.

The empirical setting is Indonesia’s national forest estate, where palm oil and wood fiber concession holders use fire as a cheap land-clearance method — burning primary forest costs 44–70% less than mechanical clearance — despite the practice being illegal. The paper assembles a novel dataset of 107,334 fires across Indonesia’s major forested islands from October 2000 to January 2016, constructed from NASA MODIS daily satellite hotspot data (1 km resolution, four flyovers per day). Fire ignitions and spread paths are traced by linking contiguous pixels burning on adjacent days. This fire data is merged with geocoded concession boundaries (logging, palm oil, wood fiber), land-use classifications (protected forest, unleased productive forest, areas outside the forest estate), annual deforestation data from Hansen et al. (2013) at 30 m resolution, daily wind speed data from NOAA NCEP-DOE Reanalysis 2 interpolated to each 1 km pixel, and data on firms investigated by the Indonesian government following the 2015 fires. The main analytical sample focuses on the 39,077 fires started inside wood fiber and palm oil concessions.

The paper’s identification strategy exploits two intersecting sources of variation: (1) temporal and spatial variation in monthly wind speed, which predicts the probability and extent of fire spread — a one-standard-deviation increase in wind speed (approximately 5 km/hr) increases fire spread area by 287%; and (2) cross-sectional variation in the land-type composition of the area surrounding each ignition pixel, which determines whether spread costs would fall on the fire-setter or on others. The interaction of these two factors identifies whether firms are more cautious about igniting fires on windy days when surrounding land is their own versus when it belongs to others.

Three main findings emerge. First, fires are systematically human-caused and linked to industrial land clearance. Fires are eight times more likely per hectare in oil palm and wood fiber concessions than in logging concessions. Completely deforesting a 1 km pixel increases the probability of fire ignition in that pixel in the subsequent year by 279%, and this effect reverses in the year after (two years post-deforestation), ruling out natural flammability as the explanation and confirming a deliberate slash-and-burn cycle. Fire use following deforestation falls by approximately 38% in oil palm concessions during district election years, consistent with tighter enforcement when political incentives favor suppression.

Second, firms partially internalize the externalities from fire-setting. They are significantly less likely to set fires on windy days when surrounding pixels belong to their own concession rather than to others. A buffer zone entirely owned by the same concession holder reduces ignitions by 8–25% at mean wind speed, and by 22–61% at the 95th-percentile wind speed. However, firms treat neighboring concession land and unleased productive forest similarly — suggesting Coasian bargaining between concession holders is not occurring.

Third, the government’s enforcement pattern shapes firm behavior. Using data on firms investigated after the 2015 fires, the paper shows the government disproportionately investigates firms whose fires burned protected areas or high-population-density land, but not those whose fires damaged other private concessions. The relative weights firms place on different land types when deciding whether to ignite fires align closely with this government punishment function, consistent with firms responding to implicit Pigouvian incentives.

Counterfactual simulations show that broadening enforcement to treat all land types as the government currently treats populated areas would reduce fires by 80%; treating all land like protected forest would reduce fires by 67%. By contrast, fully Coasian property-rights solutions yield only 14% reductions, and tort reform allowing concession holders to recover damages from neighbors yields only 6%.

Q: What is the core externality problem studied in this paper? A: Firms use fire as a cheap land-clearance method, but once set, fires risk spreading beyond the igniter’s own concession onto land owned by others, creating an uncompensated externality. The decision to use fire rather than mechanical clearance is de facto a decision to impose this spread risk on third parties. The paper asks whether firms adjust this decision depending on the extent to which spread costs fall on themselves versus others, and whether government enforcement shapes that adjustment.

Q: Why is Indonesia the empirical setting? A: Indonesia holds a large share of the world’s tropical forests and is among the countries most affected by illegal land-clearing fires. The 2015 Indonesian fires alone released approximately 400 megatons of CO2 equivalent, at their peak emitting more daily greenhouse gases than all US economic activity, and caused an estimated 100,000 excess deaths across Indonesia, Malaysia, and Singapore. The palm oil industry in Indonesia and Malaysia, where fire is used extensively, accounted for 4.7% of global CO2 emissions from 1986 to 2016.

Q: How are fire ignitions and spread identified in the data? A: The paper starts from NASA MODIS daily hotspot data at 1 km resolution from October 2000 to January 2016. An iterative procedure assigns contiguous pixels burning on adjacent days to the same fire event, with a 1-pixel buffer allowing for spread detection. This yields 176,855 total fires across Indonesia, of which 107,334 remain after restricting to the major forested islands and the forest estate. The procedure may understate single-day spread since pixels burning on the same day are classified as part of the ignition area rather than spread.

Q: What fraction of fires spread beyond their ignition area, and how much of the spread falls on outsiders? A: 87% of fires burn for only one day and 89% do not spread beyond their initial ignition area. However, the largest fire in the data spread to cover 466 times its initial area, and the largest single fire burned 764 km2. Across all multi-day fires started inside concessions, 32% of the total land burned outside the initial ignition area is outside the concession where the fire began, quantifying the scale of the local externality.

Q: How is wind speed used as an identification strategy? A: Wind speed provides temporal and spatial variation in the probability that a fire will spread. A one-standard-deviation increase in wind speed (approximately 5 km/hr) increases the extent of fire spread by 287%. Because wind varies month to month and across space, while the composition of surrounding land types is fixed in the cross-section, the interaction of wind speed with surrounding land type identifies whether firms are more cautious about igniting fires when spread risk is high and spread costs would fall on their own land versus others’ land.

Q: What is the main result on firms’ internalization of fire spread externalities? A: Firms are significantly less likely to start fires on windy days when a larger share of the surrounding buffer zone belongs to their own concession. One additional buffer pixel in one’s own land decreases ignitions by 0.2–0.7%. A buffer zone entirely owned by the same concession holder reduces ignitions by 8–25% at mean wind speed, and by 22–61% at the 95th-percentile wind speed. This demonstrates that firms take fire spread risk into account when it threatens their own assets, but discount it when spread would damage others’ land.

Q: Do firms treat different types of neighboring land differently? A: Yes. The benchmark category is unleased productive forest, which has the weakest property rights and receives the least de facto government protection. Relative to this benchmark, firms are more cautious about fire spread toward protected forest (national parks and watershed areas) and toward land outside the forest estate (typically villages and smallholders). One additional buffer pixel in protected forest versus unleased productive forest decreases ignitions by 0.9% at mean wind speed and 2.7% at the 95th-percentile wind speed; the deterrent for land outside the forest estate is even stronger at 1.6% and 4.6%, respectively. Firms treat other firms’ concession land similarly to unleased productive forest, suggesting no effective private enforcement between concession holders.

Q: What evidence shows fires are tied to intentional land clearance rather than natural ignition? A: Fires are eight times more likely per hectare in oil palm and wood fiber concessions than in logging concessions, consistent with clear-cutting versus selective logging. Completely deforesting a 1 km pixel increases fire probability in that pixel in the subsequent year by 279%. Crucially, the effect reverses in the second year after deforestation — the pixel becomes less likely to burn than before — which rules out natural flammability as the mechanism and confirms deliberate slash-and-burn timing.

Q: What does the electoral cycle evidence show about government enforcement? A: Fires following deforestation fall by approximately 38% in oil palm concessions during district election years relative to the year prior to an election, and bounce back to pre-election levels in the year after. The decline is confined to productive forest zones where conversion is occurring; no electoral cycle appears in protected areas where conversion is already prohibited. This indicates that enforcement is tightened when political incentives are strong, and confirms that these fires are set intentionally and are responsive to government pressure.

Q: How is the government’s de facto punishment function estimated? A: The paper uses data on firms investigated by the Indonesian Ministry of Forestry following the 2015 fires, matching investigated firms (identified only by initials in the published list) to concession-holder names. A logistic regression of investigation probability on the land-type outcomes of a firm’s fires — conditional on total area burned — shows the government is substantially more likely to investigate firms whose fires burned protected areas or high-population-density land, but does not differentially investigate cases where fire damage is largely confined to other private concessions.

Q: How closely do firm behavior and government enforcement weights align? A: The relative weights across land types that the government applies in its investigation decisions correspond closely to the relative weights firms apply when deciding whether to ignite fires on windy days. Firms are most deterred by spread risk toward protected forest and populated areas outside the forest estate — the same categories the government prioritizes. Firms are least deterred by spread toward unleased productive forest and other private concessions — the categories the government largely ignores. This alignment is consistent with firms responding to Pigouvian-style implicit incentives generated by the government’s enforcement pattern.

Q: What do the counterfactuals reveal about policy effectiveness? A: Fully Coasian property-rights reform — where firms treat all surrounding land as their own — would reduce fires by only 14%. Tort reform enabling concession holders to recover damages from neighbors (treating neighboring concessions as own land) would reduce fires by only 6%. By contrast, uniform enforcement raising deterrence to the level currently applied to populated areas would reduce fires by 80%; applying the level currently applied to protected forest would reduce fires by 67%. An enforcement regime that perfectly prevented all fire spread outside the igniting concession would reduce area burned by only 23%; preventing spread into protected and populated areas alone would yield only a 2% reduction.

Q: What do the benefit-cost ratios for fires look like? A: The estimated external damages from the 1997/1998 Indonesian fires range from 1,286 to 6,074 USD per hectare burned (2020 USD). The average private benefit from using fire rather than mechanical clearance — accounting for fertilizers and other costs — averages approximately 52 USD per hectare (2020 USD). Benefit-cost ratios of 0.008 to 0.04 lie well below 1, indicating that the social damages from fires vastly exceed the private benefits, even though the government currently deters only the most costly categories of fire.

Q: Why do Coasian private solutions perform poorly in this setting? A: Coasian bargaining between concession holders would require them to reach agreements to bring fire use to a locally efficient level without government intervention. The evidence shows firms treat other concession holders’ land essentially the same as unprotected unleased productive forest, implying that no such bargains are being struck. The counterfactual analysis confirms this: even a fully-Coasian outcome where every surrounding pixel is treated as own land would reduce fires by only 14%, because the bulk of fires occur when ignition costs to the firm’s own land are low regardless of wind speed.

Q: What is the primary policy implication? A: The most effective lever for reducing fires is not preventing spread after the fact, but rather deterring ignition in the first place by extending the enforcement regime uniformly across all land types. If firms were induced to treat all surrounding land with the same caution they currently apply toward populated areas — through broader and stronger penalties — fires would fall by 80%. This is substantially more effective than property-rights reforms, tort reforms, or targeted spread-prevention measures focused only on protected and populated areas.

Externality (fire spread): In this paper’s usage, the cost imposed on third parties when a fire ignited inside one concession spreads to land owned by others. The externality is quantified as the share of area burned outside the igniting concession (32% of multi-day fire spread in the data) and the ratio of external damages (1,286–6,074 USD/ha) to private benefits (52 USD/ha) from using fire rather than mechanical clearance.

Slash-and-burn (industrial scale): The two-stage land-clearance practice where valuable timber is first harvested (deforestation) and the remaining vegetation is then burned to prepare land for plantation crops. The paper establishes this cycle empirically: complete deforestation of a 1 km pixel increases fire ignitions by 279% in the following year, with the effect reversing in the second year, ruling out natural flammability.

Pigouvian enforcement: Government-imposed penalties that alter private incentives to account for externalities. In this paper’s usage, the government’s de facto punishment function — which heavily weights fires spreading into protected areas and populated land — functions as an implicit Pigouvian tax, shaping which fires firms choose to avoid rather than uniformly deterring all illegal burning.

Coasian bargaining failure: The absence of private negotiations between concession holders to internalize the externalities they impose on each other. The paper demonstrates this failure empirically by showing firms treat neighboring concession land no differently from unprotected unleased productive forest, indicating no effective private agreements are limiting cross-concession fire spread.

Wind speed as spread risk shifter: Monthly average wind speed at each 1 km pixel, used as the time-varying component of fire spread risk. A one-standard-deviation increase (approximately 5 km/hr) increases fire spread area by 287%. The paper uses wind speed variation interacted with surrounding land type composition to identify whether firms adjust ignition decisions based on spread risk and who bears the cost.

Unleased productive forest (benchmark): Land within the national forest estate that is neither in a designated concession nor in a protected zone, leaving ownership rights unclear and de facto unprotected. The paper uses firms’ behavior toward this category as the baseline against which sensitivity to other land types is measured, because it attracts the least government attention and the weakest property rights.

Government punishment function: The implicit weights the Indonesian government places on different types of fire damage when deciding whether to investigate a firm, estimated from logistic regression on the 2015 investigation data. The function heavily weights fires burning protected areas and high-population-density land, and places near-zero weight on damage to other private concessions, shaping which fire types firms strategically avoid.