Biophysical and socioeconomic drivers of oil palm expansion in Indonesia

Indonesia (6°08' N-11°15' S, 94°45' E-141°05' E), is located in Southeast Asia, and with more than 17 500 islands, covering approximately 1 904 569 km², is the largest island country in the world. It has 34 provinces, and 282 regencies and municipalities (as of 1996). The five main islands are Sumatra, Java, Kalimantan, Sulawesi and Papua. It has a population of 238 million (as of 2010), 56% of which is rural (FAO 2011). The land altitude varies from 0 m to 5030 m above sea level. The climate is almost entirely tropical, with temperatures ranging from 21 °C to 33 °C, and the average annual precipitation is around 2700 mm, varying from 1300 mm in East Nusa Tenggara to 4300 mm in parts of Papua (Bappenas 2004). The wet season lasts from September until March, while the dry season lasts from March until August. Value added in agriculture constitutes around 14% of the gross domestic product (FAO 2017), with major cultivation areas including food crops, such as rice and secondary crops (maize, cassava, soybean, sweet potatoes, and peanut), and perennial crops, including oil palm, rubber, coconut, coffee, cocoa, tea, etc. Palm oil production is one of the most important industries, employing about 2.4% of the total Indonesian workforce (as of 2017) and contributing fiscal and foreign exchange earnings to the country (Directorate General of Plantation 2018; Indonesia-Investments 2017). The Indonesian government has promoted oil palm cultivation as a way to alleviate poverty and advance development in remote areas (Li 2016, Dharmawan et al 2020).

Sumatra and Kalimantan are the two islands where more than 95% of the oil palm plantations in the country are located (Wicke et al 2011). Sumatra, located in western Indonesia, is the largest island entirely located in Indonesia, and the sixth-largest island in the world. It has a territory of 473 481 km², a population of 51 million (in 2010), and a tropical rainforest climate. Between 1996 and 2015, the annual average temperature measured from 26.6 °C–27.1 °C, and the annual average rainfall was 2500–3000 mm. Kalimantan is the Indonesian portion of Borneo Island, and comprises 73% of the Island's area. It is the largest island in Indonesia, and has a territory of 544 105 km², a population of 14 million (in 2010), and a tropical rainforest climate. Generally speaking, Kalimantan is cooler and wetter than Sumatra, with an annual average temperature from 26.1 °C–27.5 °C, and an annual average rainfall of 2700–3500 mm from 1996–2015.

We firstly constructed a pooled regression model to explain the observed patterns of oil palm expansion. Our model followed the economic theory that the decision makers will convert other land use types to estate crop plantation so as to maximize the discounted value of net benefits (revenue minus cost) of the conversion (Busch et al 2012, 2015, Busch and Engelmann 2018). The gross economic benefits were first proxied via a linear combination of the estimated potential yield of oil palm and its export value, and then corrected based on the impact of major climate factors contributing to yearly variations in oil palm yield. These major climate factors include annual average temperature, shortwave radiation, annual precipitation, and precipitation in the driest month. The cost of land conversion and transportation was proxied via a linear combination of slope, elevation, available water storage capacity (AWC) of soil, percentage of protected area, percentage of peatland, access time, population density, and a second-order polynomial on source land cover (Mertens and Lambin 2000, Busch et al 2012, Wheeler et al 2013, Austin et al 2015, Pirker et al 2016). Existing publications have demonstrated that previously established plantations had significant effects on conversions to estate crop plantation (Gaveau et al 2009, Sumarga and Hein 2016, Shevade and Loboda 2019), and that fresh fruit bunches of oil palm require to be processed with 48 h of harvesting to ensure oil quality (Furumo and Aide 2017), taking this into account, we also included the estate crop plantation fraction in 1990 and palm oil mill density as the explanatory variables. Of these explanatory variables, export value, climate factors, protected area, population density, and source land ratio are time variant, while others, including potential yield of oil palm, estate crop plantations in 1990, palm oil mill density, access time, slope, elevation, AWC, and peatland percentage, are time invariant.

To summarize, the pooled regression model for estimating the empirical relationships between the observed patterns of oil palm expansion and the variations in benefits and costs of such expansion are specified in the following equation, which shares similarities with the econometric models adopted in Busch et al (2015) and Busch and Engelmann (2018).

$$\begin{align*}\begin{array}{*{20}{c}} {{d_{it}}}&{ = {\textrm{exp}}\left( {{\beta _0} + {\beta _1}{A_i} + {\beta _2}X_i^\prime + {\beta _3}C_{it}^\prime + {\beta _4}{P_{it}}} \right.}\\ {}&{\left. { + {\beta _5}{\textrm{Po}}{{\textrm{p}}_{it}} + {\beta _6}{S_{it}} + {\beta _7}S_{it}^2 + {\beta _8}{E_{t - 1}} + {\varepsilon _{it}}} \right)} \end{array}\end{align*}$$

where ${d_{it}}$ is the area of oil palm expansion into each source land at regency i over year t−1 and t. ${A_i}$ is the potential yield per ha of oil palm plantation at regency i. ${X_i}$ is a matrix of factors which are largely time-invariant, and which play a significant role in determining the cost of land conversion and transportation, including biophysical and geographical factors such as slope, elevation, AWC, peatland percentage of regency i, as well as factors characterizing accessibility to the market, and infrastructure such as average access time to large cities, density of palm oil mills, and percentage of estate crop plantation in 1990 at regency i. ${C_{it}}$ is a matrix of climate factors, including annual precipitation, precipitation in the driest month, average annual temperature, and annual average shortwave radiation at regency i in year t. ${P_{it}}$ is the percentage of regency i within a protected area in year t. ${\text{Po}}{{\text{p}}_{it}}$ is the population density of regency i in year t. ${S_{it}}$ is the source land ratio at regency i in year t, and the second-order polynomial on ${S_{it}}$ captures the non-linear trajectory of the expansion (Busch et al 2015, Euler et al 2017, Busch and Engelmann 2018). ${E_{t - 1}}\,$is the export value, averaged over the previous time period, because there is usually a time delay of approximately 3 years between the planning and the actual planting of oil palm (Carlson et al 2012a; Gaveau et al 2016). β₀ captures the unobserved constant determinants of estate crop expansion.

To address the latency between land preparation and oil palm plantation (Carlson et al 2012a, 2018), and to demonstrate the role of bare ground in the oil palm expansion process, we used Kalimantan as an example, and began by running the model using oil palm plus bare ground expansion as the dependent variable ⁴ , then running the model using oil palm as the dependent variable and bare ground as the land source.

The pooled regression model is optimal and unbiased when the errors are independent, homoscedastic, and serially uncorrelated. However, for LULC change analysis, spatial autocorrelations typically exist among the observations (Elhorst 2003), and with respect to panel data, there are usually individual (pixel) correlations due to the traits of those individuals not represented by explanatory variables (Wooldridge 2015). We employed spatial panel models to account for individual heterogeneity and spatial autocorrelation between regencies. The neighborhood relationship was defined by the contiguity-based method: two regencies were defined as neighbors if they shared a common border. We ran random effect rather than fixed effect regressions, since time-invariant variables play important roles in oil palm expansion (Pirker et al 2016). Spatially lagged dependent variables, spatial error autocorrelation, and spatial Durbin models were included in the panel data regressions to account for the spatial dependencies in either dependent variables or unobserved variables (see Supplementary Information (available online at stacks.iop.org/ERL/16/034048/mmedia)). We used the maximum likelihood approach to estimate the parameters in all the models (Elhorst 2003). The 'plm' and 'splm' packages in R were used for the estimations of the pooled regression model and spatial panel econometric models (Croissant and Millo 2008, Millo and Piras 2012). Section S1 in the supplementary information provides more technical details regarding the above spatial panel models.

The LULC data for the period 1990–2015 were acquired from the Ministry of Environment and Forestry (MoFor) of Indonesia. The MoFor has used satellite data, particularly Landsat, for land cover mapping of Indonesia since the 1990s. To date, LULC maps are available for 1990, 1996, 2000, 2003, 2006, 2009, 2011, 2012, 2013, 2014 and 2015, at a spatial resolution of 30 × 30 m. We used maps from 1990, 1996, 2000, 2003, 2006, 2009, 2012, and 2015 in our analysis, given that it usually takes 2–4 years to allow for sufficient plant growth (Austin et al 2019) and an equal time interval is preferred in time series data (Brockwell et al 1991); in addition, the map of 1990 was used to present infrastructure associated with previously established plantations. The land cover maps of Indonesia consist of 23 classes, including 6 classes of natural forest, 1 class of plantation forest, 15 classes of non-forest, and 1 class of no data (figure 1). We removed the class of no data and reclassified the other 22 classes into seven: primary forest, secondary forest, shrub, dry agriculture, estate crop, bare ground, and others. Table S1 in the supplementary material presents the correspondences between the original 23 classes and the reclassified 7 classes.

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The estate crop plantation class includes oil palm, rubber, coconut, and other plantations. Although oil palm plantation is not an independent class in terms of the available maps, scattered evidence from remote sensing research demonstrates that the plantation of oil palm accounted for about 62% of the total estate crop plantation in the country in 2014 (Petersen et al 2016). As highlighted in the previous section, the dependent variables in our panel models are the increments in oil palm area. In this regard, data from the Statistical Yearbooks of Indonesia (Statistics Indonesia 1997–2016) and the Tree Crop Estate Statistics of Indonesia (Directorate General of Plantation 2013–2018) show that around 89% of the estate crop plantations in the country were attributed to oil palm from 1996–2015; from 2007–2015, the corresponding percentage was around 95% in Sumatra, while in Kalimantan, the expansion of oil palm accounted for the entirety of estate crop expansion. Therefore, when measuring the dependent variable, i.e., the area of oil palm expansion into each source land at regency i in year t, we directly use the area of estate crop expansion as the best available proxy for oil palm expansion.

The potential yield of oil palm was collected using GAEZ v4 from IIASA and FAO at a spatial resolution of 10 × 10 km. The GAEZ model provides an integrated agro-ecological assessment methodology, as well as a comprehensive global database for the characterization of climate, soil and terrain conditions relevant to agricultural production (IIASA/FAO 2012, 2019), and can be used to assess the potential productivity of land under different management regimes. GAEZ is widely used in the estimation of agricultural production potentials and yield gaps at the grid-cell level (Tubiello and Fischer 2007, Gohari et al 2013, Piker et al 2016, Zhong et al 2019). We used the potential yield of palm oil at high input level, with natural rainfall as the input, since it is the commonly used management strategy in oil palm plantation in Indonesia (Pirker et al 2016). Climatic factors, including annual average temperature, annual precipitation, precipitation of driest month and shortwave radiation, were obtained and calculated from the WFDEI dataset (50 × 50 km) (Weedon et al 2014). Export values for oil palm in each year were obtained from the FAO, averaged over the 3-4 year observation periods, and deflated to the value of USD in the year 2000.

We calculated the palm oil mill density based on the Universal Mill List (World Resources Institute, Rainforest Alliance, Proforest, and Daemeter, 2018). Access time data were organized on the basis of A Global Map of Accessibility (Nelson, 2008), which describes the travel time to cities with populations larger than 50 000 in 2000 using land- or water-based means of travel and a cost-distance algorithm, and is publicly available as 30 arc-second. The terrain data, including slope and elevation were compiled using elevation data from the Shuttle Radar Topography Mission (NASA, 2009), which is publicly available as 3 arc-second (approximately 90 m resolution at the equator) Digital Elevation Models (DEMs). AWC was extracted from the Harmonized World Soil Database (1 × 1 km) (FAO/IIASA/ISRIC/ISSCAS/JRC, 2012). Peatland percentages were calculated from the peatland maps collatedby the World Resources Institute (2012). Population density data were collected from the Gridded Population of the World, which provides estimates of population density every 5 years, based on counts consistent with national censuses and population registers with respect to relative spatial distribution, and adjusted to match United Nations country totals (CIESIN, 2016); here, the spatial resolution is 1 × 1 km for 2000–2015, and 5 × 5 km for 1995. The population data were interpolated to match the study period. Protected area data were compiled from IUCN Category I–VI, where point features are displayed as circles, representing the reported protected area size (WDPA, 2014). Source land ratios were calculated from the LULC maps, and natural forest ratios were calculated as the sum of primary forest and secondary forest.

Table S2 lists the variables, the description of the corresponding data, and data sources. Table S3 reports the measurement units and summary statistics of variables. Tables S4.1–S4.3 present the pairwise correlations between explanatory variables in the country, Sumatra, and Kalimantan models. Table S5 reports the variance inflation factors. All maps were projected to the same coordinate system, resampled, and calculated at second administrative level, using ArcGIS 10.5.

Some of the time-invariant variables we employed, such as palm oil mill density, or access time to large cities, are not actually static over time because the proximity or accessibility would change with the establishment of new processing mills, roads, population clusters, etc. Therefore, the effects of these variables as shown by our models may not be precise, and any of these variables constraining oil palm plantation in the past may not continue to be a constraint in the future. Similarly, new constraints may emerge in the future, such as climate change (Paterson et al 2017) and soil degradation (Guillaume et al 2016). In addition, the assessments are limited by the quality of the datasets used for this analysis. The accuracies of LULC maps and other maps have been constrained by the available techniques and socio-political hurdles with respect to data collection. The resolution and time scale of these maps will possibly influence the estimates of land use conversions and the effects of their driving forces.

As shown in figure 2(a), natural forest decreased significantly between 1990 and 2015 in Indonesia. Primary forest decreased by approximately 24.3%, with the most rapid degradation and deforestation occurring from 1996–2000, then 2003–2006, 2000–2003, 2006–2009, and 2009–2015 in order of decreasing pace. Of the 143 281 km² total decrease, 8763 km² occurred in Sumatra, and 31 653 km² occurred in Kalimantan, accounting for 16.9% and 24.8% of their primary forest area in 1990, respectively. Although secondary forest experienced over 80% (125 037 km²) of primary forest conversions, this decreased by about 15.6% (82 524 km²) from 1990–2015. Indonesia lost around 20% (227 039 km²) of its natural forest (primary plus secondary forest) during this period, with the highest deforestation rate (2.11%, 29 746 km² yr⁻¹) occurring from 1996–2000, with 2006–2009 a distant second (1.00%, 9512 km² yr⁻¹), followed by 2003–2006 (0.85%, 8378 km² yr⁻¹) and 2012–2015 (0.73%, 6647 km² yr⁻¹). Figures 2(b) and (c) illustrate these LULC changes in Sumatra and Kalimantan, respectively. The two islands together represent the majority of areas of deforestation; around 65% (517 629 km²) of deforestation in Indonesia during 1996–2000 occurred on these two islands, with the corresponding percentage jumping to 97% (408 017 km²) in the period 2009–2012, and falling back to 85% (392 845 km²) from 2012–2015. Sumatra lost 44.69% (90 206 km²) of its natural forest in the period from 1990–2015 (figure 2(b)), accounting for 39.7% of countrywide deforestation, while 24.93% (87 907 km²) natural forest disappeared in Kalimantan during the same period (figure 2(c)), accounting for 38.7% of the deforestation for the country as a whole. The deforestation rate in Sumatra was consistently higher than the country's average, with the highest annual rates occurring in the periods from 1996–2000 (5.36%, 12 514 km² yr⁻¹) and 2006–2009 (3.59%, 4876 km² yr⁻¹), in conjunction with the occurrence of El Nino events (1997 and 2006) (Field et al 2016). Although the deforestation rate was consistently high, and fluctuated, the total figure decreased as time went by, which is probably due to the long history of agriculture and plantations on the island (National Research Council 1993, Wicke et al 2008, Syuaib 2016), giving rise to the availability of suitable land for productive use which was no longer covered by natural forest (Austin et al 2017). The deforestation rates in Kalimantan were higher than the country's average after 2000, when industrial oil palm plantation was widely introduced to the island (USDA 2010).

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Meanwhile, agriculture activity increased significantly (figure 2). The area for dry agriculture increased by the greatest amount, and estate crops experienced the most rapid expansion. Together with those areas degraded to shrub and bare ground, these were the major drivers of deforestation in Indonesia. Estate crop area increased from less than 45 000 km² to more than 120 000 km² (figure 2(a)), with an average annual speed of 4.24% (annual increase of 3277 km² yr⁻¹). The most rapid estate crop expansion occurred in the period 2012–2015 (with an average annual rate of 8.40%, or 9089 km² yr⁻¹), which was largely a result of the expansion occurring in Kalimantan (with an average annual rate of 15.47%, 5484 km² yr⁻¹), followed by that in 1996–2000 (6.77%, 5668 km² yr⁻¹), mainly driven by the expansion in Sumatra (9.14%, 5057 km² yr⁻¹). Sumatra and Kalimantan together accounted for around 97% of the estate crop expansion in Indonesia in the period from 1990–2015. Sumatra dominated the expansion prior to 2000, constituting 77.1% of the national expansion from 1990–2000 (28 877 km², figure 2(b)), while Kalimantan accounted for 63.67% of the national expansion after 2003 (51 645 km², figure 2(c)), driven by policy reforms in late 1990s which facilitated direct foreign investment in agriculture (Bissonnette 2015).

Natural forest, shrub, and dry agriculture are the three major direct LULC sources of estate crop expansion in Indonesia as a whole, as well as on the two islands specifically (figure 3). Shrub is the largest direct source of estate crop expansion in the country (figure 3(a)), with a contributing share of 32.66% (27 289 km²), followed by natural forest (27.33%, 22 834 km²) and dry agriculture (21.45%, 17 924 km²). Natural forest was the largest direct source of estate crop expansion in Sumatra (figure 3(b)), with a share of 33.59% (13 259 km²), whereas shrub contributed a higher share as time went by, and was the second largest source, with a share of 23.83% (9409 km²). In Kalimantan (figure 3(c)), the trend is somewhat different, as shrub accounted for 42.48% (16 318 km²) of all direct conversions to estate crop from 1996–2015, and was the largest source in the periods from 2000–2009 and 2012–2015. As time went by, estate crop expansion tended to occur on low-biomass land, such as shrub and dry agriculture, while natural forest became a less important direct source. Dry agriculture became a major source of estate crop expansion for both islands, particularly from 2012–2015 (see figures 3(b) and (c)). These shifting patterns of estate crop expansion are consistent with the findings of Austin et al (2017), who also reported a steadily declining rate of oil palm plantations displacing natural forest. This shifting pattern may be explained in the context of the following three reasons: (a) Conservation interventions by the government, NGOs and the private sector with respect to the oil palm industry (Koh and Butler 2009, Indonesian President Instruction no. 10 2011, Indonesian President Instruction no. 6 2013, Von Geibler 2013, United Nations 2014, Butler 2015, Barthel et al 2018) are making some progress towards natural forest protection, although an extension of this protection to cover secondary forest is also needed (Austin et al 2015, Sumarga and Hein 2016). (b) As the availability of suitable forestland becomes more limited, estate crop expansion tends to occur via the conversion of existing agricultural land (Meyfroidt et al 2014). (c) A smallholder requiring access to existing oil palm processing mills will tend to prefer low-biomass land (Walker 2004, Meyfroidt et al 2014).

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Sizeable conversions are observed in relation to bare ground, particularly in Sumatra and Kalimantan in the period after 2000 (figures 2, 3 and S3). The major sources of bare ground establishment were secondary forest and shrub (figure S3). The clearance of natural forest to obtain bare ground made up a higher portion of deforestation as time went by on both islands (see figure S3). In the period from 1996–2015, bare ground accounted for 12.03% (4747 km²) and 15.30% (4878 km²) of the direct sources of oil palm expansion in Sumatra and Kalimantan, respectively (figure 3); oil palm was the only major productive sink of bare ground conversions in Kalimantan and the amount of conversion increased as time went by (figure S3). As there is often a latency between land preparation and oil palm plantation (Carlson et al 2012a), bare ground might be regarded as an intermediate phase of oil palm expansion.

We first ran pooled regression models of oil palm expansion into the three major land sources in Indonesia during 1996–2015. The regression results, as shown in table 1, indicated that oil palm expansion in Indonesia tended to occur in regencies with longer access times to major cities, lower population density, gentler slope, medium level of source land ratio (owing to the inverted U-shape relationship), lower shortwave radiation, higher peatland percentage, and a more significant presence of estate crop plantation in 1990. Higher export value in the previous period (t−1) was positively and significantly associated with a greater prevalence of oil palm expansion, supporting the proposition that oil palm expansion in Indonesia was largely driven by its profitability in terms of export (Armsworth et al 2006, Lim et al 2019). Therefore, as the global palm oil demand continues to grow (Research and Markets 2020), oil palm plantation in Indonesia will continue to expand into both natural forest and low-biomass land. This positive stimulation effect is stronger in relation to expansions into low-biomass LULC types, such as dry agriculture and shrub, than into natural forest. Numerically speaking, an increase of 1 billion (2000) USD in export value in the previous period promises an increase in oil palm expansion by 7.71%, 15.5%, and 20.2% into natural forest, shrub, and dry agriculture, respectively.

Table 1. Regression results of pooled models for expansion of oil palm into three major land sources in Indonesia.

	Natural forest			Shrub			Dry agriculture
	$\hat \beta$	t-value	sig.	$\hat \beta$	t-value	sig.	$\hat \beta$	t-value	sig.
(Intercept)	−114.400	−2.052	**	−77.669	−1.287		−43.563	−0.727
Oil palm potential yield	0.015	1.542		0.034	3.246	***	0.039	3.661	****
Plantation in 1990	0.024	1.807	*	0.042	2.976	***	0.032	2.262	**
Mill density	−0.670	−1.495		−1.249	−2.560	**	−1.388	−2.873	***
Access time	2.249	5.174	****	1.864	5.001	****	1.092	2.704	***
Temperature	0.350	1.898	*	0.236	1.181		0.114	0.577
Shortwave radiation	−0.026	−3.079	***	−0.035	−3.959	****	−0.026	−2.962	***
Precipitation	0.085	0.986		0.043	0.460		−0.093	−1.005
Driest month precipitation	0.338	2.549	**	−0.282	−1.955	*	−0.114	−0.799
AWC	−0.898	−0.257		−8.492	−2.177	**	−11.453	−3.053	***
Elevation	0.083	1.113		0.037	0.464		0.062	0.809
Slope	−0.340	−5.642	****	−0.143	−2.475	**	−0.088	−1.556
Source land ratio	18.928	13.426	****	15.081	6.888	****	12.282	7.494	****
Source land ratio2	−18.751	−11.193	****	−22.664	−6.062	****	−14.568	−7.271	****
Population density	−0.109	−2.068	**	−0.142	−2.423	**	−0.093	−1.552
Export value (t−1)	0.077	3.720	****	0.155	6.874	****	0.202	9.085	****
Peatland %	0.089	7.913	****	0.089	7.390	****	0.081	6.942	****
Protected %	−0.018	−1.461		0.036	2.903	***	0.039	3.217	***
R2	0.356			0.257			0.185
AIC	11 184.770			11 516.140			11 480.520
Log likelihood	−5573.384			−5739.071			−5721.262

Note: *, **, ***, and **** stand for the significant level of 10%, 5%, 1%, and 0.1%, respectively.

We then ran pooled regression models for each of the two islands, Sumatra and Kalimantan. In order to address the possible individual heterogeneity and spatial autocorrelation issues of the pooled models, we also ran spatial panel random effect models in the forms of spatial lag, spatial error and spatial Durbin. Figure 4 visually presents the results of all these regressions for direct comparison. All the spatial panel models showed that there were significant positive spatial autocorrelations on both islands, and that random effects were significantly more important compared to the idiosyncratic errors in Sumatra, but not in Kalimantan (table in figure 4). As shown in figure 4, addressing the spatial autocorrelation did not change the direction, magnitude, and significance inference of the coefficients for individual explanatory variables in the natural forest models, but changed the significance inference of several explanatory variables in the shrub and dry agriculture models. In the shrub models, the effects of oil palm potential yield and driest month precipitation in Sumatra, as well as the effects of mill density in Kalimantan, were largely explained by the positive spatial autocorrelation in the explanatory variables, while the effects of access time in Kalimantan were largely due to the spatial autocorrelation of the oil palm expansions. Meanwhile, the expansion into Kalimantan demonstrated a significant tendency to occur at areas with lower AWC when the spatial autocorrelations of the explanatory variables were addressed. The effects of spatial autocorrelations were larger in the dry agriculture models for both islands, and led to more significant changes in the explanatory variables in the models of Sumatra. When the spatial autocorrelations in Kalimantan were addressed, the coefficients for shortwave radiation became insignificant, while areas with gentler slopes were significantly preferred. For models of Sumatra, the expansion pattern is strongly associated with the significant positive spatial autocorrelation, with the exception that those areas with little estate crop plantation in 1990 were significantly preferred by oil palm expansion to dry agriculture, once the spatial autocorrelations between the explanatory variables were addressed.

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Figure 4 indicates that oil palm expansion on the two islands also tended to occur in areas relatively remote from major cities, in contrast to the assumptions and results of some other researches (Pirker et al 2016, Sumarga and Hein 2016, Lim et al 2019). This result may be explained by the location choice sequence of plantation developers, which is similar to the pecking order sequence of corporate managers in considering their sources of financing (Myers and Majluf 1984, Vogt 1994). This means that suitable areas with better access to major cities were already occupied by existing plantations, so thath any new plantations must therefore be located in more remote areas than the existing ones.

A comparison of the results between the two islands showed some differences in the patterns of oil palm expansion. The establishment of oil palm plantation occurred earlier, and the expansion was also faster before 2000 in Sumatra than in Kalimantan, while the expansion pace grew more rapidly in Kalimantan after 2003 (figures 2(b) and (c); USDA 2013). Since Sumatra has a longer oil palm cultivation history and more intense agricultural activity (National Research Council 1993, Wicke et al 2008, Syuaib 2016), the natural forest resources remaining for estate crop plantation has become limited (figure 2(b)). Compared with Sumatra, Kalimantan was a comparative latecomer (Wicke et al 2008, Austin et al 2017), and land resources for oil palm expansion on the island were therefore less limited (figure 2(c)). Therefore, the expansion patterns of oil palm in Kalimantan proved to be better characterized by our explanatory models than those of Sumatra.

The direction and significance of the coefficients in terms of individual explanatory variables in Kalimantan were more in line with our expectations, i.e., oil palm expansion would be stimulated by the export value of palm oil products, and would tend to occur in areas with greater biophysical suitability and infrastructure accessibility, as well as with lower conversion cost. The stimulation effects of export value were statistically significant and positive for oil palm expansion into each of the three sources in Kalimantan, but not significant for the case of expansion into natural forest in Sumatra. Oil palm expansion in Kalimantan, particularly into natural forest, was more likely to occur in areas with more suitable climatic conditions, such as high shortwave radiation and higher precipitation in the driest month. In both the countrywide and Kalimantan models, oil palm expansion showed an inverted 'U'-shaped relationship with each of the source land ratios, indicating that oil palm expansion tended to occur in areas within the medium range of the source ratio (figure S2). These findings were consistent with those of existing research (Busch et al 2015, Euler et al 2017, Busch and Engelmann 2018). In contrast, on Sumatra island, such an inverted 'U' shape existed in the expansion into natural forest for all models, and into dry agriculture for the pooled model (figure S2). With regard to infrastructure and market factors, the expansion in Kalimantan tended to benefit from existing infrastructure, associated with existing plantations and processing mills, and the beneficial connection was more significant and stronger with expansion into natural forest. By contrast, the plantation in 1990 and mill density did not constrain oil palm expansion into any sources in Sumatra, since oil palm plantation and the associated infrastructure had already dispersed over the island, with the exception of the mountainous area along the west coast. Locations with lower population density were preferred for oil palm expansion into all three land sources in Kalimantan, which could be explained by the following factors: (a) oil palm was less labor intensive than alternative crops (Feintrenie et al 2010, Euler et al 2017, Gatto et al 2017), (b) locations with higher population densities and a longer history of planting traditional crops were less attractive for switching to oil palm (Gatto et al 2015), and (c) oil palm companies intended to avoid the land tenure conflicts and high transaction costs associated with consolidating land from smallholders (Meyfroidt et al 2014). Nevertheless, this relationship was significant in Sumatra for the expansion into shrub only. The percentage of peatland showed opposite effects in terms of oil palm expansion in Sumatra versus Kalimantan. The negative relationship in Kalimantan might be ascribed to the fact that oil palm establishments on the island preferred mineral land to peatland, either due to the lower cost of land preparation, or with the intention of reducing CO₂ emissions (Meyfroidt et al 2014, Afriyanti et al 2016, Rulli et al 2019).

In contrast to our expectations, the potential yield of oil palm showed a negative effect on oil palm expansion in Kalimantan, which could be explained by the location choice sequence of plantation developers, which is similar to the pecking order sequence of corporate managers in considering their sources of financing (Vogt 1994). Areas with higher potential yield of oil palm also offer greater potential yields for other types of plantation, such as dry agriculture crops and paddy fields; as such, those areas had already been occupied by existing agricultural activity and estate crop plantations. Interestingly, such pecking order effects were not found in Sumatra, which might be explained in terms of the following two reasons: Firstly, compared with Kalimantan, where all source lands are generally suitable for oil palm plantation, with a potential yield ranging between 43 and 71 ton ha⁻¹, the potential yield of oil palm in Sumatra ranges between 6 and 72 ton ha⁻¹, with regencies along the west coast being entirely unsuitable for oil palm plantation. Secondly, owing to its longer history of plantation (National Research Council 1993, Wicke et al 2008, Syuaib 2016), remaining land resources for new plantations in Sumatra have become limited since 1990 (figure 2(b)); as a result, oil palm has to expand into areas with relatively high potential yield, but which are very costly or illegal to convert, such as peatland and logging concessions (USDA 2010, Gaveau et al 2013, Austin et al 2017), and where the high proportion of smallholders (40%) aggravates the situation (Molenaar et al 2013, Meyfroidt et al 2014, Gatto et al 2015).

In our analysis at the regency level, protected areas showed no significant effect on oil palm expansion. However, we cannot conclude that protected area status was not effective in protecting natural forest from plantation expansion, because the spatial resolution at the regency level was quite coarse, and protected areas account for only a small portion of the territory of individual regencies. To address the effects of protected areas, analyses at the grid level are required.

Since oil palm is almost the only productive sink of bare ground conversion in Kalimantan, and there is often a latency between forest clearance and oil palm plantation (Carlson et al 2018), we treated bare ground expansion as a phase of oil palm expansion, and ran the pooled and spatial panel models using oil palm and bare ground expansion together as the dependent variable in the first instance (see table 2). The results show that bare ground developed from natural forest was clustered in areas with large protected sectors, more natural forest cover, and were less significantly stimulated by the export value for the previous period, once spatial autocorrelations within the explanatory variables were addressed. We then ran the pooled and spatial panel models using oil palm expansion as the dependent variable, and bare ground as the land source (table 3). The results indicated that the conversion from bare ground to oil palm plantation was significantly stimulated by the export value for the previous period, and that conversion was clustered in those regencies with a higher proportion of protected areas. Considering that bare ground has been developed and converted to oil palm plantation at a rapid pace in recent years (Carlson et al 2012a, 2012b), the above results suggest that bare ground had been increasingly used as an indirect clearing-up tactic for oil palm expansion at a later stage, so that the expansion nominally meets the sustainable development requirements. The existence of this land banking mechanism highlights that it is practically important to include bare ground development in any monitoring system, so that the system can more effectively track where and why bare ground is developed, and its eventual utilization. Meanwhile, as the current moratorium and RSPO certification only deals with new licenses and post-certification activities, it is necessary to establish policies to cope with such land banking.

Table 2. Results of pooled and spatial panel models for bare ground as land banking for oil palm expansion to natural forest in Kalimantan.

	Pooled			Spatial lag			Spatial error			Spatial Durbin
	$\hat \beta$	t-value	sig.	$\hat \beta$	t-value	sig.	$\hat \beta$	t-value	sig.	$\hat \beta$	t-value	sig.
(Intercept)	−1014.906	−2.068		−694.287	−1.503		−970.531	−1.949		−915.913	−1.825
Oil palm potential yield	−0.038	−0.285		−0.051	−0.389		0.020	0.176		0.033	0.304
Plantation 1990	0.091	0.618		0.115	0.804		0.060	0.447		0.046	0.343
Mill density	21.254	2.577	**	20.963	2.607	***	20.915	3.274	****	20.210	3.451	****
Access time	2.028	1.364		0.955	0.668		0.894	0.642		0.757	0.560
Temperature	3.330	2.012	**	2.280	1.464		3.153	1.882	*	2.948	1.747	*
Shortwave radiation	0.046	0.788		0.044	0.787		0.086	1.510		0.110	1.877	*
Precipitation	−0.178	−0.362		−0.195	−0.429		−0.314	−0.575		−0.306	−0.536
Driest month precipitation	1.285	2.417	**	0.981	2.039	**	1.872	2.984	***	2.104	3.155	***
AWC	28.636	0.997		12.244	0.442		0.964	0.035		−0.852	−0.031
Slope	−0.331	−0.712		−0.506	−1.133		−0.330	−0.789		−0.337	−0.835
Source land ratio	14.844	1.928	*	15.691	2.183	**	18.570	2.778	***	17.478	2.752	***
Source land ratio2	−11.746	−1.398		−11.972	−1.523		−12.665	−1.786	*	−11.134	−1.684	*
Population density	−11.621	−4.368	****	−12.338	−4.888	****	−11.312	−4.541	****	−10.040	−4.024	****
Export value (t−1)	0.209	2.299	**	0.144	1.758	*	0.212	1.503		0.303	1.480
Peatland	−0.091	−2.476	**	−0.103	−2.878	***	−0.096	−2.864	***	−0.085	−2.541	**
Protected %	0.124	1.514		0.149	1.868	*	0.153	2.235	**	0.127	1.963	**
phi				0.033	0.595		1.00E-08	NA		1.00E-08	NA
rho							0.500	6.857	****	0.688	8.062	****
lambda				0.396	5.167	****				−0.350	−2.218	**
R2	0.367
AIC	1442.376			1487.258			1475.436			1473.288
Log likelihood	−703.188			−691.629			−685.718			−683.644

Note: *, **, ***, and **** stand for the significant level of 10%, 5%, 1%, and 0.1%, respectively.

Table 3. Results of pooled and spatial panel models of oil palm expansion in bare ground areas of Kalimantan.

	Pooled			Spatial lag			Spatial error			Spatial Durbin
	$\hat \beta$	t-value	sig.	$\hat \beta$	t-value	sig.	$\hat \beta$	t-value	sig.	$\hat \beta$	t-value	sig.
(Intercept)	−926.470	−1.977	**	−1088.743	−2.526	**	−1266.301	−2.602	***	−1150.240	−2.211	**
Oil palm potential yield	−0.190	−1.347		−0.226	−1.732	*	−0.220	−1.828	*	−0.209	−1.876	*
Plantation 1990	−0.020	−0.126		−0.058	−0.397		−0.096	−0.642		−0.080	−0.529
Mill density	24.908	2.886	***	26.774	3.350	****	22.057	3.302	****	16.251	2.792	***
Access time	1.440	0.971		0.753	0.550		0.982	0.680		1.035	0.709
Temperature	3.093	1.969	*	3.683	2.549	**	4.227	2.593	***	3.774	2.167	**
Shortwave radiation	0.054	0.913		0.000	0.005		0.058	0.985		0.141	2.210	**
Precipitation	−0.674	−1.326		−0.397	−0.853		−0.356	−0.636		−0.359	−0.585
Driest month precipitation	0.815	1.410		0.658	1.255		0.973	1.485		1.126	1.551
AWC	−36.322	−1.289		−36.259	−1.394		−45.970	−1.671	*	−49.287	−1.788	*
Slope	−0.484	−1.107		−0.436	−1.080		−0.356	−0.920		−0.418	−1.100
Source land ratio	77.657	1.571		78.387	1.737	*	74.354	1.752	*	55.052	1.427
Source land ratio2	−722.346	−1.716	*	−729.778	−1.909	*	−642.298	−1.766	*	−452.203	−1.369
Population density	−9.735	−3.588	****	−10.846	−4.345	****	−10.020	−3.834	****	−9.211	−3.432	****
Export value	0.500	4.866	****	0.258	2.772	***	0.408	2.999	***	0.630	2.924	***
Peatland	−0.145	−3.965	****	−0.146	−4.309	****	−0.143	−4.199	****	−0.129	−3.766	****
Protected %	0.213	2.335	**	0.253	2.996	***	0.236	3.031	***	0.180	2.475	**
phi				8.14E-03	0.245		1.44E-03	0.078		0.011	0.332
rho							0.399	5.688	****	0.665	7.905	****
lambda				0.340	4.887	****				−0.408	−2.920	***
R2	0.373
AIC	1478.492			1524.830			1518.596			1515.214
Log likelihood	−721.246			−710.415			−707.298			−704.607

Note: *, **, ***, and **** stand for significant levels of 10%, 5%, 1%, and 0.1%, respectively.