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Proc Natl Acad Sci U S A
Proc Natl Acad Sci U S A
PNAS
Proceedings of the National Academy of Sciences of the United States of America
0027-8424
1091-6490
National Academy of Sciences

39226355
202306496
10.1073/pnas.2306496121
early-career-researcherEarly-Career ResearchercodeCodedatasetDatasetresearch-articleResearch Articleenv-sci-physEnvironmental Sciencessustainability-physSustainability Science417
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Early-Career-Researcher
Physical Sciences
Environmental Sciences
Physical Sciences
Sustainability Science
Charting the future of high forest low deforestation jurisdictions
Teo Hoong Chen hcteo@u.nus.edu
a b 1 https://orcid.org/0000-0003-0127-978X

Sarira Tasya Vadya a b
Tan Audrey R. P. a b https://orcid.org/0000-0002-7984-6994

Cheng Yanyan a b c https://orcid.org/0000-0002-2065-6512

Koh Lian Pin lianpinkoh@nus.edu.sg
a b d 1 https://orcid.org/0000-0001-8152-3871

aDepartment of Biological Sciences, National University of Singapore, Singapore 117558, Singapore
bCentre for Nature-based Climate Solutions, National University of Singapore, Singapore 117546, Singapore
cDepartment of Industrial Systems Engineering & Management, National University of Singapore, Singapore 117576, Singapore
dTropical Marine Science Institute, National University of Singapore, Singapore 119222, Singapore
1To whom correspondence may be addressed. Email: hcteo@u.nus.edu or lianpinkoh@nus.edu.sg.
Edited by Christopher Field, Stanford University, Stanford, CA; received April 20, 2023; accepted June 14, 2024

3 9 2024
10 9 2024
3 9 2024
121 37 e230649612120 4 2023
14 6 2024
Copyright © 2024 the Author(s). Published by PNAS.
2024
https://creativecommons.org/licenses/by-nc-nd/4.0/ This open access article is distributed under Creative Commons Attribution-NonCommercial-NoDerivatives License 4.0 (CC BY-NC-ND).

Significance

This study highlights the importance of protecting high forest low deforestation jurisdictions (HFLDs) against future deforestation. Such deforestation could release substantial carbon into the atmosphere. Our findings have shown that it is insufficient to base market-based carbon financing on historical deforestation rates. Advances in baselining methods are necessary to achieve adequate market-based carbon financing. By using an empirical multifactorial model, we identify the HFLDs at the highest risk of future deforestation. This emphasizes the need for better baselines to protect these vital forests and mitigate the impact of deforestation on climate change.

High forest low deforestation jurisdictions (HFLDs) contain many of the world’s last intact forests with historically low deforestation. Since carbon financing typically uses historical deforestation rates as baselines, HFLDs facing the prospect of future threats may receive insufficient incentives to be protected. We found that from 2002 to 2020, HFLDs (n = 310) experienced 44% higher deforestation rates than their historical baselines, and 60 HFLDs underwent periods of high deforestation (deforestation rate > 0.501%) at 0.983 ± 0.649% (mean ± SD)—a rate 7.5 times higher than the 10-y historical baseline of all HFLDs. For HFLDs to receive sufficient carbon finance requires baselines that can better reflect future deforestation trajectories of HFLDs. Using an empirical multifactorial model, we show that most contemporary HFLDs are expected to undergo higher deforestation from 2020 to 2038 than their historical baselines, with 72 HFLDs likely (>66% probability) to undergo high deforestation. Over the next 18 y, HFLDs are expected to lose 2.16 Mha y−1 of forests corresponding to 585 ± 74 MtCO2e y−1 (mean ± SE) of emissions. Efforts to protect HFLD forests from future threats will be crucial. In particular, improving baselining methods is key to ensuring that sufficient financing can flow to HFLDs to prevent deforestation.

deforestation
climate finance
HFLD
baselines
carbon credits
National Research Foundation Singapore (NRF) 501100001381 NRF-RSS2019-007 Lian Pin Koh MAC3 Impact Philanthropies 0 Tasya Vadya SariraLian Pin Koh National Research Foundation Singapore and Agency for Science, Technology and Research LCER Phase 2 funding programme U2303D4101 Tasya Vadya SariraLian Pin Koh
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pmcThe growing recognition of the need to tackle the twin crises of biodiversity loss and climate change has brought increasing attention to the potential of natural climate solutions (NCS) to mitigate climate change by protecting, managing, and restoring natural ecosystems (1, 2). One of the most important NCS pathways is reducing emissions from deforestation, which prevents the large carbon stocks accumulated by forests for millennia from being released, while safeguarding the cobenefits delivered by forests such as biodiversity conservation (3–5), soil protection (6), hydrological benefits (7, 8), and other ecosystem services (9).

High forest low deforestation jurisdictions (HFLDs) are typically defined as jurisdictions with forest extents that cover more than half their land area and have a historical deforestation rate lower than the global average (10–12). HFLDs contain many of the world’s last ecologically intact and stable forests which harbor exceptional biodiversity and high carbon stocks (13, 14) as well as substantial areas of Indigenous territories (15). Ironically, due to their success at avoiding forest loss, these jurisdictions often face difficulties in accessing market-based carbon finance mechanisms because these mechanisms are designed for jurisdictions to curb high rates of historical deforestation (14, 16). These mechanisms include the trade of carbon credits from reducing deforestation emissions in the compliance or voluntary carbon market to help direct funding for forest protection and other socioeconomic benefits (17–19). These mechanisms are based on methodologies which typically rely on the comparison of a baseline deforestation rate with the expected outcome of the carbon program to determine the program’s added value for reducing emissions from deforestation. This “additionality” determines the amount of carbon finance that the program receives. As such, jurisdictions that have already experienced higher deforestation can receive larger incentives. Concerns have been growing that HFLDs lack sufficient incentives to be effectively protected in the long term (10, 14). This insufficient support may not only result in increased deforestation emissions and undermine climate mitigation efforts but also exacerbate social inequalities by failing to reward the contributions of Indigenous and local communities who have effectively stewarded their forests (20, 21).

To prove additionality, programs for reducing emissions from deforestation are required to follow a range of prescribed methods and guidelines to calculate deforestation emission baselines and their corresponding uncertainties (22–24). Typically, the deforestation emission baseline is a product of two components—a projected deforestation rate for the future period and a forest carbon estimate (10). The types of projection approaches allowed typically fall into three categories: i) historical average, which is a continuation of the average annual rate calculated for a continuous preceding historical reference period of a certain duration; ii) time function, where historical trends are extrapolated to the future from the historical reference period using a linear or logistic regression; and iii) modeling, which uses a model that expresses future deforestation as a function of driver variables (22, 23). Historical averages are most commonly used, although some standards allow adjustments with justification in certain circumstances to derive a crediting level higher than the historical deforestation baseline (22, 24). The methods for calculating deforestation emission baselines as described above broadly apply to both project-scale crediting schemes and jurisdictional-scale crediting programs; however, jurisdictional and nested baselines for reducing emissions from deforestation and forest degradation (REDD+) are gaining favor (25, 26), with baselines determined at the jurisdictional level and then either applied to jurisdictional programs or allocated to site-based projects nested within the jurisdiction. In this study, we focus on baselines determined at the jurisdictional level.

Despite low historical deforestation rates, HFLDs are as susceptible as other forest regions to future deforestation due to political, economic, and climatic drivers. This is especially important for many HFLDs located in the Global South, which are often vulnerable to political instability and may thus lack long-term political will and financial resources for forest protection initiatives. For example, the East Malaysian state of Sabah attempted to use its semiautonomous status to enact more environmentally stringent forestry policies compared to the federal government, but ultimately failed due to deep-rooted patronage networks and local political instability (27). Technological advancements such as electric vehicles spurring demand for minerals also pose threats to forests from mining (28, 29). As such, incentives for forest protection could help to counterbalance the perverse financial incentives to destroy and degrade forests for resource extraction, agriculture, and other purposes, while also providing much-needed resources for enforcement and supporting sustainable livelihoods. New schemes, such as the Architecture for REDD+ Transactions: The REDD+ Environmental Excellence Standard (ART TREES), allow HFLDs an upward adjustment of their crediting level above that derived from a historical average deforestation baseline (24) but have also been accused of supplying nonadditional credits to carbon markets due to doubts over the credibility of these adjustments (30).

Here, we study deforestation trajectories in HFLDs to inform the development of better mechanisms for the sustained protection of HFLDs. First, we challenge the assumption that the past performance of individual HFLDs can predict their future performance in terms of deforestation rates. We do so by mapping and tracking the forest cover of 310 HFLDs across 70 countries during the period 2002–2020. Second, we develop an empirically grounded, multifactorial methodology which uses the collective historical patterns and trends of HFLDs to predict the probability that contemporary HFLDs in the year 2020 would experience significant future deforestation during the period 2020–2038 and their expected future deforestation rates. We demonstrate this by fitting an observation-based phenomenological model to empirical biophysical and socioeconomic data.

Results and Discussion

Observed Past HFLD Trends.

We found that in 2002, there were 310 HFLDs in 70 countries, which contained 1,382 Mha of forests or 43.0% of all forests globally (Fig. 1A; see Methods for details). These HFLDs are jurisdictions that had forest covering more than half their land area, and an annual deforestation rate (taken as a 10-y historical average in this study) lower than the global average of 0.501% found in this study (10-y historical average across all jurisdictions in this study for years ending between 2002 and 2020). Various forest biomes were well represented among HFLD jurisdictions. The largest and most significant HFLDs were those part of the largest major remaining intact tropical forests of the Amazon, Congo Basin, Borneo, and New Guinea. Other notable areas included the Central American rainforests, as well as boreal forest in Russia and Canada. Across all 310 HFLDs, the average annual deforestation rate between 2002 and 2020 was 0.189 ± 0.312% (in this study, mean ± SD for deforestation rates), corresponding to a total of 1.83 Mha y−1 of forest lost and 397 ± 29 MtCO2e y−1 (in this study, sum ± SE for carbon emissions) of emissions from above- and below-ground biomass carbon as well as soil organic carbon.

Fig. 1. (A) HFLDs (n = 310) in 70 countries contained 1,382 Mha of forest cover in the year 2002, comprising 43.0% of all forests globally. Fill color indicates deforestation rate in HFLDs (10-y historical average) for the period 2002–2020. Between the years 2002 and 2020, n = 60 HFLDs (blue outline) experienced periods of high deforestation (>0.501%). (B) HFLDs (n = 274) in 68 countries contained 1,359 Mha of forest cover in the year 2020, comprising 44.0% of all forests globally. Fill color indicates the expected deforestation rate modeled for the period 2020–2038. For the period 2020–2038, n = 72 HFLDs (blue outline) are likely (>66%) to experience periods of high deforestation. (C) Difference map showing jurisdictions which fulfilled HFLD status in 2002 and 2020 (n = 251); jurisdictions which gained HFLD status from 2002 to 2020, i.e., were not HFLD in 2002 but were HFLD in 2020 (n = 23); and jurisdictions which lost HFLD status from 2002 to 2020, i.e., were HFLD in 2002 but were not HFLD in 2020 (n = 59).

However, 60 of these 310 HFLDs no longer met the criteria for HFLD status at some point during the period 2002–2020 due to high deforestation (>0.501%). During periods of high deforestation, these 60 jurisdictions underwent deforestation at the rate of 0.983 ± 0.649%, which was 7.5 times higher on average than the historical baseline of 0.131 ± 0.119% for all 310 HFLDs as of 2002. These 60 jurisdictions lost a total of 516,559 ha y−1 of forest corresponding to 111 ± 11.3 MtCO2e y−1 between the years 2002 and 2020. These HFLDs which subsequently experienced high deforestation were found in tropical, subtropical, and temperate biomes; examples included the Kachin state in Burma, Pahang, and Kelantan states in Malaysia, Lindi and Ruvuma regions in Tanzania, Västernorrland and Värmland counties in Sweden, Samdrup Jongkhar and Chhukar districts in Bhutan, as well as the countries of Puerto Rico and El Salvador. Worryingly, some of these HFLDs are on the edges of the largest remaining intact forest ecosystems on earth, such as the Amazon (Acre state in Brazil and Caquetá department in Colombia), Borneo (Central Kalimantan province in Indonesia and Sarawak state in Malaysia), and Congo Basin (Uíge and Cabinda provinces of Angola). One major impetus for high deforestation in HFLDs in the Global South was the cessation of conflict; for example, Kachin state and El Salvador experienced a cessation of civil war in the early 1990s that resulted in a push for economic development (31, 32). Deforestation was amplified by the presence of valuable resources such as timber, minerals, and hydropower in the HFLDs (e.g., Kachin, Burma, and Sarawak, Malaysia), as well as changing land use to meet food and energy demands (33).

There was a net decline in the number of HFLDs, with the number of jurisdictions qualifying for HFLD status declining from 310 in 2002 to 274 in 2020, and total forest cover within HFLDs declining from 1,382 Mha in 2002 to 1,359 Mha in 2020. A rising proportion of the world’s remaining forests are within HFLDs, increasing from 43.0% to 44.0% from 2002 to 2020. Of the 59 jurisdictions which lost HFLD status between 2002 and 2020 (i.e., qualified as HFLD in 2002 but not in 2020), 45 had underwent periods of high deforestation between 2002 and 2020, while 14 maintained a low deforestation rate throughout 2002–2020 but experienced small decreases in forest cover from just above the 50% threshold in 2002 to just below 50% in 2020. There were 23 jurisdictions which gained HFLD status between 2002 and 2020 (i.e., did not qualify as HFLD in 2002 but did in 2020), as forest cover remained high but the 10-y historical average deforestation rate decreased below the threshold of 0.501% to fulfill HFLD criteria.

Future Projected HFLD Trends.

To predict the probability of contemporary HFLDs (as of 2020) experiencing significant deforestation and their expected deforestation rates over the next 18 y (years 2020–2038), we developed a two-step modeling approach. First, a binomial logistic model was used to estimate the probability of each HFLD jurisdiction as of 2020 either undergoing i) high deforestation or ii) low deforestation. Second, the expected deforestation rates for HFLDs undergoing high and low deforestation, respectively, were taken from i) a random forest machine learning model and ii) the 10-y historical average HFLD deforestation rate. Finally, the expected deforestation rate for each HFLD was derived by taking the probabilistic weighted average from the two-step model.

Our model showed that 72 out of 274 HFLDs as of the year 2020 are likely (>66% probability) to experience high deforestation in the next 18 y. Examples included Littoral in Cameroon, Estuaire, and Moyen-Ogooué in Gabon, Bheri in Nepal, Guinea Bissau, as well as Aceh and North Maluku in Indonesia. These 72 most threatened jurisdictions are expected to experience an average annual deforestation rate of 0.402 ± 0.102% over the next 18 y, corresponding to 283,776 ha y−1 of forest loss and 64.7 ± 5.6 MtCO2e y−1 of emissions. Across all 274 HFLDs, the expected annual deforestation rate is 0.254 ± 0.143% over the next 18 y, corresponding to 2.16 Mha y−1 of forest loss and 585 ± 74 MtCO2e y−1 of emissions.

In the model, the likelihood of a HFLD undergoing high deforestation in the following 18 y was higher for jurisdictions which were located at lower elevations reflecting greater accessibility and competing land uses (34–37) (Table 1). Higher population density and GDP per capita were also risk factors for HFLDs, in line with existing theory and literature on deforestation drivers (27, 38–40). HFLDs with lower forest cover remaining had higher risk of losing their status than HFLDs with higher forest cover, corroborating forest transition theory (11). The higher risks faced by smaller-sized jurisdictions may reflect the modifiable areal unit problem (MAUP) (41, 42), in which the outcomes of spatial analyses can change based on the size and boundaries of the areas studied; jurisdictional baselining approaches need to consider this issue. Interestingly, HFLDs with steeper slopes and lower nightlight intensity were at higher risk of losing their status to high deforestation. Among HFLDs which lost their status to high deforestation, some of the most important variables (in descending order) affecting the deforestation rate were remaining forest cover, proportion of tree plantation area, Human Development Index, and slope (Table 2).

Table 1. Summary statistics from the binomial logistic model used to estimate the probability of HFLDs undergoing high deforestation

Variable	Mean (2002)	Mean (2020)	Regression coefficient	
Elevation (m asl)	587	607	−1.9 × 10−3 ± 4.4 × 10−4	
Slope (°)	3.03	2.96	1.7 × 10−1 ± 6.5 × 10−2	
Temperature (°C)	17.5	18.6	4.6 × 10−4 ± 2.3 × 10−4	
Precipitation (mm)	1,699	1,791	−5.8 × 10−4 ± 2.1 × 10−4	
GDP per capita (US$)	12,249	13,717	2.2 × 10−5 ± 1.4 × 10−5	
Nightlight intensity	1.8	2.5	−9.8 × 10−2 ± 4.5 × 10−2	
Population density (persons per km2)	48.4	49.3	3.4 × 10−3 ± 2.2 × 10−3	
Forest cover (%)	65.0	65.1	−7.0 × 10−2 ± 1.3 × 10−2	
Size of jurisdiction (km2)	67,943	75,222	−7.6 × 10−6 ± 2.2 × 10−6	
Regression coefficients for the final fitted model used, after bidirectional stepwise elimination, are shown here.

Table 2. Variable importance (unitless relative values) from the random forest model used to estimate deforestation rates for HFLDs should they lose their HFLD status to high deforestation

Variable	Variable importance	
Forest cover (%)	21.69	
Tree plantation area (%)	11.94	
Human Development Index	10.33	
Slope (°)	9.71	
Elevation (m)	5.99	
Size of jurisdiction (km2)	5.88	
Nightlight intensity	5.76	
Population density (persons per km2)	5.52	
Jurisdiction (categorical)	4.9	
GDP per capita (US$)	4.5	
Temperature (°C)	3.67	
Precipitation (mm)	3.22	
Mining area (%)	2.83	

Analysis and Discussion of Past and Future HFLD Trends.

Corroborating forest transition theory, our observational evidence showed that many HFLDs experienced increasing deforestation exceeding their historical averages (n = 178; Fig. 2A and Table 3a), which for some HFLDs occurred at high rates exceeding historical averages by many times and thus caused them to lose their HFLD status (n = 60; Fig. 1A and Table 3a). For HFLDs which lose their status to high deforestation, these changes in deforestation rates are sustained over many years, as the HFLD criterion uses a 10-y historical average, and the duration of their lost status was an average of 7.55 ± 4.33 y. There were also some HFLDs which experienced lower deforestation than their historical averages (n = 132), which can be due to policy changes and more success in controlling deforestation (43), or stochastic deviations from the long-term deforestation rate during the historical reference period (44); this also points to the shortcomings of historical averages in accurately predicting future deforestation. However, it should be noted that the actual deforestation rate of 0.105 ± 0.101% for HFLDs which remained low deforestation throughout 2002–2020 (n = 250) was only slightly lower than their historical average of 0.113 ± 0.107%, whereas the actual deforestation rate of 0.538 ± 0.556% for HFLDs which underwent some periods of high deforestation throughout 2002–2020 (n = 60) was 2.6 times higher than their historical average of 0.205 ± 0.137%. This shows that HFLDs face the possibility of a sharply increased deforestation rate in accordance with forest transition theory (11) but not much scope for large decreases in deforestation rate given that the deforestation rates in HFLDs are typically very low to begin with (historical averages were 0.131 ± 0.119% for all HFLDs in 2002 and 0.127 ± 0.119% for all HFLDs in 2020, much lower than the ≤0.501% threshold for low deforestation).

Fig. 2. (A) Difference in deforestation rates (in % points) between actual observations and the historical average, between the years 2002 and 2020 for n = 310 jurisdictions with HFLD status in 2002. Actual observations were higher than the historical average for n = 178 jurisdictions (positive values indicated by yellow to red shading on map), and lower than the historical average for n = 132 jurisdictions (negative values indicated by turquoise shading on map). (B) Difference in deforestation rates (in % points) between our modeled predictions and the historical average, between the years 2020 and 2038 for n = 274 jurisdictions with HFLD status in 2020. Our modeled predictions were higher than the historical average for n = 274 jurisdictions (positive values indicated by yellow to red shading on map).

Table 3. Mean deforestation rate ± SD corresponding to Fig. 2A, for all HFLDs (All), HFLDs which underwent periods of high deforestation (High), and HFLDs which remained low deforestation throughout (Low)

(a) Historical (2002–2020)	(b) Future (2020–2038)	
Data	HFLDs	Deforestation rate (%; mean ± SD)	Data	HFLDs	Deforestation rate (%; mean ± SD)	
Actual	All	0.189 ± 0.312	Modeled	All	0.254 ± 0.143	
	High	0.538 ± 0.556		High	0.402 ± 0.102	
	Low	0.105 ± 0.101		Low	0.202 ± 0.116	
Historical average	All	0.131 ± 0.119	Historical average	All	0.127 ± 0.119	
	High	0.205 ± 0.137		High	0.193 ± 0.144	
	Low	0.113 ± 0.107		Low	0.103 ± 0.098	
(a) For the historical period from year 2002 to 2020, HFLDs (All n = 310) were defined as of 2002, with High (n = 60) and Low (n = 250) determined from actual observations. The same jurisdictions’ historical averages (in the 10-y period prior to 2002) were extracted for comparison. (b) For the future period from year 2020 to 2038, HFLDs (All n = 274) were defined as of 2020, with High (n = 72) and Low (n = 202) determined from modeled predictions where High refers to jurisdictions likely (>66%) to experience periods of high deforestation. The same jurisdictions’ historical averages (in the 10-y period prior to 2020) were extracted for comparison.

In line with the historical patterns and trends, our modeled predictions suggest that future deforestation rates for HFLDs may continue to increase further, and highlighted certain HFLDs as facing greater risks of experiencing high deforestation and thus losing their HFLD status (Figs. 2B and 3B and Table 3b). The differences between historical and future deforestation rates for HFLDs are due to changes to the pool of HFLDs between 2002 (n = 310) and 2020 (n = 274), as well as changes in the driver variables between 2002 and 2020 (Tables1 and 2).

Fig. 3. (A) Forest loss trajectories for all HFLDs, HFLDs which underwent periods of high deforestation, and HFLDs which remained low deforestation throughout, during i) the years between 2002 and 2020 for HFLDs as of 2002, and (ii) the years between 2020 and 2038 for HFLDs as of 2020. See Table 3 for corresponding statistics of mean deforestation rate. (B) Observed forest loss trajectories for HFLDs (n = 60) which lost their HFLD status to high deforestation after 2002. Color indicates whether 10-y historical deforestation preceding the given year was high (>0.501%) or low (≤0.501%). For HFLDs which lost their status to high deforestation, the duration of their lost status was 7.55 ± 4.33 (mean ± SD) years.

Future rates of deforestation derived by this study are likely to be an underestimate, given that our model assumes a temporally stationary relationship between deforestation and driver variables. It does not take into account the indirect effects of deforestation on forest ecosystem resilience, with growing evidence showing that the hotter and drier conditions created by deforestation increase the vulnerability of remaining forests to climatic events such as drought and fire (45), and the associated risk of crossing tipping points at large scales (46, 47). Future scenarios may also involve accelerating deforestation due to rising resource consumption (48, 49), infrastructure development expanding deforestation frontiers further into previously intact forests (50), climatic and biophysical stresses on forests due to climate change (51) such as increasing forest fire occurrences (52), as well as climate refugees (53). For example, despite promises that the impending relocation of the Indonesian capital of Jakarta to forest-rich Borneo would not disturb existing protected forests, conservationists still fear that urbanization and the expansion of infrastructure will encroach into natural habitats home to Indigenous communities and wildlife (38). In addition, the United Nations estimates that the number of people displaced by climate change-related disasters since 2010 has risen to 21.5 million (54) and is projected to increase further as climate impacts worsen. Such large-scale migration away from disaster-prone areas could also result in the degradation of forests elsewhere (55). Our model does not consider these potential future accelerations of threats and would thus be considered conservative. Further research on statistical and process-based models is needed to robustly quantify the potential future acceleration of threats accurately and thus reduce the conservativeness bias that would need to be applied for uncertain or unknown processes (56, 57).

Policy Implications.

Our findings show that crediting baselines based on historical averages are inadequate for protecting HFLDs, particularly a subset of them which face greater risks of high deforestation. Fundamentally, this is because historical averages rely only on an individual HFLD’s historical performance, which do not linearly reflect its future deforestation trajectories as shown by our empirical evidence and forest transition theory. Many existing crediting methods do not apply special consideration for HFLDs and use only a simple 10-y historical average, which was 0.127 ± 0.119% in our study for HFLDs as of 2020. Other methods allow for an upward adjustment of a specific amount of the crediting level above that derived from a historical average deforestation baseline, ranging from up to 0.02% per year for the Green Climate Fund, up to 0.05% per year for ART TREES, and up to 0.1% per year for the Forest Carbon Partnership Facility (22); this is equivalent to considering the additional percentages of the total carbon stock as being at risk of deforestation each year. Our data show that although these upward-adjusted crediting levels can generally reflect the collective historical performance of HFLDs, they are very conservative for those HFLDs facing higher risks, and also underestimate the potential for rising deforestation in the future.

For HFLDs to be sufficiently protected, better approaches toward setting crediting levels need to be developed. These need to be forward-looking by accurately estimating future deforestation risk, but given the need to substantiate additionality and ensure the credibility of HFLD credits, will still need to rely on historical patterns and trends. For example, Guyana in December 2022 issued the world’s first jurisdictional HFLD credits, but this move was met with criticism from observers who said the credits, which had been derived from upward adjustments of historical baselines, lacked additionality (30). The method we demonstrated in this study incorporates the collective historical performance of HFLDs globally, including those which underwent high deforestation, and uses them to predict future trajectories of individual HFLDs. Further modeling efforts and implementation can examine ways to improve the prediction accuracy of deforestation risk in HFLDs especially in those facing high risks of losing their HFLD status, complement our statistical modeling with mechanistic process-based modeling, more sophisticated model validations, and sensitivity analysis to assuage additionality concerns, and manage uncertainties that will be inherent in any model. In particular, the sensitivity of deforestation projections to methodological choices such as parameters and thresholds selected (58, 59), uncertainties in forest datasets such as classification error and semantic differences in forest cover definitions (60–62), as well as the robustness of the commonly used HFLD criterion itself can be further explored.

Our analyses included HFLDs in the Global North that met HFLD criteria, which also contain many Indigenous lands and deliver climate and socioeconomic cobenefits while facing realistic threats of loss. We acknowledge that the current focus of HFLD crediting is on the Global South and there are contextual differences between the Global South and North; future studies may explore this further, and also model both separately. As better global spatial datasets become available, future studies may also make use of them, particularly for drivers known to influence deforestation, such as road penetration and the presence of Indigenous territories.

Overall, our approach is realistic and evidence-based because it is constructed on the basis of historical deforestation patterns across HFLDs and thus can substantiate additionality from future forest loss, while overcoming the limitations of using historical average deforestation rates of individual HFLDs as baselines. These principles can guide efforts to improve crediting methodologies for HFLDs and, along with our observational evidence on historical trends and magnitude of threats facing HFLDs, support HFLDs in accessing international climate finance to better prevent their loss going forward.

Methods

We first mapped forest cover from 1992 to 2020 for 1,920 jurisdictions worldwide. Using 300 m spatial resolution land cover data from the ESA-CCI dataset (63) in Mollweide projection, forest was defined as pixels with at least 15% tree canopy cover; sparse tree cover (<15%), shrublands, and grasslands were not included. The ESA-CCI land cover dataset is a well-regarded and widely used global product available for a long temporal duration (60, 61), with an overall accuracy of 75% against the GlobCover 2009 validation dataset (64). However, as this includes confusion among different subclasses of forest, the user’s accuracy for forest as defined in this study is much higher at 93%. The bias-adjustment technique from map classification error for area estimation was applied (65), which resulted in an adjustment of forest area by 7.4% derived from adding the omission error and deducting the commission error. The producers of the dataset noted that certain regions had a lower number of valid remote sensing observations which may affect classification reliability: the western part of the Amazon basin, Chile, and the southern part of Argentina, the western part of Congo basin, the gulf of Guinea, the eastern part of Russia, and the eastern coast of China and Indonesia; users are advised to be aware of these potential limitations.

In this study, forest gain from newly regenerated areas during the study duration were excluded, given that such forest gain may often be plantations (66) and also because many carbon crediting methodologies require forest lands to have been forest for a minimum duration before the program start date such as 10 y (23). Jurisdictions in this study are de facto countries (as per ISO status) (67) with between 0.1 Mha and 1 Mha of forest cover and first-order subnational divisions (such as states or provinces) in countries with at least 1 Mha of forest cover.

Next, we modeled forest loss trajectories to identify jurisdictions that qualified as HFLD jurisdictions in the year 2002. HFLDs are defined as those with forest cover greater than 50% of total land area, and average deforestation rate of the preceding 10 y less than the global average, in line with the commonly used definition (10–12). The global average deforestation for this study was 0.501%, which was the average of all jurisdictions’ 10-y historical average deforestation for years ending between 2002 and 2020; this threshold was used for all HFLD definitions in this study (i.e., both 2002 and 2020).

We modeled expected deforestation rates over the next 18 y (2020–2038) for each HFLD jurisdiction as of 2020 using a two-step modeling approach. Biophysical, socioeconomic, and land cover covariates were first extracted for each jurisdiction in Mollweide projection as per Table 4. These were chosen to be aligned with globally available spatial datasets with historical time-series coverage ideally dating back to 2002 or earlier, which are commonly used for deforestation risk analysis (68), and reflect processes known to be deforestation drivers (39, 40, 69) (see SI Appendix, Note S1 for further explanation).

Table 4. Data sources and processing methods for biophysical, socioeconomic, and land cover covariates

Variable	Data source and processing methods	Ref.	
Elevation and slope	Gap-filled digital elevation data from Shuttle Radar Topography Mission (SRTM)	(70)	
Long-term mean annual temperature	WorldClim BIO Variables V1	(71)	
Long-term mean annual precipitation			
Global gridded per capita Gross Domestic Product (GDP) adjusted for purchasing power parity (PPP)	Extracted for 2002–2015; years 2016–2020 used data from 2015 due to the lack of more recent data	(72)	
Human Development Index			
Average nightlight intensity	Average nightlight intensity extracted from consistent and corrected DMSP-OLS data for 2002–2013; VIIRS nightlights data were extracted for 2012–2020 and calibrated to DMSP-OLS as per the method of Li et al. (73) and Teo et al. (38) by spatially aggregating both VIIRS and DMSP-OLS to 1 km resolution, generating power regression models for the temporally overlapping mosaics, and using the average of the resulting coefficients to calibrate all VIIRS images	(74, 75)	
Population density	Extracted for 2002–2020 from the Global Human Settlement layer (GHSL)	(76)	
Percentage forest area	Derived from the ESA-CCI dataset as described in Methods, for 1992–2020	(63)	
Percentage land area occupied by mining land uses	Extracted for the year 2019, and applied to all years, based on the assumption that areas mined in the year 2019 generally represent areas with easily accessible and known mineral deposits which present an incentive for deforestation, and also because mining infrastructure tends to remain for decades	(77)	
Percentage land area occupied by tree plantations	Extracted for 2002–2020 from global map of planting years of plantations	(66)	
Refer to SI Appendix, Note S1 on the explanation of use of variables in linear modeling.

In the first step, a binomial logistic model was first used to estimate the probability of each HFLD jurisdiction as of 2020 (n = 274) losing their HFLD status in the next 18 y (pi), as described in Eq. 1:[1] logitpi=logpi1-pi=β0+∑βkXit,k,

where for each jurisdiction i in the year t, logitpi is the log-odds of an HFLD losing its HFLD status in the next 18 y, β0 is the intercept, and βk are the coefficients for covariates Xit,k.

The logistic model was fitted on a dataset of HFLD jurisdictions as of 2002 (n = 310), of which 60 lost their HFLD status to high deforestation at some time between 2002 and 2020, using the covariates listed above for the year 2002. To prevent overfitting and improve model performance given the unbalanced sample, the Synthetic Minority Over-sampling Technique (SMOTE) was applied to generate an additional 210 synthetic samples of the minority class (lost HFLD status) to create a balanced sample. Bidirectional stepwise elimination was used to determine variables that created the best model fit. The fitted model performed generally well, achieving a Brier score of 0.222. The fitted model was used for prediction on HFLDs as of 2020 (n = 274) using covariates for the year 2020. As per Intergovernmental Panel for Climate Change (IPCC) guidelines, a probability of >66% was considered “likely.” This probability threshold was then assessed through a 10-fold cross validation and sensitivity analysis of varying threshold levels (refer to SI Appendix, Note S2 for further details); the findings supported the use of this 66% threshold in our study. Note that this 66% threshold was only used to highlight which HFLD jurisdictions are likely to lose HFLD status and does not affect the expected deforestation rates derived by Eqs. 1 and 2.

In the second step, a random forest machine learning regression model was then used to predict deforestation rates for HFLDs should they lose their HFLD status to high deforestation (rhd,i). The model used 1,000 trees and was trained on a dataset of HFLDs as of 2002 for each year they lost their HFLD status to high deforestation between 2002 and 2020 (n = 453). Given that losing HFLD status to high deforestation entails the average deforestation in the 10 y prior exceeding 0.501%, covariates from the start of that 10-y period were used for model training. The model performed generally well, achieving an out-of-bag R2 of 0.787 and a RMSE of 0.299%. The trained model was used for prediction on HFLDs as of 2020 (n = 274) using covariates for the year 2020. Variable importance was derived from the random forest model using the technique of Janitza et al. (78).

Finally, the expected deforestation rate for each HFLD was derived by taking the probabilistic weighted average between low and high deforestation states that the HFLD may subsequently experience. For HFLDs which lost their status to high deforestation, the mean duration of their lost status was 7.55 y out of 18 total years. As such, for a duration of 7.55 y, we consider that there is a possibility of a HFLD losing its status to high deforestation; for the remaining 10.45 y, all HFLDs are considered to remain as HFLD. This is described by Eq. 2:[2] rexp,i=7.5518×pi×rhd,i+1-pi×rld,i+1-7.5518×rld,i,

where rexp,i is the expected deforestation rate for the ith HFLD jurisdiction, rhd,i is the expected deforestation rate for the ith HFLD jurisdiction that loses its HFLD status to high deforestation, and rld,i is the expected deforestation rate for the ith HFLD jurisdiction should it remain as HFLD (this is taken as the ith HFLD jurisdiction’s 10-y historical average deforestation rate).

Average forest carbon in CO2e was calculated for each jurisdiction. Aboveground and belowground biomass carbon was extracted from (79), and soil organic carbon was extracted from organic carbon density of the topsoil layer (0 to 30 cm) obtained from the European Soil Data Centre (80). A conversion factor of 3.67 was applied to derive the volume of CO2 from carbon (2). We assumed a conservative 10-y decay estimate for the belowground carbon pool (81). Uncertainty for the aboveground and belowground biomass carbon was derived from the data provider, and the IPCC default of 33% was applied for soil organic carbon; these uncertainties were propagated and combined with the regression model uncertainties by summation of quadrature.

In this study, SD were presented for deforestation rates as these analyses take the form of a population model, since the entire population of relevant jurisdictions was included in these analyses. SE were used for carbon analyses as these were propagated from reported SE values from carbon datasets, which utilize a sampling approach.

Spatial datasets were processed in Google Earth Engine using Mollweide equal-area projection. Statistical modeling and calculations were performed in R version 4.0.2, using the statistical and modeling packages “dplyr” v1.1.2, “MASS” v7.3-51.6, “smotefamily” v1.3.1, “ranger” v0.16.0, the visualization packages “ggplot2” v3.4.2 and “cowplot” v1.1.1, and the parallelization packages “foreach” v1.5.0 and “doParallel” v1.0.15.

Supplementary Material

Appendix 01 (PDF)

Dataset S01 (TXT)

Dataset S02 (CSV)

Dataset S03 (CSV)

Dataset S04 (CSV)

Dataset S05 (CSV)

Dataset S06 (TXT)

Dataset S07 (TXT)

Dataset S08 (TXT)

Dataset S09 (CSV)

Dataset S10 (CSV)

Dataset S11 (CSV)

Dataset S12 (CSV)

Dataset S13 (CSV)

Code S01 (TXT)

We thank Xiaogang He (National University of Singapore) for the useful feedback and assistance. This work was supported by the Singapore National Research Foundation grant NRF-RSS2019-007 (L.P.K.), Singapore National Research Foundation and Agency for Science, Technology and Research LCER Phase 2 funding programme U2303D4101 (L.P.K.), and MAC3 Impact Philanthropies (T.V.S.).

Author contributions

H.C.T., T.V.S., Y.C., and L.P.K. designed research; H.C.T. performed research; H.C.T. and Y.C. contributed new reagents/analytic tools; H.C.T. analyzed data; and H.C.T., T.V.S., A.R.P.T., Y.C., and L.P.K. wrote the paper.

Competing interests

The authors declare no competing interest.

Data, Materials, and Software Availability

All study data are included in the article and/or supporting information.

Supporting Information

Although PNAS asks authors to adhere to United Nations naming conventions for maps (https://www.un.org/geospatial/mapsgeo), our policy is to publish maps as provided by the authors.

This article is a PNAS Direct Submission.
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