1. Introduction

2. Materials and Methods

2.1. Study Area

Malawi is a landlocked country in Southern Africa, shares borders with Zambia to the west, Tanzania to the north and northeast, and Mozambique to the south and southwest and covers 94,080 square kilometers of land areas. The country's subtropical climate, mainly influenced by the Lake Malawi, is characterized by three distinct seasons: a warm and wet season from November to April, a cool and dry season from May to August, and a hot and dry season from September to November. Malawi's forests, defined as having at least of 10% canopy cover and covering more than 0.5 hectares, consist of both natural and plantation forests predominantly featuring Miombo woodland ecosystems, a semi-arid tropical woodland (Skole et al., 2021). The Miombo system covers 90% of the natural forest areas, with some dense evergreen forest located in the highlands. Pine and Eucalyptus are prevalent in plantation areas. The country’s forests are predominantly found in the Northern, Central, and Southern Regions. With a population of approximately 20.6 million people, Malawi relies heavily on agriculture, forestry, and natural resources, with around 85% of the population living in rural areas (World Bank 2022).

Figure 1. Map of Malawi protected areas and government plantations.

Figure 1. Map of Malawi protected areas and government plantations.

2.2. Development of Activity Data

Malawi's REDD+ program (2012-2019) aimed to maximize emission reductions through targeted measures and activities. These activities focused on lowering net emissions by reducing deforestation rates, minimizing forest degradation from unsustainable fuelwood harvesting, and enhancing carbon stocks through afforestation and reforestation. The program included three key activities: Reducing Emissions from Deforestation, defined as the conversion of forest to non-forest land uses; Reducing Emissions from Forest Degradation, which involves a reduction in canopy class while maintaining a minimum canopy cover of 10%; and Forest Enhancements, encompassing activities that increase carbon stocks within public and private plantations.

In contrast, two activities were excluded from the program. Sustainable Management of Forests, which involves the conversion of non-planted forest areas to planted forest areas, was not included due to operational capacity limitations and is instead considered under Forest Enhancements. Conservation of Carbon Stocks, referring to conservation activities that reduce emissions in naturally occurring forests, was also excluded due to a lack of clear definition and distinction within Malawi's forestry sector. However, both activities may be considered for inclusion in future monitoring as their scope and definition become more established.

2.2.1. Activity Data for Deforestation

Deforestation and forest degradation were estimated using a combined random sampling approach. This method involved visually interpreting plots to extrapolate the entire landscape's forest condition. The image interpretation was done using Google Earth images accessed via Collect Earth, a free and open-source system for remote sensing image analysis, with a 0.5 ha plot gridded into a 3x3 sub-grid configuration. Additionally, other image repositories, such as Bing Maps and Planet Imagery, were also considered to support the mapping process. The Collect Earth platform not only facilitates access to high-resolution imagery and streamlines the observation process, but it also offers flexible survey design and is easy to use (FAO, 2017). Collect Earth also promotes uniformity in identifying, interpreting, and annotating plots for reference data to categorize and monitor changes in land cover and land use (Saah et.al, 2019). This method proved advantageous over the traditional wall-to-wall mapping approach, which involves classifying every single pixel of land cover, making sampling more efficient and less resource-intensive despite requiring numerous analysts (Maniatis 2021).

Collect Earth's features, including pop-ups for error reminders and easy data exportation, ensured efficient assessment. Analysts underwent training to adhere to standard operating procedures, ensuring high-quality and reliable interpretation. Color, size, shape, texture, pattern, shadow, seasonality, context and cloud cover were thoroughly considered during the visual interpretation process. Overall, this sample-based approach provided comprehensive insights into deforestation and forest degradation dynamics, supporting effective conservation and management strategies in Malawi. Dozens of countries have used Collect Earth to account activity data for forest cover monitoring (CEO 2021).

The sampling frame, or area over which the random plots were generated, encompassed 2,459,000 hectares (25% of Malawi’s land area), focusing on protected areas, forest reserves, and customary lands, while excluding timber and fuelwood plantations. The sample frame was developed using a land use land cover map developed by USGS in 2017, which was the most up to date land cover map available. Approximately 6,000 points were generated and 5,087 were evaluated to reach the precision threshold of at least 3,000 complete plots with usable imagery available for both 2010 and 2020, enabling analysis of deforestation activity data. If the image not available for specific year for certain plots, analysts were given the flexibility to use images with dates up to two years either before or after the nominal years, meaning that 2010 could include 2008-2012 and 2020 could include 2018-2022. Activity data for deforestation, expressed in units of hectares per year, were estimated by multiplying the period-adjusted proportion deforestation rate in the sample by the total area of the sample frame, as shown in equation 1.

Equation 1: Annual Rates of Deforestation in Hectares

The percent deforestation was estimated by comparing the hectares of forest loss, which was calculated by multiplying the period-adjusted proportion of deforestation rate in the sample by the total area of the sample frame (Equation 1), to the circa-2010 forest extent observed in the sample (Equation 2). This comparison yielded the percentage of forest areas lost due to deforestation, providing a measure of the extent of deforestation in the sample area.

Equation 2: Forest Loss Expressed as a Percent of Initial Sampled Forest Area

2.2.2. Activity Data for Forest Degradation

The quantification of degradation activity data was collected concurrently with the deforestation data collection, as described above. Analysts used a Collect Earth survey, the same survey developed for deforestation assessment, to evaluate the landcover changes using freely available high resolution Google Earth images for circa 2010 (Time 1) and 2020 (Time 2). The same points generated for deforestation activity data assessment were used, including protected areas, and forests outside of protected areas. A mask was applied to exclude government and private timber or fuelwood plantations. Forest degradation was identified as a two-class reduction in canopy cover between the start and end of the reference period, as adopted by Zambia in their FRL (RoZ 2021). Each forested sample plot was assigned a canopy closure class for 2010 and 2020 with a transition from a higher canopy closure class to a lower-class indicating forest degradation. Analysts recorded canopy coverage within each plot for the following canopy coverage thresholds - sparse canopy closure (10%-15%), low canopy closure (15%-30%), moderate canopy closure (30%- 60%) and dense canopy closure (>60%). These canopy closure classes were derived from a fractional tree cover study in Malawi and complement the natural ecology and natural breaks in canopy (Skole 2021). Out of all analyzed plots, 3,301 plots were forest remaining forest, of which 277 points exhibited signs of degradation resulting from a change in canopy closure.

The rate of forest degradation transition was calculated using Equation 3, which divides the total count of degradation observations by the total number of observations to give the proportion of the sample that exhibits degradation.

Equation 3: Proportion of Degradation in the Sample

The area of each degradation transition was quantified, applying Equation 4. To estimate activity data for degradation in hectares per year, the period-adjusted proportion of degradation in the sample was multiplied by the total area of the sample frame, as shown in Equation 4.

Equation 4: Annual Rate of Degradation in Hectares.

The percentage of forest transition from one higher level canopy closure to lower-level canopy closure was estimated using equation 5.

Equation 5: Forest transition expressed as a percent of initial sampled forest Area

2.2.3. Activity Data for Enhancement

The forest carbon stock enhancement accounts for the total GHG removals generated by replanting non-forest areas within designated forest land. This includes all replanted timber and fuelwood plantations from 2010 to 2020. Any planting done outside of this reference period was excluded due to insufficient data and assumed to be negligible. Although the DoF is involved in forest restoration and planting activities outside of timber plantations - either directly or as a coordinator of NGO and community-based organizations-there is currently no systemized approach for monitoring the non-plantation-related enhancement of forest carbon stocks activities. Consequently, non-plantation restoration efforts have been omitted from the FRL. Plantation data was collected via a survey of each registered plantation manager. These managers oversee timber and fuelwood plantations established on lands managed by the government and private entities, including tobacco and tea estates. The survey template was designed to capture the best possible available data including years of plantation, species planted, planting density, the survival rate and areas planted in hectares.

2.3. Emission Factors

2.3.1. Emission Factors for Deforestation

The emission factor for deforestation was derived from the National Forest Inventory (NFI) data from 2018 to 2023. A total of 583 plots level data, 426 from protected areas and 157 from forest outside the protected areas (FOPA), were used to derive the emission factors. The inventory followed the DoF standard operating procedures (SOPs), which were developed and refined through several series of NFI events. National NFI synthesis presented forests data in two strata: protected areas and areas outside of protected areas, representing predominantly dense and less dense forests, respectively. These strata reported carbon stock for above and below-ground biomass. Malawi applies a T-shaped cluster design three nested plots for inventory purpose. Within each plot, the diameter at breast height (DBH), species and damage level of all live trees was recorded. The DBH measurements were taken in three plots of varying radii: 6 m (5-14.9 cm), 12 m (15-29.9 cm) and 20 m (DBH ≥ 30 cm).

Based on the trees inventoried in each plot, the forest carbon stocks were then calculated to determine the national emissions factors for tree carbon pools – aboveground and belowground. Aboveground biomass (AGB) and belowground biomass (BGB) were estimated using the country-specific allometric models (Equation 6), as developed by Kachamba et al (2016).

Equation 6: Above and Belowground Biomass Equation, from Kachamba et al. 2016.

Forest carbon stocks in the deadwood pool (standing and lying) were estimated by assuming dead biomass was equivalent to 1% of the total live biomass and litter biomass was equivalent to 1% of total live biomass following CDM look up tables for tropical forests which receive an annual precipitation of 1,000mm – 1,600mm per year. Forest soil carbon stocks were obtained from Henry et al. (2008).

The total live tree biomass was then converted to tons of carbon (C) multiplying by 0.47 t C t-1 dry biomass matter, which was then multiplied by the molecular weight ratio of CO₂ to C (i.e., 44/12) to convert to CO₂e, following IPCC 2006 Guidelines.

Deforestation emission factors were developed following the IPCC stock-difference approach (Equation 2.25 in the IPCC 2006 Guidelines, Volume 4), which estimates the difference between the pre-deforestation and post-deforestation carbon stocks for each stratum (Equation 7). The carbon stock in biomass prior to deforestation is subtracted from the carbon stock post deforestation, plus the change in soil carbon stock following deforestation (see Equation 7), the conversion factor of C to CO₂ e were applied to give a final result in units of tonnes of CO₂ e per ha. The post deforestation carbon stock in biomass was derived from the IPCC guidelines lookup tables based on the end land use. The tropical dry ecosystem type was applied because Malawi is generally dry for 5-8 months of the year. The post deforestation carbon stock for forests converted to grasslands is 8.7 tC/ha and the carbon stock for forests converted to croplands is 1.8 tC/ha (IPCC Guidelines 2006, chapters 6, Table 6.4 and Chapter 5, Table 5.9 respectively).

Equation 7: Deforestation Emission factor.

The forest soil carbon stock (SOC.f) was obtained from Henry et al., 2008 (Table 6). At the recommendation of the technical assessment team of the initial FRL submission, a national average provided for Malawi was used rather than an average of an ecoregion. As suggested in IPCC 2006 guidelines, the relative stock change coefficients for forest land converted into grassland are 1, 0.97 and 1 for land use factor (FLU), management factor (FMG) and input factor (FI) respectively. Similarly, for forest land converted into cropland, the coefficients are 0.58, 1, and 0.96 for land use factor (FLU), management factor (FMG) and input factor (FI), respectively. Malawi’s deforestation EF development assumed that cropland is characterized by long-term, full-tillage, and low to medium inputs (as described in Table 5.5 and 5.9 of the IPCC 2006 Guidelines, Volume 4), whereas grassland is assumed to have a moderately degraded management (as described in Table 6.2 of the IPCC 2006 Guidelines, Volume 4).

Equation 8: Change in Soil Carbon Stock Following Deforestation

The deforestation emission factor is based on biomass carbon stock difference for above and below ground biomass (Equation 9), with the addition of soil emissions which are calculated separately (Equation 8). The carbon stock in aboveground biomass (C.AGB) and belowground biomass (C.BGB) were calculated as the area-based aggregate from the NFI reports from 2018- 2023. The rest of the carbon stocks (i.e. deadwood and litter) were obtained following the IPCC guidelines.

Equation 9: Carbon Stock in Total Forest Biomass, Prior to Deforestation, used in Malawi's Deforestation EF.

2.3.2. Emission Factor for Forest Degradation

Estimating emission factors from forest degradation poses a significant challenge due to various factors inherent in the dynamic nature of forests and the complexities involved in measuring and quantifying changes accurately. For emissions from forest degradation, aboveground biomass and below ground biomass pools are included. It is assumed that deadwood and litter pools would be insignificant due to anthropogenic pressures. In Malawi, estimating emission factors for forest degradation is particularly challenging due to the prevalence of anthropogenic activities such as illegal charcoal production, unsustainable firewood collection, forest fires, logging, and agricultural expansion. To address this challenge, emission factors were calculated using a robust method based on national inventory data collected from 2018-2023, comprising 583 total plots. The inventory teams assessed and recorded all canopy closure classes in the field, and the following steps were taken to account for activity data:

• Each of the 583 plots was assigned a canopy closure class based on field observations, categorizing them as sparse forest (10%-15%), low forest (15%-30%), moderate forest (30%-60%), or dense forest (60% and above).
• The area-weighted average of each canopy closure class was then calculated to ensure representative coverage
• Finally, emission factors were calculated by applying the change in canopy closure class (from initial to final) to each plot and multiplying it by the area of each transition, thereby accounting for the impact of forest degradation on carbon stocks.

Equation 10: Change in Carbon Stock EF for Degradation.

2.3.3. Emission Factor for Forest Enhancement

The removal factors utilized in this study represent the carbon sequestration capacity of planted tree species in both governmental and private plantations in Malawi. Derived from empirical data, these factors estimate the amount of carbon dioxide removed from the atmosphere through the growth of specific tree species over time. Predominantly, Eucalyptus and pine species are documented in plantation records, and chosen for their suitability to local conditions. Table 1 outlines the proportions of plantation areas allocated to each species and their typical harvest cycles, providing essential data for estimating carbon accumulation rates. By incorporating species-specific information, this approach ensures accurate carbon accounting in Malawi's forestry sector, tailored to the characteristics of planted tree species and management practices. This comprehensive methodology enhances the reliability and precision of carbon sequestration assessments in plantation forests.

The removal factors used in this study were extracted from the Global CO₂ Removals Database (Bernal et al., 2018), specifically opting for values applied to tropical dry climates and corresponding to the tree species outlined in Table 2. The Global Removals Database was chosen over the IPCC defaults (2006 Guidelines and 2019 Refinement, Volume 4) due to its comprehensive and scientifically validated data on all three species of interest in Malawi. Notably, the IPCC (2019) does not provide removal rates for coniferous Eucalyptus species in tropical dry climates. In contrast, the Global CO₂ Removals Database, developed through a review of 335 scientific peer-reviewed manuscripts and published reports, offers a robust dataset with 1197 independent data points. Specifically, the database provides 32 data points for Eucalyptus and 28 data points for Pine species, ensuring a reliable basis for our removal factor calculations. Adhering to a conservative methodology, only above ground and below ground biomass pools are included as the other pools are not considered a significant source of additional removals (Pearson et al. 2005). The growth curves employed to derive removal factors for the three species categories encompassed in this investigation were selected to ensure alignment with empirical research and observed growth patterns (Bernal et al. 2018).

To derive an annual removal rate, the total aboveground biomass carbon stocks for each of these species were divided by the length of their rotation (listed in Table 1). The final removal factors applied for each species planted in forest plantations included in Malawi’s REDD+ program assume that each year the committed sequestration for an entire rotation length is accounted for each year a new plantation area is planted. This entails taking the middle point of the maximum peak biomass at felling age. Table 2 summarizes the removal rates.

3. Results and Discussion

3.1. Deforestation: Activity Data and Emission Factor

The activity data for deforestation in protected areas and FOPA is presented in Table 4. Of the total number of points assessed, 247 points exhibited a conversion from forest area in Time 1 (2010) to non-forest area in Time 2 (2020). This translates to an annual deforestation rate of 11,565ha ± 1,067ha per year, equivalent to annual loss of 0.66% ± 0.03% of the total forest area nationally. From 2010 to 2020, forests in protected areas decreased by an annual rate of 7,035 ha, while forest outside the protected areas decreased by the annual rate of 4,192 ha, during the same period (Table 4).

This current estimated deforestation rate of 0.66% per annum is slightly higher than the 0.63% rate reported during the first FRL submission, which translated to 14,500 ha. Previously, the Government of Malawi had estimated a deforestation rate of 2.98% since the early 1990s (Malawi Forestry Policy, 2016). Various studies have been conducted over time using different approaches. For instance, Skole et al. (2021) employed fraction cover mapping, reporting a deforestation rate of 22,410 ha/yr. Similarly, Bone et al. (2016) assessed deforestation from 1972 to 2009, yielding a rate of 34,486 ha/yr. Kerr (2005), while reporting a deforestation rate of 2.4%, identified some of the underlying drivers to this deforestation as population increase that was reported as high as 3.1% per annum; and poverty with 80% of the population employed in subsistence agriculture. Despite these variations in these estimates, most of this deforestation is commonly and proximately attributed to excessive biomass extraction, agriculture expansion and flue-cured tobacco growing. World bank data shows that population growth rate is persistently high at 2.6% and 72% of the population living on less than $2.15/day, replying in forests for their livelihood.

Deforestation predominantly occurred within forests having a canopy coverage exceeding 30%, with over 60% of the deforestation taking place in these areas. As shown in Table 3, the majority of the plots (3,301) remained as forested areas, while a notable 227 plots underwent a transition from non-forest to forest areas. This conversion can be attributed to restoration efforts within both customary forests and protected reserves (GoM, 2017).

Table 3. National transition.

Table 3. National transition.

Time 1	Time 2	Number of plots	Transition
Forest	Non-Forest	247	Forest- non-Forest
Forest	Forest	3,301	Forest- Forest
Non-Forest	Forest	227	Non-Forest- Forest
Non-forest	Non-Forest	1,312	Non-forest-non-forest
Total		5,087

Notably, 85% of the deforestation points were converted to grassland, while 15% were attributed to cropland, accounting for the majority of land-use conversion. Within the protected areas, an average of 703 hectares of forest were converted to cropland, while 6,331 hectares were converted to grassland each year. Similarly, outside the protected areas, the annual conversion rates were 1,048 hectares to grassland and 3,144 hectares to cropland. Over a ten-year period, the cumulative conversion of forest to grassland and cropland was 94,755 hectares and 17,515 hectares, respectively. A study by Missanjo and Kadzuwa (2024) reported significantly higher conversion rates, with 220,800 hectares of forest land converted to cropland and 15,300 hectares to grassland between 2010 and 2022. These findings align with other studies that have linked deforestation to agricultural expansion, unsustainable fuelwood extraction, and charcoal production (Missanjo and Kadzuwa, 2024; Munthali et al., 2019). Study conducted in Dzalanyama Forest Reserve and Dedza also found that charcoal production, firewood production, infrastructure development, and agriculture expansion are major drivers of deforestation in the forest reserves and peripheral areas (Katumbi et al., 2017; Munthali et al., 2019).

Table 4. Deforestation rate in Malawi between 2010 and 2020.

Table 4. Deforestation rate in Malawi between 2010 and 2020.

Forest stratum	Total area (ha)	Deforestation
				Annual change (ha)	Annual change (%)
Protected area	1,324,500	7035 ± 1215	0.66% ± 0.07%
Non protected area	1,134,500	4192 ± 1040	0.69% ± 0.07%
National	245,900	11,565 ± 1,067	0.66% ± 0.03%

Five major carbon pools were considered to estimate emission factors, as provisioned in the UNFCCC guidelines: Aboveground Biomass (ABG), Belowground Biomass (BGB), Leaf Litter, Deadwood, and Soil Organic Carbon (SOC). Using allometric equations from Kachamba et al. (2016), the National Forest Inventory (NFI) data collected between 2018 and 2023 were used to estimate ABG and BGB. As expected, carbon stock estimates for ABG and BGB differed between Reserve and FOPA areas. In the Reserve area, the average carbon stock per hectare (tC/ha) ranged from 46.46 tC/ha in 2023 to 67.73 tC/ha in 2021, with an area-weighted average of 57.26 tC/ha across the five inventory periods (Table 5). In contrast, the non-reserve area exhibited relatively stable carbon stock estimates, with an area-weighted average of 45.03 tC/ha. Notably, the average weighted biomass value of forests outside protected areas was 18% lower than those within protected areas, indicating some variation, although less than expected anecdotally. The differences in carbon stock estimates between Reserve and Non-Reserve areas can be attributed to differences in forest management practices and pressure on forest resources from the communities.

The CDM AR tool's default factor of 1% of total biomass was used to calculate the carbon stock for deadwood and leaf litter. This resulted in a carbon stock estimate of 0.57 tC/ha for both leaf litter and deadwood in Forest Reserves, and 0.45 tC/ha for both components in FOPA. As recommended by the technical assessment team during the initial FRL submission, the national average carbon stock value for Malawi, based on Henry et al. (2008), was used to calculate the SOC stock, with a national average carbon stock of 47 tC/ha.

Deforestation emission factors were developed following the IPCC stock-difference approach (Equation 2.25 in the IPCC 2006 Guidelines, Volume 4). This approach estimates the difference between pre-deforestation and post-deforestation carbon stocks for each stratum (Equation 7). The carbon stock in biomass prior to deforestation is subtracted from the carbon stock post-deforestation, and then the change in soil carbon stock following deforestation is added. The resulting value is then converted to CO₂ equivalent (CO₂-e) using the appropriate conversion factor, yielding a result in units of tons of CO₂-e per hectare. The final deforestation emission factors for areas of Forest Reserve and FOPA converted into cropland are 133.92- and 112.42-tons CO₂-e/ha, respectively. Similarly, the deforestation emission factors for areas of Forest Reserve and FOPA converted into grassland are 88.09- and 66.59-tons CO₂-e/ha, respectively.

4. Conclusions

Note

¹ 

https://cdm.unfccc.int/methodologies/ARmethodologies/tools/ar-am-tool-12-v3.0.pdf

² 

https://www.worldbank.org/en/country/malawi/overview

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