Carbon emissions and sequestration produced by deflorestation, forest degration, reforestation, and natural forest recovery, case studyABSTRACT
Background: The degradation and loss of forest cover negatively impact different ecosystems, causing many socio-environmental challenges. This study aims to estimate carbon emissions from deforestation and forest degradation, as well as carbon sequestration through natural forest recovery and reforestation in the North Pacific Basin of Mexico. The methodology follows the guidelines of the good practices of the Intergovernmental Panel on Climate Change, combining activity data with emission and sequestration factors.
Results: The findings indicate a deforestation of 597,124 ha, a forest degradation of 491,285 ha, resulting in emissions of 9,685.28 Gg CO2e and 1,048.49 Gg CO2e, respectively, as well as reforestation of 5,328 ha and a natural forest recovery of 97,112 ha, which originated an absorption of 6.81 Gg CO2e and 413.38 Gg CO2e, consecutively.
Conclusion: The highest emissions were associated with the conversion of primary and secondary deciduous forests into annual croplands. Furthermore, primary oak forests transitioned into secondary oak forests. The most affected municipalities include Badiraguato, Mezquital, Guadalupe y Calvo, Durango, Guachochi, Culiacán, and El Fuerte.
Keywords:
Carbon sequestration; Land-use change; Carbon emissions; Deforestation; Forest degradation; Natural Forest recovery; Reforestation; Carbon dioxide
Introduction
Scientific evidence suggests that the quality of human life is closely linked to ecosystems (Tolessa et al., 2017) since they depend on the services provided by these. Such as drinking water, food, climate regulation, and the nutrient cycle, among other things (Gao et al. 2017; Pullanikkatil et al., 2016). However, economic and demographic growth that humans have developed has contributed to a decrease in the quality of these ecosystems, causing adverse reactions such as the loss of biodiversity, soil, and carbon (Cao et al., 2015). Furthermore, deforestation and other exploitative activities disrupt ecosystem balance and hinder natural recovery processes, posing a severe risk to humanity’s future (Leal Esper, 2021).
Land-use changes are key drivers of ecosystem stability, decline, or extinction, particularly in forest ecosystems (Zhang et al., 2017). Especially deforestation, which is defined as the conversion of forest land to non-forest land (Oca et al., 2021; UNFCC, 2011), followed by the process of forest degradation, which is caused by selective logging, forest fires, local use of wood for fuel, and livestock grazing, mainly (Achard et al., 2014; Betts et al., 2024). For this reason, deforestation and forest degradation considered within the Agriculture, Forest, and Other Land Uses (AFOLU) sector of the Intergovernmental Panel on Climate Change (IPCC), contribute up to 20% of global emissions of anthropogenic carbon dioxide (CO2) in the atmosphere (Cadman et al., 2017; IPCC, 2007). Consequently, it is preoccupying since forests serve as essential sinks that can capture close to 20% of anthropogenic carbon emissions globally (Pan et al., 2024).
In contrast, to counteract these two negative forest processes, reforestation is a human-driven process that involves active planting in areas where forest cover previously existed, to restore that cover. Similarly, natural forest recovery is a spontaneous process, without direct human intervention, in which local species colonize an abandoned or disturbed area (Crouzeilles et al., 2017).
The Reducing Emissions from Deforestation and Forest Degradation (REDD+) strategies established by the IPCC play a crucial role in sustainable forest management, conservation, and carbon stock enhancement (Nathan and Pasgaard, 2017). Effective implementation of these strategies requires accurate assessment of forest losses and gains, as well as robust monitoring capabilities.
The latest Global Forest Resources Assessments (FRA) program revealed in its latest assessment that deforestation was reduced in the last decade (2010-2020) to 4.7 million ha per year, due to an increase in forest area due to afforestation or natural expansion of forests (FAO, 2020). However, it does not mean that these last figures are not alarming, since during the period 1990-2000, there was a forest decrease of 7.8 million ha per year and 5.2 million ha during 2000-2010 (FAO, 2020; Keenan et al., 2015).
Additionally, deforestation monitoring capabilities have improved in recent years in tropical countries. That has allowed various research projects on forest processes to develop in different parts of the world (Bartalev et al., 2014; FSI, 2018; Grinand et al., 2013; Hansen et al., 2013; INPE, 2018).
Forest degradation can also lead to significant CO2 emissions, to the point that in some regions this process emits more emissions than the process of deforestation itself (Hosonuma et al., 2012). Globally, forest degradation is responsible for approximately 5-25% of forest CO2 emissions (Pearson et al., 2017). This forest degradation is caused by the legal and illegal logging of trees, the use of wood as firewood or for the construction of farms, forest fires, and animal grazing (Hosonuma et al., 2012).
Deforestation and forest degradation have been jointly evaluated for CO2 emissions, as indicated by the works of (Achard et al., 2014; Pacheco-Angulo et al., 2017). These processes play an essential role in the global carbon cycle, affecting the ability of ecosystems to provide their climate micro-regulation service, water, and biodiversity.
Globally (Achard et al., 2014), studies of forest degradation and implicitly made estimates of carbon loss for the tropical belt. Their results reported a forest loss of 1.514 million hectares, while carbon losses from deforestation from 2000 to 2010 accounted for 10% of carbon emissions from fossil fuel burning. While, Andersen et al. (2016) generated estimates of carbon associated with land-use change and estimated carbon content of land cover. They found that for 2000-2010, 45% of the cover recorded neutral emissions, while 39% of the cover recorded net carbon emissions, followed by 17% as carbon absorbers.
Mexico is among the top 20 countries with the highest deforestation rates worldwide (FAO, 2020; FRA, 2010; Hansen et al., 2013). Not escaping from the world trends described previously, with changes that are generally above the world average in terms of deforestation rates, increase in cultivation areas, grazing, urban expansion, and many others (Rosete-Vergés et al., 2014; Skutsch et al., 2014; Valdez Pérez et al., 2015). Likewise, it is located among the first 50 countries with the highest emissions due to forest degradation (Pearson et al., 2017).
For these reasons, the main objective of this research is to estimate the CO2 emissions produced by deforestation, forest degradation, and the CO2 absorptions generated by natural forest recovery and reforestation in the North Pacific basin, Mexico, from 2002 to 2021, and, as a result, to determine which land use and land cover changes generate the most emissions, as well as to identify the municipalities most affected by these forest processes. This region is among the most affected by deforestation and forest degradation due to socio-economic activities such as agriculture, mineral extraction, industry, commerce, and tourism, which have severe impacts on forest ecosystems (CONAGUA, 2012). Therefore, we start from the following hypothesis: “changes in land use and land cover from primary and secondary forests to agricultural land and pastures are conversions that generate higher net CO2e emissions than the carbon balance generated by reforestation and natural forest recovery.”
Materials and methods
Study area
The North Pacific Basin covers the entire state of Sinaloa and some municipalities of the states of Durango, Chihuahua, Zacatecas, and Nayarit, in an area of 152, 013 km2, corresponding to 8.0% of the surface of the Mexican country (Figure 1). Its estimated population is 4, 466, 000 inhabitants (INEGI, 2016a). The increase in the population and socioeconomic activities caused changes in occupation and land-use, which have induced adverse effects, such as soil degradation, a decrease in aquifers by altering the water cycle, and loss of biodiversity (CONAGUA, 2012).
The study area is the most fertile valley in the country, contributing 30.2% of the cultivated food production (CONAGUA, 2015). This agricultural production is mainly due to the water wealth of the region, which has 13 rivers and 11 main dams, which fulfill different functions such as water storage, fishing, and irrigation, and 7 of these serve as hydroelectric dams (CONAGUA, 2012).
Materials
The official maps from series III (2002) and series VII (2021) of Land Use and Vegetation (US&V, by its acronym in Spanish) were used at a scale of 1:250,000 in a Lambert Conformal Conic reference system, which were obtained through INEGI (INEGI, 2016b). The cartography was generated based on photo interpretation by experts and fieldwork to validate and refine the interpretations made, using criteria of physiognomy, floristics, and phenology. The Serie III used Landsat 4 and 5 sensor images, Series VII incorporated Spot 5 and Landsat 8 for photo interpretation. These maps were selected as they closely align with the data from the National Forest and Soil Inventory (INFyS, by its acronym in Spanish), which was done between 2004 and 2009 (CONAFOR and SEMARNAT, 2009). This inventory covers the field sampling stage with 24,659 clusters studied, which in turn comprised 81,665 sampling sites distributed geographically across all vegetation conditions in the country. Also, the data from the National Inventory of Greenhouse Gases of Mexico was used (INGEI, by its acronym in Spanish) (INECC and SEMARNAT, 2015), generated through field surveys in the INFyS as mentioned above.
Methodology
To achieve the stated objective, land-use changes within the basin were analyzed, followed by the estimation of CO2 emissions resulting from deforestation and forest degradation, as well as CO2 sequestration from reforestation and natural forest recovery. These estimations were conducted following the IPCC good practice guidelines (IPCC, 2006).
Preprocessing of Data
Geometric and topological correction was applied to the US&V maps, and thematic categories were homogenized according to the National Inventory of Greenhouse Gases of Mexico reported to the United Nations Framework Convention on Climate Change (UNFCCC) (INECC and SEMARNAT, 2015). After, the US&V maps were rasterized (vector to raster) with a pixel size of 100 m. This was done to determine the emission and absorption factors at the pixel level, because these factors are measured at the hectare level.
Land-use change
Land-use change analysis between 2002 and 2021 was performed using the cross-tabulation matrix proposed by Pontius et al. (2004). This method allowed the calculation of land-use gains (Equation 1), losses (Equation 2), net change (Equation 3), and total changes (Equation 4) among the categories of the US&V maps.
Where Gij is the gain, P+j is the sum of the column in question, Pjj is the value of the main diagonal of the column in issue, Lij is the losses, Pj+ is the sum of the row in question, Dj is the net change and DTj is the total change.
Estimation of emissions and absorptions of CO2e
The analysis of CO2 emission and absorption caused by the forest processes of deforestation, degradation, reforestation, or natural recovery was done by classifying based on the INEGEI. For this analysis, the Activity Data (AD) referring to the land-use change of the US&V maps, the Emission Factor (EF), which represents forest loss, was also used, as well as the Absorption Factor (AF), which means a forest gain (INECC and SEMARNAT, 2015).
These EF and AF data were acquired from INEGEI, where emissions were estimated for all land-use coverage in Mexico, using field dasometric data, soil type, and vegetation prepared by the INFyS (Annexes 1, 2, 3, 4, 5, and 6). It should be noted that to obtain the EF/AF, the INFyS conducted an uncertainty analysis with 95% statistical reliability using a stratified and systematic cluster sampling design, which indicates that the carbon estimations could have a relative sampling error of between 5% and 10% (CONAFOR and SEMARNAT, 2009).
In accordance with the IPCC good practice guide, the following equations were used to obtain the FE. They used the "stock change" method, measuring the existence of carbon (Equation 5) in 2 times.
where i corresponds to the type of climate, j corresponds to the type of forest, k corresponds to land use, Ct1 is the existence of the carbon sink at time 1 (in tons of carbon) and Ct2 is the existence of the carbon sink at time 2 (in tons of carbon).
For the FA, the "gain-loss" (Equation 6) method depended on the rates of loss or gain of the land use area.
where A is the land use area, Ci is the rate of carbon gain (in tons per year) and CL is the rate of carbon loss (in tons per year).
In such a way that, to know the estimate of CO2 emission (E), Equation 7 was used, and to know the absorption (A) of CO2 (Equation 8), both stipulated by the IPCC guidelines. The amounts of CO2 emitted or absorbed were converted to Carbon Dioxide Equivalent (CO2e) according to (WRI, 2005; INECC; SEMARNAT, 2015).
Results
Preprocessing of data, and Land-use change
The first result was the homogenized US&V maps according to the INGEI of Mexico (Figure 2). The variations between categories were determined by analyzing the changes between both maps through a cross-tabulation matrix. For example, in the human settlements category (5), in 2002 its area was 75,014 ha, while in 2021 it was 109,354 ha, that is, there was a gain between 2002 and 2021 of 36,356 ha. Said profits are generated mainly from the annual agricultural (2) and pasture (20) categories with 26,552 ha and 4,075 ha, respectively. Nevertheless, human settlements remained stable at 72,998 ha. When comparing this value with the superficial value of the first date, a differential of 2,016 ha went from this category to annual agricultural land (2) and perennial agricultural land (3) with 1,607 ha and 79 ha, respectively.
The forest covers with the greatest losses were the primary coniferous forest (7) with 477,019 ha, the primary oak forest (9) with 348,392 ha, and the primary deciduous forest (21) with 284,505 ha. On the other hand, the categories that presented the most significant gains were annual agricultural (2) with 409,026 ha, secondary coniferous forest (8) with 359,687 ha, and secondary oak forest (10) with 193,149 ha.
The greatest persistence occurred in the primary coniferous forest (7) with 3,264,158 ha, in annual agricultural (2) with 2,596,434 ha, and in the primary deciduous forest (21) with 2,080,985 ha. The categories with the greatest net variation were the secondary coniferous forest (8) with 297,559 ha, the primary coniferous forest (7) with 274,654 ha, and the primary oak forest (9) with 175,037 ha (Table 1).
Forest processes
Forest processes. Leyend (1 Batopilas; 2 Chínipas; 3 Guachochi; 4 Guadalupe y Calvo; 5 Guazapares; 6 Maguarichi; 7 Morelos; 8 Urique; 9 Canatlán; 10 Canelas; 11 Durango; 12 Guadalupe Victoria; 13 Mezquital; 14 Nombre de Dios; 15 Otáez; 16 Pánuco de Coronado; 17 Poanas; 18 Pueblo Nuevo; 19 San Dimas; 20 Súchil; 21 Tamazula; 22 Topia; 23 Vicente Guerrero; 24 Nuevo Ideal; 25 Acaponeta; 26 Huajicori; 27 Rosamorada; 28 Ruíz; 29 Santiago Ixcuintla; 30 Tecuala; 31 Tuxpan; 32 Ahome; 33 Angostura; 34 Badiraguato; 35 Concordia; 36 Cosalá; 37 Culiacán; 38 Choix; 39 Elota; 40 Escuinapa; 41 El Fuerte; 42 Guasave; 43 Mazatlán; 44 Mocorito; 45 Rosario; 46 Salvador Alvarado; 47 San Ignacio; 48 Sinaloa; 49 Navolato; 50 Chalchihuites; 51 Sombrerete).
This analysis (Figure 3) also gave us an overview of the forest processes. For example, 18.08% of secondary jungles, 5.87% of primary Jungles, 4.10% of secondary forests, and 2.01% of primary forests were deforested between 2002 and 2021.
Concerning the gain represented by the natural conversion within the same categories of forest and jungles, 0.92% of secondary forest went to the primary forest, and 1.55% of secondary jungle went to the primary jungle. In contrast, the reforestation of forests and jungles taken in this study as the transformation of other land-use to forest or jungle categories, where 1.47% of different categories went to primary forest, 2.85% to primary Jungle, 14.48% secondary forest, and 15.90% secondary jungle.
In general, it can be determined that the conversion of primary and secondary deciduous forests into agricultural land is the main factor driving carbon emissions in the region, which validates the study conducted by Plata-Rocha et al. (2021), which determines that the expansion of the agricultural frontier is the main cause of forest cover loss in the North Pacific Basin.
Regarding the changes that occurred at the municipal level (Figure 3), there was greater deforestation in Badiraguato at 51,067 ha, followed by Culiacán at 39,731 ha, El Fuerte at 35,067 ha, at Sombrerete 28,308 ha, and Tamazula at 26,680 ha. The municipalities with the most degraded forest area were Mezquital 62,434 ha, Guadalupe y Calvo 52,252 ha, Durango 50,676 ha, Guachochi 50,535 ha, and San Dimas 40,760 ha. Continuing with the forest processes that occurred, the municipalities that presented the greatest natural recovery were Mezquital 13, 191 ha, Durango 9, 821 ha, Guadalupe y Calvo 9, 662 ha, Ruiz 8, 778 ha, and Rosario 7, 418 ha. On the other hand, the most representative reforested areas were observed in Santiago Ixcuintla 1,586 ha, Angostura 1,212 ha, Navolato 922 ha, Tuxpan 337 ha, and Ahome 324 ha.
CO2 emissions estimation
The forest processes resulted in both CO2 emissions and absorption. The estimated emission from the deforestation of 597,124 ha was 9,685.28 Gg of CO2e. Most emissions came from changes such as primary deciduous jungle to annual agriculture with 9,065.41 Gg CO2e, primary coniferous forest to grassland with 7,002.51 Gg CO2e, primary coniferous forest to annual agriculture with 4,771.24 Gg CO2e, secondary deciduous jungle to annual agriculture with 4,106.71 Gg CO2e, and primary oak forest to grassland with 1,713.87 Gg CO2e. Forest degradation contributed 1,048.49 Gg of CO2e from 491,285 hectares of degraded land. Influential transitions included primary deciduous jungle to secondary deciduous jungle with 662.93 Gg CO2e, primary oak forest to secondary oak forest with 138.14 Gg CO2e, primary coniferous forest to secondary coniferous forest with 115.99 Gg CO2e, primary woody hydrophilic vegetation to secondary woody hydrophilic vegetation with 55.62 Gg CO2e, and primary deciduous jungle to secondary deciduous jungle with 45.85 Gg CO2e (Table 2).
Regarding the emission and absorption results obtained in the present evaluation period, they indicate that the emission was greater, with an estimate of 10,733.77 Gg of CO2e, than the absorption, with 420.19 Gg of CO2e, with a net balance of 10,313.58 Gg of CO2e.
Discussion
Mexico is among the first 20 countries (FAO, 2020; Hansen et al., 2013) with more significant loss of forest and jungle cover worldwide, and mid the first 50 countries with the highest forest degradation (Pearson et al., 2017).
In this regard, at the national level, the study by Masera et al. (1997), is an important reference point for carbon emission estimates in the mid-1980s in contrast to scenarios for the year 2025. This revealed that 804,000 hectares per year suffered disturbance in Mexico, of which 668,000 hectares were due to deforestation, generating net carbon emissions of 52 million TCO2/year, this already includes carbon gains from reforestation and natural forest recovery. Comparing these results with the present study, we have lower values per unit area, since 9,685 Gg CO2e of emissions were estimated for 597,124 hectares.
Návar-Chaidez (2008) assessed the change in the Tamaulipas forest to other land uses and land cover and determined the significant contribution of CO2 flux, and that deforestation could be responsible for 41% of national emissions in semi-arid ecosystems, which reinforces our results indicating that arid and semi-arid areas contribute significantly by surface area.
Pompa-García and Sigala-Rodríguez (2017) highlighted the lack of comprehensive syntheses on carbon sequestration rates by species in Mexico, which limits the references available for comparative studies. This justifies the significance of our results, as they provide quantitative and spatial data for a specific region of Mexico.
Another study conducted in Brazil by Csillik et al. (2024), indicates that 17% of emissions are the result of direct deforestation, while the remaining 83% come from forest degradation and disturbance, including fires, selective logging, and natural events, highlighting that degradation is the largest source of emissions in this region. In contrast to our results, the opposite occurred, as deforestation was responsible for approximately 90% of emissions, and 10% corresponded to forest degradation.
Globally, forest ecosystems absorb about 9.53 GtCO₂e/year, while deforestation and degradation contribute 5.87 GtCO₂e, leaving a net positive balance (VIRGILIO, 2010). On the other hand, our results show a significant negative net balance (10,313 Gg CO₂e), indicating that locally, the North Pacific Basin is a net source of emissions.
Mature forests tend to act as carbon sinks in the process of storing carbon as above and below-ground biomass (Munawar et al., 2015). As for the estimated carbon emissions of 9,685.28 Gg of CO2e for the 597,124 ha. Our estimates are found in the published by (Pearson et al., 2017) which reports 0 a 5 million Mg CO2 per year of total degradation emissions for the area of this study, associated with 15-50 million Mg CO2e/yr Timber Emissions, which, for Mexico, represents less than 50 Mt CO2/yr of emissions from wood, followed by firewood and fires.
Likewise, Tölgyesi et al. (2025) show that the potential for climate mitigation through ecosystem restoration is insufficient, as it ranges between 3.7% and 17.6% of cumulative human emissions, and that the benefits of forest cover should be considered more in terms of biodiversity and resilience than as a primary climate solution. This demonstrates that reforestation efforts are valuable but must be complemented by conservation, sustainable management, and emissions reduction strategies following recommendations aimed at promoting REDD+ and even more local-administrative management, such as at the municipal level, as indicated in this study.
On the other hand, it is worth mentioning that the most affected municipalities in the basin in terms of forest and jungle loss match the municipalities that are the hot spot of deforestation most critical in the state of Sinaloa by Monjardin-Armenta et al. (2016).
Our results for CO2 emissions and removals in the period 2002-2021 reveal a higher estimate of emissions of 10,733.77 Gg of CO2e, compared to the absorption estimates of 420.16 Gg of CO2e, with a net balance of 10,313.58 Gg of CO2e. The forestry processes that occurred during the period under evaluation were as follows 9,685.28 Gg of CO2e in 597,124 ha, with the largest transition from primary coniferous forest to grassland. This change in cover from forest to pasture causes an 8% increase in soil organic carbon (Guo & Gifford, 2002), while extensive agriculture causes the loss of up to 50% of soil carbon (Andersen et al., 2016). The change from forest cover to agriculture for food production leads to high CO2 emissions rates, similar those reported by Khan et al. (2020).
Degradation and deforestation can be attributed to a variety of causes, such as disease, floods, fires, and storms. However, anthropogenic activities such as illegal logging, agricultural expansion, and lack of government policy enforcement have the greatest impact on CO2 emissions from deforestation (Khan et al., 2020).
In this aspect, we agree with the recommendations made by (Khan et al., 2020), that, to continue with sustainable forest management practices, the REDD+ incentive (Collins et al., 2022; Park and Yeo-Ch, 2012). Should be extended to localities at the municipal level, to obtain carbon credits, as well as to reforest deforested areas, both in municipalities with forest cover and in those with greater agricultural land cover (Park and Yeo-Ch, 2012).
At the municipal level in Mexico, there are indications of difficulties in developing the REDD+ program due to weak local governance, where conflicts have been reported arising from the implementation of policies that do not take into account traditional forms of management or local agrarian governance, as (Klooster and Masera, 2000) points out, community forest management projects have the capacity to mitigate deforestation and increase forest cover. For example, although REDD+ has promoted the creation of inter-municipal coordination boards between municipalities, CONAFOR, NGOs, and communities, these structures remain limited to pilot areas and do not always guarantee equitable participation or community benefits (Amico and Trench, 2016). Furthermore, during the REDD+ preparation phase (2007–2017), serious deficiencies have been documented in the inclusion of rural and indigenous communities, especially, regard to free, prior, and informed consent, thus affecting the legitimacy and effectiveness of national REDD+ policies (Almanza-Alcalde et al., 2022). Therefore, the results of this study serve to plan sustainable forest management, conservation, and increased forest cover in a more appropriate manner, as they quantitatively determine the areas that would be a priority for attention within each municipality.
Limitations
One of the limitations of this study is that, to determine the ADs, we started from the changes determined from the land use and vegetation mapping at a scale of 1:250,000, which does not provide an analysis of uncertainty and validation of cartographic accuracy. This may overestimate carbon emission and sequestration results, although EF/AF were determined with a 95% confidence level. Therefore, the results obtained may vary within a reasonable confidence interval, as the methodologies recommended by the scientific literature and the guidelines of the IPCC and the REDD+ program were followed.
Conclusions
In general, this study identified and spatially quantified the carbon balance associated with deforestation, forest degradation, reforestation, and natural forest recovery of different forest cover types in the North Pacific Basin of Mexico between 2002 and 2021, indicating a net negative balance of 10,313.58 GgCO₂e, which confirms that changes in land use and land cover, particularly the conversion of primary and secondary deciduous forests to agricultural land, are the main driver of carbon emissions in the region. Although reforestation and natural forest recovery processes favored carbon sequestration, their effect was not sufficient to offset the magnitude of emissions from deforestation and forest degradation.
The cross-tabulation matrix helped to evaluate in detail the changes generated in the land-use cover. It also allowed specialists to focus attention on the most critical transitions and facilitate the understanding of the different processes that occur on the land-cover, such as the forest processes. According to this analysis of changes, a map of forest processes was generated, with affected and benefited surfaces determined at the municipal level.
When compared with the few national and international studies available, the results coincide with evidence that deforestation in Mexico continues to be a significant source of greenhouse gases. However, degradation also contributes approximately 10% of total emissions, which is less than that reported in tropical regions such as the Amazon, where degradation can even exceed the impact of deforestation. Therefore, it was generally determined that the North Pacific Basin is a net source of CO2 emissions, which implies integrating policies to promote sustainable forest management and the conservation of forest ecosystems to progress toward climate neutrality.
Acknowledgements
The authors would like to thank SECIHTI and SNII. This research received no external funding.
Authorship Contribution
Project Idea: SAMA.; MAQM.
Funding: SAMA.; MAQM.
Database:SAMA.; MAQM.; EAA.
Processing: SAMA.; MAQM.
Analysis: SAMA.; MAQM.; EAA.; LYPA.
Writing: SAMA.; MAQM.
Validation: SAMA, EAA, LYPA
Review: SAMA.; MAQM.
Data Availability
The datasets analyzed during the current study are available from the corresponding author upon reasonable request.
Competing Interests
The authors declare no competing interests.
ANNEX 1 FOREST LANDS REMAINING AS FOREST LANDS
ANNEX 2 GRASSLANDS THAT REMAIN AS GRASSLANDS
ANNEX 3 LANDS CONVERTED TO FOREST LANDS
ANNEX 4 LANDS CONVERTED TO GRASSLANDS
ANNEX 5 FOREST LANDS REMAINING AS FOREST LANDS
ANNEX 6 FOREST LAND AND GRASSLAND CONVERTED TO OTHER USES
References
-
ACHARD, F.; BEUCHLE, R.; MAYAUX, P.; et al. Determination of tropical deforestation rates and related carbon losses from 1990 to 2010. Global Change Biology, v. 20, n 8, p. 2540–2554, 2014. https://doi.org/10.1111/gcb.12605
» https://doi.org/10.1111/gcb.12605 -
ALMANZA-ALCALDE, H.; SATYAL, P.; CORBERA, E.; et al. Participatory injustice in Mexico’s Readiness process to Reduce Emissions from Deforestation and forest Degradation (REDD +). Human Ecology, v. 50, n.1, p. 79–90, 2022. https://doi.org/10.1007/s10745-021-00280-7
» https://doi.org/10.1007/s10745-021-00280-7 - AMICO, A. L.; TRENCH, T. Bosques y suelos en el contexto de redd+: entre gobierno y gobernanza en México. Terra Latinoamericana, v. 34, n. 1, p. 113–124, 2016.
-
ANDERSEN, L. E.; DOYLE, A. S.; DEL GRANADO, S.; et al. Net Carbon Emissions from Deforestation in Bolivia during 1990-2000 and 2000-2010: Results from a Carbon Bookkeeping Model. PLOS ONE, v. 11, n. 3, p. e0151241, 2016. https://doi.org/10.1371/journal.pone.0151241
» https://doi.org/10.1371/journal.pone.0151241 -
BARTALEV, S. S.; KISSIYAR, O.; ACHARD, F.; et al. Assessment of forest cover in Russia by combining a wall-to-wall coarse-resolution land-cover map with a sample of 30 m resolution forest maps. International Journal of Remote Sensing, v. 35, n.7, p. 2671–2692, 2014. https://doi.org/10.1080/01431161.2014.883099
» https://doi.org/10.1080/01431161.2014.883099 -
BETTS, M. G.; YANG, Z.; HADLEY, A. S.; et al. Quantifying forest degradation requires a long-term, landscape-scale approach. Nature Ecology and Evolution, v. 8, p. 1054–1057, 2024. https://doi.org/10.1038/s41559-024-02409-5
» https://doi.org/10.1038/s41559-024-02409-5 -
CADMAN, T.; MARASENI, T.; MA, H. O.; et al. Five years of REDD+ governance: The use of market mechanisms as a response to anthropogenic climate change. Forest Policy and Economics, v. 79, p. 8–16, 2017. https://doi.org/10.1016/j.forpol.2016.03.008
» https://doi.org/10.1016/j.forpol.2016.03.008 -
CAO, V.; MARGNI, M.; FAVIS, B. D.; et al. Aggregated indicator to assess land use impacts in life cycle assessment (LCA) based on the economic value of ecosystem services. Journal of Cleaner Production, v. 94, p. 56–66, 2015. https://doi.org/10.1016/j.jclepro.2015.01.041
» https://doi.org/10.1016/j.jclepro.2015.01.041 -
COLLINS, A. C.; GROTE, M. N.; CARO, T.; et al. How community forest management performs when REDD+ payments fail. Environmental Research Letters, v. 17, n. 3, p. 1-12, 2022.DOI 10.1088/1748-9326/ac4b54
» https://doi.org/10.1088/1748-9326/ac4b54 -
CONAFOR, SEMARNAT. Inventario Nacional Forestal y de Suelos México 2004-2009. 2009. Available at: https://www.snieg.mx/Documentos/IIN/Acuerdo_3_X/Manual_y_Procedimientos_para_el_Muestreo_de_Campo_INFyS_2004-2009.pdf Accessed in: May 12th 2024.
» https://www.snieg.mx/Documentos/IIN/Acuerdo_3_X/Manual_y_Procedimientos_para_el_Muestreo_de_Campo_INFyS_2004-2009.pdf -
CONAGUA. Programa Hídrico Regional Visión 2030. Región Hidrológico-Administrativa III Pacífico Norte. 2012. Available at: https://sinav30.conagua.gob.mx:8080/Descargas/pdf/RHA%20IX%20GN%20PHR.pdf Accessed in: January 8th 2024.
» https://sinav30.conagua.gob.mx:8080/Descargas/pdf/RHA%20IX%20GN%20PHR.pdf -
CONAGUA. Estadísticas Agrícolas de los Distritos de Riego Año agrícola 2013-2014. 2015. Available at: https://bpo.sep.gob.mx/#/recurso/615 Accessed in: February 11th 2024.
» https://bpo.sep.gob.mx/#/recurso/615 -
CROUZEILLES, R.; FERREIRA, M. S.; CHAZDON, R. L.; et al. Ecological restoration success is higher for natural regeneration than for active restoration in tropical forests. Science Advances, v. 3, n. 11, p. 123-130, 2017. https://doi.org/10.1126/sciadv.1701345
» https://doi.org/10.1126/sciadv.1701345 -
CSILLIK, O.; KELLER, M.; LONGO, M.; et al. A large net carbon loss attributed to anthropogenic and natural disturbances in the Amazon Arc of Deforestation. Proceedings of the National Academy of Sciences, v. 121, n. 33, p. 1-7, 2024. https://doi.org/10.1073/pnas.2310157121
» https://doi.org/10.1073/pnas.2310157121 -
FAO. Global Forest Assessment Resources 2020 Main report. 2020. In Food and Agriculture Organization of the United Nations. Available at: https://doi.org/10.4060/ca9825es Accessed in: March 11th 2024.
» https://doi.org/10.4060/ca9825es -
FRA. Global Forest Resources Assessment 2010. 2010. Available at: http://www.fao.org/3/a-i1757e.pdf Accessed in: March 5th 2024.
» http://www.fao.org/3/a-i1757e.pdf -
FSI. Forest Survey of India. 2018. http://fsi.nic.in/fsi-node
» http://fsi.nic.in/fsi-node -
GAO, J.; GAO, H.; ZHOU, C.; et al. The impact of land-use change on water-related ecosystem services: a study of the Guishui River Basin, Beijing, China. Journal of Cleaner Production, v. 163, 148–155, 2017. https://ui.adsabs.harvard.edu/link_gateway/2017JCPro.163S.148G/doi:10.1016/j.jclepro.2016.01.049
» https://ui.adsabs.harvard.edu/link_gateway/2017JCPro.163S.148G/doi:10.1016/j.jclepro.2016.01.049 -
GRINAND, C.; RAKOTOMALALA, F.; GOND, V.; et al. Estimating deforestation in tropical humid and dry forests in Madagascar from 2000 to 2010 using multi-date Landsat satellite images and the random forests classifier. Remote Sensing of Environment, v. 139, p. 68–80, 2013. https://doi.org/10.1016/j.rse.2013.07.008
» https://doi.org/10.1016/j.rse.2013.07.008 -
GUO, L. B.;GIFFORD, R. M. Soil carbon stocks and land use change: a meta analysis. Global Change Biology, v. 8, n. 4, p. 345–360, 2002. https://doi.org/10.1046/j.1354-1013.2002.00486.x
» https://doi.org/10.1046/j.1354-1013.2002.00486.x -
HANSEN, M. C.; POTAPOV, P. V.; MOORE, R.; et al. High-Resolution Global Maps of 21st-Century Forest Cover Change. Science, v. 342, p. 850–853, 2013. https://doi.org/10.1126/science.1244693
» https://doi.org/10.1126/science.1244693 -
HOSONUMA, N.; HEROLD, M.; DE SY, V.; et al. An assessment of deforestation and forest degradation drivers in developing countries. Environmental Research Letters, v. 7, n. 4, p. 1-12, 2012. DOI 10.1088/1748-9326/7/4/044009
» https://doi.org/10.1088/1748-9326/7/4/044009 -
INECC, Instituto Nacional de Ecología y Cambio Climático; SEMARNAT, Secretaría de Medio Ambiente y Recursos Naturales. 2015. First Biennial Update Report to the United Nations Framework Convention on Climate Change. INECC/Semarnat, México. Available at: https://unfccc.int/sites/default/files/resource/Executive Summary_1.pdf. Accessed in: March 12th 2024.
» https://unfccc.int/sites/default/files/resource/Executive -
INEGI. Encuesta Intercensal 2015. 2016a. Available at: https://www.inegi.org.mx/contenidos/programas/intercensal/2015/doc/eic_2015_presentacion.pdf Accessed in: February 22st 2024.
» https://www.inegi.org.mx/contenidos/programas/intercensal/2015/doc/eic_2015_presentacion.pdf -
INEGI. Uso de suelo y vegetación. Instituto Nacional de Estadística y Geografía. INEGI. 2016b. Available at: http://www.beta.inegi.org.mx/temas/mapas/usosuelo/ Accessed in: February 12th 2024.
» http://www.beta.inegi.org.mx/temas/mapas/usosuelo/ -
INPE. PRODES — Coordenação-Geral de Observação da Terra. 2018. Available at: https://terrabrasilis.dpi.inpe.br/downloads/ Accessed in: January 12th 2024.
» https://terrabrasilis.dpi.inpe.br/downloads/ -
IPCC 2006, 2006 IPCC Guidelines for National Greenhouse Gas Inventories, Prepared by the National Greenhouse Gas Inventories Programme, Eggleston H.S., Buendia L., Miwa K., Ngara T. and Tanabe K. (eds). Published: IGES, Japan. https://www.ipcc-nggip.iges.or.jp/public/2006gl/pdf/4_Volume4/V4_00_Cover.pdf Accessed in: January 17th 2024.
» https://www.ipcc-nggip.iges.or.jp/public/2006gl/pdf/4_Volume4/V4_00_Cover.pdf -
IPCC, Intergovernmental Panel on Climate Change. Climate Change 2007: The Physical Science Basis. Contribution of Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change. Available at: https: http://www.ipcc.ch/publications_and_data/publications_ipcc_fourth_assessment_report_wg1_report_the_physical_science_basis.htm Accessed in: January 25th 2024.
» http://www.ipcc.ch/publications_and_data/publications_ipcc_fourth_assessment_report_wg1_report_the_physical_science_basis.htm -
KEENAN, R. J.; REAMS, G. A.; ACHARD, F.; et al. Dynamics of global forest area: Results from the FAO Global Forest Resources Assessment 2015. Forest Ecology and Management, v. 352, p. 9–20, 2015. https://doi.org/10.1016/j.foreco.2015.06.014
» https://doi.org/10.1016/j.foreco.2015.06.014 -
KHAN, K.; IQBAL; ALI; KHAN, S. N. Assessment of sentinel-2-derived vegetation indices for the estimation of above-ground biomass/carbon stock, temporal deforestation and carbon emissions estimation in the moist temperate forests of Pakistan. Applied ecology and environmental research, v. 18, n. 1, p. 783-815, 2020. https://doi.org/10.15666/aeer/1801_783815
» https://doi.org/10.15666/aeer/1801_783815 -
KLOOSTER, D.; MASERA, O. Community forest management in Mexico: carbon mitigation and biodiversity conservation through rural development. Global Environmental Change, v. 10, n. 4, p. 259–272, 2000. https://doi.org/10.1016/S0959-3780(00)00033-9
» https://doi.org/10.1016/S0959-3780(00)00033-9 - LEAL ESPER, Y. E. La contaminación ambiental y su influencia en los ecosistemas de páramo. Revista Academia y Derecho, v. 12, p. 1-16, 2021.
-
MASERA, O. R.; ORDÓÑEZ, M. J.; DIRZO, R. Carbon emissions from Mexican forests: Current situation and long-term scenarios. Climatic Change, v. 35, n. 3, p. 265–295, 1997. https://doi.org/10.1023/A:1005309908420
» https://doi.org/10.1023/A:1005309908420 -
MONJARDIN-ARMENTA, S. A.; PACHECO-ANGULO, C. E.; PLATA-ROCHA, W.; et al. Determination and Analysis of Hot Spot Areas of Deforestation Using Remote Sensing and Geographic Information System Techniques. Case Study: State Sinaloa, Mexico. Open Journal of Forestry, v. 6, n. 4, p. 295–304, 2016. http://dx.doi.org/10.4236/ojf.2016.64024
» http://dx.doi.org/10.4236/ojf.2016.64024 -
MUNAWAR, S.; KHOKHAR, M. F.; ATIF, S. Reducing emissions from deforestation and forest degradation implementation in northern Pakistan. International Biodeterioration & Biodegradation, v. 102, p. 316–323, 2015. http://dx.doi.org/10.1016/j.ibiod.2015.02.027
» http://dx.doi.org/10.1016/j.ibiod.2015.02.027 -
NATHAN, I.; PASGAARD, M. Is REDD+ effective, efficient, and equitable? Learning from a REDD+ project in Northern Cambodia. Geoforum, v, 83, p. 26–38, 2017. https://doi.org/10.1016/j.geoforum.2017.04.020
» https://doi.org/10.1016/j.geoforum.2017.04.020 -
NÁVAR-CHAIDEZ, J. Carbon fluxes resulting from land-use changes in the Tamaulipan thornscrub of northeastern Mexico. Carbon Balance and Management, v. 3, n.1, p. 1–11, 2008. https://doi.org/10.1186/1750-0680-3-6
» https://doi.org/10.1186/1750-0680-3-6 -
OCA, A. I. F. M.; GALLARDO-CRUZ, J. A.; GHILARDI, A.; et al. An integrated framework for harmonizing definitions of deforestation. Environmental Science & Policy, v. 115, p. 71–78, 2021.https://doi.org/10.1016/j.envsci.2020.10.007
» https://doi.org/10.1016/j.envsci.2020.10.007 -
PACHECO-ANGULO, C.; VILANOVA, E.; AGUADO, I.; et al. Carbon Emissions from Deforestation and Degradation in a Forest Reserve in Venezuela between 1990 and 2015. Forests, v. 8, n. 8, p. 1-22, 2017. https://doi.org/10.3390/f8080291
» https://doi.org/10.3390/f8080291 -
PAN, Y.; BIRDSEY, R. A.; PHILLIPS, O. L.; et al. The enduring world forest carbon sink. Nature, v. 631, p. 563–569, 2024. https://doi.org/10.1038/s41586-024-07602-x
» https://doi.org/10.1038/s41586-024-07602-x -
PARK, M.; YOUN, Y. Development of South Korean REDD+ Strategies for Forest Carbon Credits. Journal of Environmental Policy and Administration, v. 20, n. 2, p. 19–48, 2012. https://doi.org/10.15301/jepa.2012.06.20.2.19
» https://doi.org/10.15301/jepa.2012.06.20.2.19 -
PEARSON, T. R. H.; BROWN, S.; MURRAY, L.; et al. Greenhouse gas emissions from tropical forest degradation: an underestimated source. Carbon Balance and Management, v. 12, n. 3, p.1-11, 2017. https://doi.org/10.1186/s13021-017-0072-2
» https://doi.org/10.1186/s13021-017-0072-2 -
PLATA-ROCHA, W.; MONJARDIN-ARMENTA, S. A.; PACHECO-ANGULO, C. E.; et al. Proximate and Underlying Deforestation Causes in a Tropical Basin through Specialized Consultation and Spatial Logistic Regression Modeling. Land , v. 10, n. 186, p. 1-18, 2021. https://doi.org/10.3390/land10020186
» https://doi.org/10.3390/land10020186 -
POMPA-GARCÍA, M.; SIGALA-RODRÍGUEZ, J. Á. Variación de captura de carbono de especies forestales en México: una revisión. Madera y Bosques, v. 23, n. 2, p. 225–235, 2017. https://doi.org/10.21829/myb.2017.2321512
» https://doi.org/10.21829/myb.2017.2321512 -
PONTIUS, R. G.; SHUSAS, E.; MCEACHERN, M. Detecting important categorical land changes while accounting for persistence. Agriculture, Ecosystems & Environment, v. 101, n. 2, p. 251–268, 2004. https://doi.org/10.1016/j.agee.2003.09.008
» https://doi.org/10.1016/j.agee.2003.09.008 -
PULLANIKKATIL, D.; PALAMULENI, L. G.; RUHIIGA, T. M. Land use/land cover change and implications for ecosystems services in the Likangala River Catchment, Malawi. Physics and Chemistry of the Earth, Parts A/B/C, v. 93, p. 96–103, 2016. https://doi.org/10.1016/j.pce.2016.03.002
» https://doi.org/10.1016/j.pce.2016.03.002 - ROSETE-VERGÉS, F. A.; PÉREZ-DAMIÁN, J. L.; VILLALOBOS-DELGADO, M.; et al.. El avance de la deforestación en México 1976-2007. Madera y Bosques, v. 20, n. 1, p. 21–35, 2014.
-
SKUTSCH, M.; MAS, J. F.; BOCCO, G.; et al. Deforestation and land tenure in Mexico: A response to Bonilla-Moheno et al. Land Use Policy, v. 39, p. 390–396, 2014. https://doi.org/10.1016/j.landusepol.2013.11.013
» https://doi.org/10.1016/j.landusepol.2013.11.013 -
TOLESSA, T.; SENBETA, F.; KIDANE, M. The impact of land use/land cover change on ecosystem services in the central highlands of Ethiopia. Ecosystem Services, v. 23, p. 47–54, 2017. https://doi.org/10.1016/j.ecoser.2016.11.010
» https://doi.org/10.1016/j.ecoser.2016.11.010 -
TÖLGYESI, C.; CSIKÓS, N.; TEMPERTON, V. M.; et al. Limited carbon sequestration potential from global ecosystem restoration. Nature Geoscience, v. 18, n. 8, p. 761–768, 2010. https://doi.org/10.1038/s41561-025-01742-z
» https://doi.org/10.1038/s41561-025-01742-z -
UNFCC, Conference of the Parties (COP). Report of the Conference of the Parties on its sixteenth session, held in Cancun from 29 November to 10 December 2010. 2011. Part one: Proceedings. Available at: https://unfccc.int/resource/docs/2010/cop16/eng/07a01.pdf Accessed in: April 21st 2024.
» https://unfccc.int/resource/docs/2010/cop16/eng/07a01.pdf -
VALDEZ PÉREZ, M. E.; OROZCO HERNÁNDEZ, M. E.; ROMERO-SALAZAR, L.; et al. Analysis Of Land Use Changes And Carbon Content In Forest Covers In The State Of Mexico With Spatial And Stochastic Methods. Journal of Geography and Earth Sciences, v. 3, n. 2, p. 33–49, 2015. https://doi.org/10.15640/jges.v3n2a2
» https://doi.org/10.15640/jges.v3n2a2 -
VIRGILIO, N. R.; MARSALL, S.; ZERBOCK, O.; et al. Reducing Emissions from Deforestation and Degradation (REDD): A Casebook of On-the-Ground Experience. 2010. The Nature Conservancy, Conservation International and Wildlife Conservation Society. Arlington, Virginia, 2010, 67p. Available at: https://www.nature.org/media/climatechange/redd-casebook-tnc-ci-wcs.pdf?utm_source=chatgpt.com Accessed in: January 21st 2024.
» https://www.nature.org/media/climatechange/redd-casebook-tnc-ci-wcs.pdf?utm_source=chatgpt.com -
ZHANG, F.; ZHAN, J.; ZHANG, Q.; et al. Impacts of land use/cover change on terrestrial carbon stocks in Uganda. Physics and Chemistry of the Earth, Parts A/B/C, v. 101, p. 195–203, 2017. https://doi.org/10.1016/j.pce.2017.03.005
» https://doi.org/10.1016/j.pce.2017.03.005
-
Scientific Editor:
Luciano Cavalcante de Jesus França
