An assessment of monitoring requirements and costs of 'Reduced Emissions from Deforestation and Degradation'

The association of birthweight with fine particle exposure is modifiable by source sector: Findings from a cross-sectional study of 17 low- and middle-income countries

BACKGROUND

Low birthweight attributable to fine particulate matter (PM_2.5) exposure is a global issue affecting infant health, especially in low- and middle-income countries (LMICs). However, large-population studies of multiple LMICs are lacking, and little is known about whether the source of PM_2.5 is a determinant of the toxic effect on birthweight.

OBJECTIVE

We examined the effect on birthweight of long-term exposure to PM_2.5 from different sources in LMICs.

METHODS

The birthweights of 53,449 infants born between September 16, 2017 and September 15, 2018 in 17 LMICs were collected from demographic and health surveys. Long-term exposure to PM_2.5 in 2017 produced by 20 different sources was estimated by combining chemical transport model simulations with satellite-based concentrations of total mass. Generalized linear regression models were used to investigate the associations between birthweight and each source-specific PM_2.5 exposure. A multiple-pollutant model with a ridge penalty on the coefficients of all 20-source-specific components was employed to develop a joint exposure-response function (JERF) of the PM_2.5 mixtures. The estimated JERF was then used to quantify the global burden of birthweight reduction attributable to PM_2.5 mixtures and to PM_2.5 from specific sources.

RESULTS

The fully adjusted single-pollutant model indicated that exposure to a 10 μg/m³ increase in total PM_2.5 was significantly associated with a -6.6 g (95% CI -11.0 to -2.3) reduction in birthweight. In single- and multiple-pollutant models, significant birthweight changes were associated with exposure to PM_2.5 produced by international shipping (SHP), solvents (SLV), agricultural waste burning (GFEDagburn), road transportation (ROAD), waste handling and disposal (WST), and windblown dust (WDUST). Based on the global average exposure to PM_2.5 mixtures, the JERF showed that the overall change in birthweight could mostly be attributed to PM_2.5 produced by ROAD (-37.7 g [95% CI -49.2 to -24.4] for a global average exposure of 2.2 μg/m³), followed by WST (-27.5 g [95% CI -42.6 to -10.7] for a 1.6-μg/m³ exposure), WDUST (-19.5 g [95% CI -26.7 to -12.6] for a 8.6-μg/m³ exposure), and SHP (-19.0 g [95% CI -32.3 to -5.7] for a 0.2-μg/m³ exposure), which, with the exception of WDUST, are anthropogenic sources. The changes in birthweight varied geographically and were co-determined by the concentration as well as the source profile of the PM_2.5 mixture.

CONCLUSION

PM_2.5 exposure is associated with a reduction in birthweight, but our study shows that the magnitude of the association differs depending on the PM_2.5 source. A source-targeted emission-control strategy that considers local features is therefore critical to maximize the health benefits of air quality improvement, especially with respect to promoting maternal and child health.

Integrated Segmentation Approach with Machine Learning Classifier in Detecting and Mapping Post Selective Logging Impacts Using UAV Imagery

Selective logging can cause significant impacts on the residual stands, affecting biodiversity and leading to environmental changes. Proper monitoring and mapping of the impacts from logging activities, such as the stumps, felled logs, roads, skid trails, and forest canopy gaps, are crucial for sustainable forest management operations. The purpose of this study is to assess the indicators of selective logging impacts by detecting the individual stumps as the main indicators, evaluating the performance of classification methods to assess the impacts and identifying forest gaps from selective logging activities. The combination of forest inventory field plots and unmanned aerial vehicle (UAV) RGB and overlapped imaged were used in this study to assess these impacts. The study area is located in Ulu Jelai Forest Reserve in the central part of Peninsular Malaysia, covering an experimental study area of 48 ha. The study involved the integration of template matching (TM), object-based image analysis (OBIA), and machine learning classification—support vector machine (SVM) and artificial neural network (ANN). Forest features and tree stumps were classified, and the canopy height model was used for detecting forest canopy gaps in the post selective logging region. Stump detection using the integration of TM and OBIA produced an accuracy of 75.8% when compared with the ground data. Forest classification using SVM and ANN methods were adopted to extract other impacts from logging activities such as skid trails, felled logs, roads and forest canopy gaps. These methods provided an overall accuracy of 85% and kappa coefficient value of 0.74 when compared with conventional classifier. The logging operation also caused an 18.6% loss of canopy cover. The result derived from this study highlights the potential use of UAVs for efficient post logging impact analysis and can be used to complement conventional forest inventory practices.

Cell therapy in patients with COVID-19 using Wharton's jelly mesenchymal stem cells: a phase 1 clinical trial

BACKGROUND

Mesenchymal stem cells (MSCs) have received particular attention because of their ability to modulate the immune system and inhibit inflammation caused by cytokine storms due to SARS-CoV-2. New alternative therapies may reduce mortality rates in patients with COVID19. This study aimed to assess the safety and efficacy of injecting intravenous Wharton's jelly-derived MSCs in patients with COVID-19 as a treatment.

METHODS

In this study, five patients with severe COVID-19 were treated with Wharton's jelly-derived mesenchymal stem cells (150 × 106 cells per injection). These patients were subject to three intravenous injections 3 days apart, and monitoring was done on days 0, 3, 6, and 14 in routine tests, inflammatory cytokines, and flow cytometry of CD4 and CD8 markers. A lung CT scan was performed on base and days 14 and 28. In addition, IgM and IgG antibodies against SARS-CoV-2 were measured before and after treatment.

RESULTS

The results showed that IL-10 and SDF-1 increased after cell therapy, but VEGF, TGF-β, IFN-γ, IL-6, and TNFα decreased. Routine hematology tests, myocardial enzyme tests, biochemical tests, and inflammation tests were performed for all patients before and after cell therapy on base and days 3, 6, and 14, which indicated the improvement of test results over time. COVID-19 antibody tests rose in 14 days after WJ-MSC injection. The total score of zonal involvement in both lungs was improved.

CONCLUSIONS

In patients, the trend of tests was generally improving, and we experienced a reduction in inflammation. No serious complications were observed in patients except the headache in one of them, which was resolved without medication. In this study, we found that patients with severe COVID-19 in the inflammatory phase respond better to cell therapy. More extensive clinical trials should be performed in this regard.

TRIAL REGISTRATION

IRCT, IRCT20190717044241N2 . Registered April 22, 2020.

Lycopene sensitizes the cervical cancer cells to cisplatin via targeting nuclear factor- kappa B (NF-κB) pathway

Background/aim

Lycopene is associated with anticancer effects in various tumor types. However, the exact underlying mechanisms of action of lycopene in human cervical cancer remain to be determined. This study aimed to determine anticancer efficacy and mechanism of lycopene in human cervical carcinoma (HeLa) cells.

Materials and methods

HeLa cells were treated with cisplatin (1 μM) alone, lycopene (10 μM) alone, and in combination for 72 h. The cell viability of HeLa cells was assessed via MTS assay. Western blot was used to analyze the expression levels of the nuclear factor-kappa B (NF-κB), B-cell-associated X protein (Bax), nuclear factor erythroid 2-related factor (Nrf2), and B-cell lymphoma 2 (Bcl-2).

Results

We found that lycopene acts as a synergistic agent with cisplatin in preventing the growth of HeLa cells. The rates of HeLa cells’ viability were 65.6% and 71.1% with lycopene and cisplatin treatment alone compared to the control group, respectively (P < 0.001). The inhibitory effect of cisplatin was enhanced with lycopene addition by declining the cell viability to 37.4% (P < 0.0001). Lycopene treatment significantly increased Bax expression (P < 0.0001) and decreased Bcl-2 expression (P < 0.0001) in HeLa cells. Furthermore, lycopene markedly activated the Nrf2 expression (P < 0.001) and suppressed the NF-κB signaling pathway (P < 0.0001).

Conclusion

Lycopene increases the sensitization of cervical cancer cells to cisplatin via inhibition of cell viability, up-regulation of Bax expression, and down-regulation of Bcl-2 expression. Furthermore, the anticancer effect of lycopene might be also associated with suppression of NF-κB-mediated inflammatory responses, and modulation of Nrf2-mediated oxidative stress. The results of the present study suggest that lycopene and concurrent cisplatin chemotherapy might have a role in improving the treatment of cervical cancer.

Tissue Plasminogen Activator Loaded PCL Nanofibrous Scaffold Promoted Nerve Regeneration After Sciatic Nerve Transection in Male Rats

According to the studies, damages to the peripheral nerve as a result of a trauma or acute compression, stretching, or burns accounts for a vast range of discomforts which strongly impressed the patient's life quality. Applying highly potent biomolecules and growth factors in the damaged nerve site would promote the probability of nerve regeneration and functional recovery. Tissue plasminogen activator (tPA) is one of the components that can contribute importantly to degenerating and regenerating the peripheral nerves following the injuries occurred and the absence of this biomolecule hinders the recoveries of the nerves. This technique would guarantee the direct accessibility of tPA for the regenerating axons. Structural, physical, and in vitro cytotoxicity evaluations were done before in vivo experiments. In this study, twenty-four mature male rats have been exploited. The rats have been classified into four groups: controls, axotomy, axotomy + scaffold, and axotomy + tPA-loaded scaffold. Four, 8, and 12 weeks post-surgical, the sciatic functional index (SFI) has been measured. After 12 weeks, the spinal cord, sciatic nerve, and dorsal root ganglion specimens have been removed and stereological procedures, immunohistochemistry, and gene expression have been used to analyze them. Stereological parameters, immunohistochemistry of GFAP, and gene expression of S100, NGF, and BDNF were significantly enhanced in tPA-loaded scaffold group compared with axotomy group. The most similarity was observed between the results of control group and tPA-loaded scaffold group. According to the results, a good regeneration of the functional nerve tissues in a short time was observed as a result of introducing tPA.

Agriculture, dairy and fishery farming practices and greenhouse gas emission footprint: a strategic appraisal for mitigation

Rising global population would force farmers to amplify food production substantially in upcoming 3-4 decades. The easiest way to increase grain production is through expanding cropping area by clearing uncultivated land. This is attained by permitting deadly loss of carbon (C) stocks, jeopardizing ecosystem biodiversity and deteriorating environmental quality. We aim to propose key agronomical tactics, livestock management strategy and advance approaches for aquaculture to increase productivity and simultaneously reduce the environmental impacts of farming sector. For this, we considered three major sectors of farming, i.e. agriculture, fishery and dairy. We collected literatures stating approaches or technologies that could reduce GHG emission from these sectors. Thereafter, we synthesized strategies or options that are more feasible and accessible for inclusion in farm sector to reduce GHG emission. Having comprehensively reviewed several publications, we propose potential strategies to reduce GHG emission. Agronomic practices like crop diversification, reducing summer fallow, soil organic carbon sequestration, tillage and crop residue management and inclusion of N₂-fixing pulses in crop rotations are some of those. Livestock management through changing animals' diets, optimal use of the gas produced from manures, frequent and complete manure removal from animal housing and aquaculture management strategies to improve fish health and improve feed conversion efficiency could reduce their GHG emission footprint too. Adapting of effective and economic practices GHG emission footprint reduction potential of farming sector could make farming sector a C neutral enterprise. To overcome the ecological, technological and institutional barriers, policy on trade, tax, grazing practice and GHG pricing should be implemented properly.

Similarities and Differences between International REDD+ and Transnational Deforestation-Free Supply Chain Initiatives—A Review

After years of multilateral deliberations on how to stop global deforestation, such as REDD+ under the UNFCCC, deforestation-free supply chain (DFSC) initiatives emerged from the private sector. Linking both concepts conceptually and in policy practice could provide for synergies and enable more effective approaches against global deforestation. To operationalise such a linkage, a prerequisite is the knowledge of both concepts’ key characteristics, as well as resulting similarities and differences. This literature review firstly identifies key characteristics that affects the potential impact of such concepts, secondly analyses if and how REDD+ and DFSC define these characteristics, and thirdly compares both concepts towards a potential linkage. The results show that a linkage of REDD+ and DFSC provides numerous complementarities which could foster the goal of halting deforestation. This includes for example the driver commercial agriculture, and in terms of permanence, leakage, and degradation. But close coordination is needed to avoid unintended negative consequences, especially for subsistence and smallholder agriculture. The comparison shows that the political consensus found under REDD+ provides a good basis to be supplemented with private sectors’ DFSC initiatives, but additional initiatives like the Bonn Challenge and investments in agroforestry are needed in order to ensure the long-term effect on forest conversion.

Time-series maps of aboveground biomass in dipterocarps forests of Malaysia from PALSAR and PALSAR-2 polarimetric data

BACKGROUND

Malaysia typically suffers from frequent cloud cover, hindering spatially consistent reporting of deforestation and forest degradation, which limits the accurate reporting of carbon loss and CO₂ emissions for reducing emission from deforestation and forest degradation (REDD+) intervention. This study proposed an approach for accurate and consistent measurements of biomass carbon and CO₂ emissions using a single L-band synthetic aperture radar (SAR) sensor system. A time-series analysis of aboveground biomass (AGB) using the PALSAR and PALSAR-2 systems addressed a number of critical questions that have not been previously answered. A series of PALSAR and PALSAR-2 mosaics over the years 2007, 2008, 2009, 2010, 2015 and 2016 were used to (i) map the forest cover, (ii) quantify the rate of forest loss, (iii) establish prediction equations for AGB, (iv) quantify the changes of carbon stocks and (v) estimate CO₂ emissions (and removal) in the dipterocarps forests of Peninsular Malaysia.

RESULTS

This study found that the annual rate of deforestation within inland forests in Peninsular Malaysia was 0.38% year^-1 and subsequently caused a carbon loss of approximately 9 million Mg C year^-1, which is equal to emissions of 33 million Mg CO₂ year^-1, within the ten-year observation period. Spatially explicit maps of AGB over the dipterocarps forests in the entire Peninsular Malaysia were produced. The RMSE associated with the AGB estimation was approximately 117 Mg ha^-1, which is equal to an error of 29.3% and thus an accuracy of approximately 70.7%.

CONCLUSION

The PALSAR and PALSAR-2 systems offer a great opportunity for providing consistent data acquisition, cloud-free images and wall-to-wall coverage for monitoring since at least the past decade. We recommend the proposed method and findings of this study be considered for MRV in REDD+ implementation in Malaysia.

Estimation of Forest Area and Canopy Cover Based on Visual Interpretation of Satellite Images in Ethiopia

Forests, particularly in the tropics, are suffering from deforestation and forest degradations. The estimation of forest area and canopy cover is an essential part of the establishment of a measurement, reporting, and verification (MRV) system that is needed for monitoring carbon stocks and the associated greenhouse gas emissions and removals. Information about forest area and canopy cover might be obtained by visual image interpretation as an alternative to expensive fieldwork. The objectives of this study were to evaluate different types of satellite images for forest area and canopy cover estimation though visual image interpretation, and assess the influence of sample sizes on the estimates. Seven sites in Ethiopia with different vegetation systems were subjectively identified, and visual interpretations were carried out in a systematical design. Bootstrapping was applied to evaluate the effects of sample sizes. The results showed that high-resolution satellite images (≤5 m) (PlanetScope and RapidEye) images produced very similar estimates, while coarser resolution imagery (10 m, Sentinel-2) estimates were dependent on forest conditions. Estimates based on Sentinel-2 images varied significantly from the two other types of images in sites with denser forest cover. The estimates from PlanetScope and RapidEye were less sensitive to changes in sample size.

Uncertainty in Estimates, Incentives, and Emission Reductions in REDD+ Projects

The accurate monitoring and measurement of emission reductions is a critical step in Reducing Emissions from Deforestation and Degradation (REDD+). However, the existence of uncertainty in emission reduction estimates affects the performance of REDD+ projects. We assert that incentive could be a valuable policy tool for reducing monitoring errors and transaction costs. Using Stackelberg models and simulation research, this paper examines the effects of uncertainty and incentive on performance and stakeholder benefits of REDD+ projects. Finally, the uncertainties in REDD+ projects are further discussed, and equilibrium errors, emission reductions, and stakeholder benefits in different scenarios are compared. The results show that errors do affect the measured value of carbon emissions and compensation payments. However, incentive for investors can reduce monitoring errors and improve the performance of REDD+ projects. Therefore, in the future, incentive should be provided to investors rather than landholders.

Current remote sensing approaches to monitoring forest degradation in support of countries measurement, reporting and verification (MRV) systems for REDD

Forest degradation is a global phenomenon and while being an important indicator and precursor to further forest loss, carbon emissions due to degradation should also be accounted for in national reporting within the frame of UN REDD+. At regional to country scales, methods have been progressively developed to detect and map forest degradation, with these based on multi-resolution optical, synthetic aperture radar (SAR) and/or LiDAR data. However, there is no one single method that can be applied to monitor forest degradation, largely due to the specific nature of the degradation type or process and the timeframe over which it is observed. The review assesses two main approaches to monitoring forest degradation: first, where detection is indicated by a change in canopy cover or proxies, and second, the quantification of loss (or gain) in above ground biomass (AGB). The discussion only considers degradation that has a visible impact on the forest canopy and is thus detectable by remote sensing. The first approach encompasses methods that characterise the type of degradation and track disturbance, detect gaps in, and fragmentation of, the forest canopy, and proxies that provide evidence of forestry activity. Progress in these topics has seen the extension of methods to higher resolution (both spatial and temporal) data to better capture the disturbance signal, distinguish degraded and intact forest, and monitor regrowth. Improvements in the reliability of mapping methods are anticipated by SAR-optical data fusion and use of very high resolution data. The second approach exploits EO sensors with known sensitivity to forest structure and biomass and discusses monitoring efforts using repeat LiDAR and SAR data. There has been progress in the capacity to discriminate forest age and growth stage using data fusion methods and LiDAR height metrics. Interferometric SAR and LiDAR have found new application in linking forest structure change to degradation in tropical forests. Estimates of AGB change have been demonstrated at national level using SAR and LiDAR-assisted approaches. Future improvements are anticipated with the availability of next generation LiDAR sensors. Improved access to relevant satellite data and best available methods are key to operational forest degradation monitoring. Countries will need to prioritise their monitoring efforts depending on the significance of the degradation, balanced against available resources. A better understanding of the drivers and impacts of degradation will help guide monitoring and restoration efforts. Ultimately we want to restore ecosystem service and function in degraded forests before the change is irreversible.

Effect of Uncertainties in Estimated Carbon Reduction from Deforestation and Forest Degradation on Required Incentive Payments in Developing Countries

For reducing emissions from deforestation and forest degradation (REDD+) programs, it is particularly important that monitoring for emission reductions is tied to the revenues a developing country receives from REDD+ projects; any estimated uncertainties will have significant impacts on the emission reduction estimation and incentive scheme of REDD+. However, the effects of estimated uncertainties on incentives for developing countries have not been deeply discussed in the current literature. To fill this gap, two estimation approaches for emission reductions are introduced by considering the incentive coefficient by the principle of reliable minimum estimation. The relationship between estimated uncertainties and incentive coefficient is simulated to illustrate the effects of estimated uncertainties on the emission reduction estimation and incentive scheme. Data from six tropical developing countries are used, including Nigeria, Honduras, Indonesia, Cambodia, Ghana, and Brazil. The results indicate that both the errors of referential and actual carbon stock must be considered when estimating and predicting emission reductions. The effects of the error of actual carbon stock on the emission reduction estimation and incentive coefficient were determined to be more influential. The current incentive scheme was more favorable to developing countries with high carbon stock variability, while developing countries with low carbon stock variability had insufficient incentives to implement REDD+ project.

Using fragmentation to assess degradation of forest edges in Democratic Republic of Congo

BACKGROUND

Recent studies have shown that fragmentation is an increasing threat to global forests, which has major impacts on biodiversity and the important ecosystem services provided by forested landscapes. Several tools have been developed to evaluate global patterns of fragmentation, which have potential applications for REDD+. We study how canopy height and above ground biomass (AGB) change across several categories of forest edges determined by fragmentation analysis. We use Democratic Republic of Congo (DRC) as an example.

RESULTS

An analysis of variance of different edge widths and airborne estimated canopy height found that canopy heights were significantly different in forest edges at a distance of 100 m from the nonforest edge. Biomass was significantly different between fragmentation classes at an edge distance of 300 m. Core forest types were found to have significantly higher canopy height and greater AGB than forest edges and patches, where height and biomass decrease significantly as the level of fragmentation increases. A change analysis shows that deforestation and degradation are increasing over time and biomass loss associated with degradation account for at least one quarter of total loss. We estimate that about 80 % of primary forests are intact, which decreases 3.5 % over the 15 year study period, as primary forest is either deforested or transitioned to forest edge. While the carbon loss per hectare is lower than that of deforestation, degradation potentially affects up to three times more area than deforestation alone.

CONCLUSIONS

When defining forest degradation by decreased biomass without any loss in forest area, assessing transitions of core forest to edges over time can contribute an important element to REDD+MRV systems. The estimation of changes between different forest fragmentation types and their associated biomass loss can provide an estimate of degradation carbon emission factors. Forest degradation and emissions due to fragmentation are often underestimated and should comprise an essential component of MRV systems.

Indirect approach for estimation of forest degradation in non-intact dry forest: modelling biomass loss with Tweedie distributions

BACKGROUND

Implementation of REDD+ requires measurement and monitoring of carbon emissions from forest degradation in developing countries. Dry forests cover about 40 % of the total tropical forest area, are home to large populations, and hence often display high disturbance levels. They are susceptible to gradual but persistent degradation and monitoring needs to be low cost due to the low potential benefit from carbon accumulation per unit area. Indirect remote sensing approaches may provide estimates of subsistence wood extraction, but sampling of biomass loss produces zero-inflated continuous data that challenges conventional statistical approaches. We introduce the use of Tweedie Compound Poisson distributions from the exponential dispersion family with Generalized Linear Models (CPGLM) to predict biomass loss as a function of distance to nearest settlement in two forest areas in Tanzania.

RESULTS

We found that distance to nearest settlement is a valid proxy variable for prediction of biomass loss from fuelwood collection (p < 0.001) and total subsistence wood extraction (p < 0.01). Biomass loss from commercial charcoal production did not follow a spatial pattern related to settlements.

CONCLUSIONS

Distance to nearest settlement seems promising as proxy variable for estimation of subsistence wood extraction in dry forests in Tanzania. Tweedie GLM provided valid parameters from the over-dispersed continuous biomass loss data with exact zeroes, and observations with zero biomass loss were successfully included in the model parameters.

Co-benefits, trade-offs, barriers and policies for greenhouse gas mitigation in the agriculture, forestry and other land use (AFOLU) sector

The agriculture, forestry and other land use (AFOLU) sector is responsible for approximately 25% of anthropogenic GHG emissions mainly from deforestation and agricultural emissions from livestock, soil and nutrient management. Mitigation from the sector is thus extremely important in meeting emission reduction targets. The sector offers a variety of cost-competitive mitigation options with most analyses indicating a decline in emissions largely due to decreasing deforestation rates. Sustainability criteria are needed to guide development and implementation of AFOLU mitigation measures with particular focus on multifunctional systems that allow the delivery of multiple services from land. It is striking that almost all of the positive and negative impacts, opportunities and barriers are context specific, precluding generic statements about which AFOLU mitigation measures have the greatest promise at a global scale. This finding underlines the importance of considering each mitigation strategy on a case-by-case basis, systemic effects when implementing mitigation options on the national scale, and suggests that policies need to be flexible enough to allow such assessments. National and international agricultural and forest (climate) policies have the potential to alter the opportunity costs of specific land uses in ways that increase opportunities or barriers for attaining climate change mitigation goals. Policies governing practices in agriculture and in forest conservation and management need to account for both effective mitigation and adaptation and can help to orient practices in agriculture and in forestry towards global sharing of innovative technologies for the efficient use of land resources. Different policy instruments, especially economic incentives and regulatory approaches, are currently being applied however, for its successful implementation it is critical to understand how land-use decisions are made and how new social, political and economic forces in the future will influence this process.

Forest biodiversity monitoring for REDD+: a case study of actors' views in Peru

The climate change mitigation mechanism Reducing Emissions from Deforestation and Forest Degradation in developing countries (REDD+) is currently being negotiated under the United Nations Framework Convention on Climate Change (UNFCCC). Integrating biodiversity monitoring into REDD+ facilitates compliance with the safeguards stipulated by the UNFCCC to exclude environmental risks. Interviews with actors engaged in REDD+ implementation and biodiversity conservation at the national and sub-national level in Peru (n = 30) and a literature review (n = 58) were conducted to pinpoint constraints and opportunities for monitoring effects of REDD+ management interventions on biodiversity, and to identify relevant biodiversity data and indicators. It was found that particularly sub-national actors, who were frequently involved in REDD+ pilot projects, acknowledge the availability of biodiversity data. Actors at both the national and sub-national levels, however, criticized data gaps and data being scattered across biodiversity research organizations. Most of the literature reviewed (78 %) included indicators on the state of certain biodiversity aspects, especially mammals. Indicators for pressure on biodiversity, impacts on environmental functions, or policy responses to environmental threats were addressed less frequently (31, 21, and 10 %, respectively). Integrating biodiversity concerns in carbon monitoring schemes was considered to have potential, although few specific examples were identified. The involvement of biodiversity research organizations in sub-national REDD+ activities enhances monitoring capacities. It is discussed how improvements in collaboration among actors from the project to the national level could facilitate the evaluation of existing information at the national level. Monitoring changes in ecosystem services may increase the ecological and socioeconomic viability of REDD+.

Implications of sampling design and sample size for national carbon accounting systems

BACKGROUND

Countries willing to adopt a REDD regime need to establish a national Measurement, Reporting and Verification (MRV) system that provides information on forest carbon stocks and carbon stock changes. Due to the extensive areas covered by forests the information is generally obtained by sample based surveys. Most operational sampling approaches utilize a combination of earth-observation data and in-situ field assessments as data sources.

RESULTS

We compared the cost-efficiency of four different sampling design alternatives (simple random sampling, regression estimators, stratified sampling, 2-phase sampling with regression estimators) that have been proposed in the scope of REDD. Three of the design alternatives provide for a combination of in-situ and earth-observation data. Under different settings of remote sensing coverage, cost per field plot, cost of remote sensing imagery, correlation between attributes quantified in remote sensing and field data, as well as population variability and the percent standard error over total survey cost was calculated. The cost-efficiency of forest carbon stock assessments is driven by the sampling design chosen. Our results indicate that the cost of remote sensing imagery is decisive for the cost-efficiency of a sampling design. The variability of the sample population impairs cost-efficiency, but does not reverse the pattern of cost-efficiency of the individual design alternatives.

CONCLUSIONS, BRIEF SUMMARY AND POTENTIAL IMPLICATIONS

Our results clearly indicate that it is important to consider cost-efficiency in the development of forest carbon stock assessments and the selection of remote sensing techniques. The development of MRV-systems for REDD need to be based on a sound optimization process that compares different data sources and sampling designs with respect to their cost-efficiency. This helps to reduce the uncertainties related with the quantification of carbon stocks and to increase the financial benefits from adopting a REDD regime.

On fair, effective and efficient REDD mechanism design

The issues surrounding 'Reduced Emissions from Deforestation and Forest Degradation' (REDD) have become a major component of continuing negotiations under the United Nations Framework Convention on Climate Change (UNFCCC). This paper aims to address two key requirements of any potential REDD mechanism: first, the generation of measurable, reportable and verifiable (MRV) REDD credits; and secondly, the sustainable and efficient provision of emission reductions under a robust financing regime.To ensure the supply of MRV credits, we advocate the establishment of an 'International Emission Reference Scenario Coordination Centre' (IERSCC). The IERSCC would act as a global clearing house for harmonized data to be used in implementing reference level methodologies. It would be tasked with the collection, reporting and subsequent processing of earth observation, deforestation- and degradation driver information in a globally consistent manner. The IERSCC would also assist, coordinate and supervise the computation of national reference scenarios according to rules negotiated under the UNFCCC. To overcome the threats of "market flooding" on the one hand and insufficient economic incentives for REDD on the other hand, we suggest an 'International Investment Reserve' (IIR) as REDD financing framework. In order to distribute the resources of the IIR we propose adopting an auctioning mechanism. Auctioning not only reveals the true emission reduction costs, but might also allow for incentivizing the protection of biodiversity and socio-economic values. The introduced concepts will be vital to ensure robustness, environmental integrity and economic efficiency of the future REDD mechanism.