Narratives in REDD+ benefit sharing: examining evidence within and beyond the forest sector

REDD+ was designed globally as a results-based instrument to incentivize emissions reduction from deforestation and forest degradation. Over 50 countries have developed strategies for REDD+, implemented pilot activities and/or set up forest monitoring and reporting structures, safeguard systems and benefit sharing mechanisms (BSMs), offering lessons on how particular ideas guide policy design. The implementation of REDD+ at national, sub-national and local levels required payments to filter through multiple governance structures and priorities. REDD+ was variously interpreted by different actors in different contexts to create legitimacy for certain policy agendas. Using an adapted 3E (effectiveness, efficiency, equity and legitimacy) lens, we examine four common narratives underlying REDD+ BSMs: (1) that results-based payment (RBP) is an effective and transparent approach to reducing deforestation and forest degradation; (2) that emphasis on co-benefits risks diluting carbon outcomes; (3) that directing REDD+ benefits predominantly to poor smallholders, forest communities and marginalized groups helps address equity; and (4) that social equity and gender concerns can be addressed by well-designed safeguards. This paper presents a structured examination of eleven BSMs from within and beyond the forest sector and analyses the evidence to variably support and challenge these narratives and their underlying assumptions to provide lessons for REDD+ BSM design. Our findings suggest that contextualizing the design of BSMs, and a reflexive approach to examining the underlying narratives justifying particular design features, is critical for achieving effectiveness, equity and legitimacy.

Key policy insights

• A results-based payment approach does not guarantee an effective REDD+; the contexts in which results are defined and agreed, along with conditions enabling social and political acceptance, are critical.

• A flexible and reflexive approach to designing a benefit-sharing mechanism that delivers emissions reductions at the same time as co-benefits can increase perceptions of equity and participation.

• Targeting REDD+ to smallholder communities is not by default equitable, if wider rights and responsibilities are not taken into account

• Safeguards cannot protect communities or society without addressing underlying power and gendered relations.

• The narratives and their underlying generic assumptions, if not critically examined, can lead to repeated failure of REDD+ policies and practices.

     

A discrete element study on the deformation and degradation of coal-fouled ballast

Acta Geotechnica - This paper presents the results of Discrete Element Modelling (DEM) which quantitively examine the effect of coal fouling on the deformation and degradation of ballast upon...     

The October 13, 2020, deadly rapid landslide triggered by heavy rainfall in Phong Dien,Thua Thien Hue,Vietnam

Landslides - At about 12:00 a.m., on October 13, 2020, a rapid rotational landslide induced by rainfall swept over Ranger Station-7 in Phong Xuan commune, Phong Dien district, Thua Thien Hue...     

Numerical analysis of subsurface deformation induced by groundwater level changes in the Bangkok aquifer system

Acta Geotechnica - Subsurface deformation due to long terms of groundwater drawdown from 1960 to 1997 and groundwater recovery from 1997 to 2016 in the upper part of the Bangkok multi-aquifer...     

Spatial prediction of landslide susceptibility in western Serbia using hybrid support vector regression (SVR) with GWO,BAT and COA algorithms

In this study, we developed multiple hybrid machine-learning models to address parameter optimization limitations and enhance the spatial prediction of landslide susceptibility models. We created a geographic information system database, and our analysis results were used to prepare a landslide inventory map containing 359 landslide events identified from Google Earth, aerial photographs, and other validated sources. A support vector regression (SVR) machine-learning model was used to divide the landslide inventory into training (70%) and testing (30%) datasets. The landslide susceptibility map was produced using 14 causative factors. We applied the established gray wolf optimization (GWO) algorithm, bat algorithm (BA), and cuckoo optimization algorithm (COA) to fine-tune the parameters of the SVR model to improve its predictive accuracy. The resultant hybrid models, SVR-GWO, SVR-BA, and SVR-COA, were validated in terms of the area under curve (AUC) and root mean square error (RMSE). The AUC values for the SVR-GWO (0.733), SVR-BA (0.724), and SVR-COA (0.738) models indicate their good prediction rates for landslide susceptibility modeling. SVR-COA had the greatest accuracy, with an RMSE of 0.21687, and SVR-BA had the least accuracy, with an RMSE of 0.23046. The three optimized hybrid models outperformed the SVR model (AUC = 0.704, RMSE = 0.26689), confirming the ability of metaheuristic algorithms to improve model performance.     

A Novel Classifier Based on Composite Hyper-cubes on Iterated Random Projections for Assessment of Landslide Susceptibility

In this paper, the main objective is to discover an application of a novel classifier based on Composite Hyper-cubes on Iterated Random Projections (CHIRP) for assessment of landslide susceptibility at the Uttarakhand Area (India). For this, 1295 historical landslides events and landslide affecting parameters were collected and used for creating training and testing datasets. Other benchmark models namely Logistic Regression (LR), RBF neural network (ANN-RBF), and Naïve Bayes (NB) were chosen for comparison. Analysis results indicate that the CHIRP is the best, followed by the LR, the ANN-RBF, and the NB, respectively. Overall, the CHIRP indicates as a promising and good alternative method that could be used to assess landslide susceptibility in other landslide prone areas.     

Investigation of ground rotational motions caused by direct and scattered P-waves from the 4 March 2008 TAIGER explosion experiment

High-frequency rotational motions of P-waves and coda waves were analysed using rotation rate sensors and strong motion array data from the 4 March 2008 TAiwan Integrated GEodynamics Research (TAIGER) explosion experiment in northeastern Taiwan. Theoretical and observational investigations focussed on the effects of this experiment on the free surface. The main goal of this study was to explore possible applications of combined measurements of artificial explosion-derived translational and rotational motions. Also investigated was the consistent ground rotation observed directly by rotation rate sensors and derived using translational seismic arrays. Common near-source high-frequency rotational motion observations and array-recorded translational motions from one shallow borehole explosion are analysed in this study. Using a half-space assumption of plane P-wave propagation across the recording site, we conclude that: (1) rotational motions induced by direct P-waves interacting with a free surface in theory can be used to estimate wave radial direction, velocity and anisotropic properties; (2) rotational motions derived from scattering are predominant among the observed rotations during the TAIGER explosion experiments and allow us to image the heterogeneous structure of the medium at the investigated site; and (3) rotation sensor measurements undertaken during TAIGER explosion experiments may be affected by cross-axis sensitivities, which need to be considered when using the data obtained during these experiments.     

Damage detection of a thin plate using modal curvature via macrostrain measurement

Plate structures are employed as important structural components in many engineering applications. Hence, assessing the structural conditions of in-service plate structures is critical to monitoring global structural health. Modal curvature-based damage detection techniques have recently garnered considerable attention from the research community, and have become a promising vibration-based structural health monitoring solution. However, computing errors arise when calculating modal curvatures from lateral mode shapes, which result from unavoidable measurement errors in the mode shapes as identified from lateral vibration signals; this makes curvature-based algorithms that use a lateral measurement only theoretically feasible, but practically infeasible. Therefore, in this study, long-gauge fiber Bragg grating strain sensors are employed to obtain a modal curvature without a numerical differentiation procedure in order to circumvent the computing errors. Several damage indices based on modal curvatures that were developed to locate beam damage are employed. Both numerical and experimental studies are performed to validate the proposed approach. However, although previous studies have reported relative success with the application of these damage indices on a simple beam, only one damage index demonstrated the capability to locate damage when the stiffness of the local region changed near the sensor.     

Bayesian Chance‐Constrained Hydraulic Barrier Design under Geological Structure Uncertainty

The groundwater community has widely recognized geological structure uncertainty as a major source of model structure uncertainty. Previous studies in aquifer remediation design, however, rarely discuss the impact of geological structure uncertainty. This study combines chance‐constrained (CC) programming with Bayesian model averaging (BMA) as a BMA‐CC framework to assess the impact of geological structure uncertainty in remediation design. To pursue this goal, the BMA‐CC method is compared with traditional CC programming that only considers model parameter uncertainty. The BMA‐CC method is employed to design a hydraulic barrier to protect public supply wells of the Government St. pump station from salt water intrusion in the “1500‐foot” sand and the “1700‐foot” sand of the Baton Rouge area, southeastern Louisiana. To address geological structure uncertainty, three groundwater models based on three different hydrostratigraphic architectures are developed. The results show that using traditional CC programming overestimates design reliability. The results also show that at least five additional connector wells are needed to achieve more than 90% design reliability level. The total amount of injected water from the connector wells is higher than the total pumpage of the protected public supply wells. While reducing the injection rate can be achieved by reducing the reliability level, the study finds that the hydraulic barrier design to protect the Government St. pump station may not be economically attractive.     

A hybrid machine learning ensemble approach based on a Radial Basis Function neural network and Rotation Forest for landslide susceptibility modeling: A case study in the Himalayan area,India

In this paper, a hybrid machine learning ensemble approach namely the Rotation Forest based Radial Basis Function (RFRBF) neural network is proposed for spatial prediction of landslides in part of the Himalayan area (India). The proposed approach is an integration of the Radial Basis Function (RBF) neural network classifier and Rotation Forest ensemble, which are state-of-the art machine learning algorithms for classification problems. For this purpose, a spatial database of the study area was established that consists of 930 landslide locations and fifteen influencing parameters (slope angle, road density, curvature, land use, distance to road, plan curvature, lineament density, distance to lineaments, rainfall, distance to river, profile curvature, elevation, slope aspect, river density, and soil type). Using the database, training and validation datasets were generated for constructing and validating the model. Performance of the model was assessed using the Receiver Operating Characteristic (ROC) curve, area under the ROC curve (AUC), statistical analysis methods, and the Chi square test. In addition, Logistic Regression (LR), Multi-layer Perceptron Neural Networks (MLP Neural Nets), Naïve Bayes (NB), and the hybrid model of Rotation Forest and Decision Trees (RFDT) were selected for comparison. The results show that the proposed RFRBF model has the highest prediction capability in comparison to the other models (LR, MLP Neural Nets, NB, and RFDT); therefore, the proposed RFRBF model is promising and should be used as an alternative technique for landslide susceptibility modeling.