Les facteurs ESG : modèles d'évaluation des actifs financiers incluant des facteurs de risque extra-financiers

Plus du tiers des actifs financiers gérés professionnellement aux États-Unis l’est de manière socialement responsable (Global Sustainable Investment Alliance, 2020). Nombreux sont donc les chercheurs qui ont voulu savoir quelles étaient les implications de la prise en compte des enjeux environnemental, social et de gouvernance dans les décisions d’investissement sur la performance et le risque. Friede et al. (2015) recense que plus de 90% des études au cours des quarante dernières années conclues à l’existence d’une relation non négative entre la performance financière et les critères ESG. Pour Sassen et al. (2016), une bonne performance ESG peut faire baisser le risque total de l’entreprise et par conséquent augmenter la valeur de l’entreprise en réduisant le rendement exigé sur les actifs, car moins risqués. C’est dans cette perspective que nous avons inclus des facteurs environnement, social, gouvernance et ESG agrégé construit à l’aide de la banque de données MSCI-IVA et CRSP, dans les modèles d’évaluation des actifs financiers afin d’apprécier leur contribution à la performance de ces dernières dans l’explication des rendements sur la période de 2005 à 2020 aux États-Unis. Deux types de modèles ont été considérés, à savoir les modèles d’évaluations des actifs financiers inconditionnels (Fama & French, 1993) et ceux conditionnels (Ferson & Schadt, 1996). Nos résultats indiquent que l’intégration des facteurs ESG dans les modèles d’évaluation des actifs financiers sur la période de 2005 à 2020 n’a pas contribué à améliorer de manière significative à la performance des modèles dans l’explication des rendements aux États-Unis, sans nuire non plus. Les critères ESG ne sont pas certes des facteurs de risque à rémunérer, mais ils peuvent sûrement être utilisés comme complément d’information dans l’établissement du profil rendement risque d’une entreprise

Application of Geographic Information System (GIS) for mapping land use types in Musanze district, Rwanda

Abstract: The purpose of this study was to apply Geographic Information System (GIS) for mapping different land use types. This study was conducted in Sahara, Gisesero and Kavumu cells of Busogo Sector from May to June 2012. In this area, land use management practices are not well applied and therefore, the natural environment is being degraded and this is illustrated by the cropping on hills and mountains with high slope area while they are appropriate for forest plantations. This study provides the information on how forests and other land use types should be managed in order to sustain the environment. Global Positioning System (GPS) was used to collect data and Arc map GIS 9.2 software was used for data analysis. Results showed that forestland occupies 42.5 ha (3.15 % of total study area), the area covered by crops while it is more appropriate to grow forests occupies 45.7 ha (3.45 %), the total area that must be occupied by the forest is 6.6% and the area covered by human settlement, city and institutions is 116.5 ha (8.79 %). The remaining 1045 ha (84 % of study area) is used in agricultural activities, agroforestry and infrastructure. Forest managers should advocate for planting trees in all areas with steep slope on hills and mountains and in areas where agriculture has no potential with good sylvicultural practices and expanding the agriculture in lowland where the erosion risk is minimum

Gaining power and precision by using model-based weights in the analysis of late stage cancer trials with substantial treatment switching.

In randomised controlled trials of treatments for late-stage cancer, it is common for control arm patients to receive the experimental treatment around the point of disease progression. This treatment switching can dilute the estimated treatment effect on overall survival and impact the assessment of a treatment's benefit on health economic evaluations. The rank-preserving structural failure time model of Robins and Tsiatis (Comm. Stat., 20:2609-2631) offers a potential solution to this problem and is typically implemented using the logrank test. However, in the presence of substantial switching, this test can have low power because the hazard ratio is not constant over time. Schoenfeld (Biometrika, 68:316-319) showed that when the hazard ratio is not constant, weighted versions of the logrank test become optimal. We present a weighted logrank test statistic for the late stage cancer trial context given the treatment switching pattern and working assumptions about the underlying hazard function in the population. Simulations suggest that the weighted approach can lead to large efficiency gains in either an intention-to-treat or a causal rank-preserving structural failure time model analysis compared with the unweighted approach. Furthermore, violation of the working assumptions used in the derivation of the weights only affects the efficiency of the estimates and does not induce bias or inflate the type I error rate. The weighted logrank test statistic should therefore be considered for use as part of a careful secondary, exploratory analysis of trial data affected by substantial treatment switching

Sustainable Agroforestry for Soil Chemical Properties Improvement and Nutrients Availability in Agriculture Landscape around Cyamudongo Isolated Forest, Rwanda

The protected areas of Rwanda are facing various challenges resulting from the anthropogenic activities of the surrounding communities, especially in the adjacent area to Cyamudongo isolated rain forest, which results in soil degradation. Therefore, this study aims to broaden current knowledge on the impact of sustainable Agroforestry (AF) on soil-selected chemical and physical properties. To understand this, the permanent sample plots (PSPs) were established mainly in the designed four transects of four km long originating on the boundary of the Cyamudongo isolated rain forest following the slope gradient ranging from 1286 to 2015 m asl. A total number of 73 PSPs were established in the Cyamudongo study area. The Arc Map GIS 10.4 was used to design and map the sampling areas while GPS was used for localization of plots centers. Statistical significance was analyzed through R-software. The recorded soil pH means value across in Cyamudongo study area is 4.2, which is strongly acidic. The tests revealed that the soil pH, C, N, C: N ratio, OM, NH4+, NO3-+NO2-, PO43-, and CEC were significantly different in various soil depths. The pH, N, C: N ratio, CEC, NH4+, PO43-, and Al3+ showed a significant difference across land uses whereas the C and NO3-+NO2- did not show any statistical difference. All tested chemical elements showed a statistical difference as far as altitude ranges are concerned. The only NH4+, PO43-, and CEC showed significant differences with time whereas all other remaining chemical elements did not show any statistical significance. The soil pH was very strongly correlated with CEC, Mg, and Ca in cropland (CL) whereas it was strongly correlated in both AF and natural forest (NF) except for Mg, which was moderately correlated in AF. Furthermore, its correlation with K was strong in CL, and moderate in AF while it was weak in NF. Finally, the pH correlation with Na was weak in both AF and CL whereas it was negligible in NF

Carbon Sequestration and Carbon Stock of Agroforestry Tree Species Around Cyamudongo Isolated Rain Forest and Arboretum of Ruhande, Rwanda

Agroforestry (AF) is widely considered the most important tool to mitigate climate change-related issues by removing Carbon (C) Dioxide (CO2) from the atmosphere and storing C. Therefore, this study aims to broaden current knowledge on the impact of sustainable Agroforestry (AF) on the C sequestration rate and C stock in the surroundings of Cyamudongo isolated rain forest and Ruhande Arboretum. To understand this, the permanent sample plots (PSPs) were established mainly in the four designed transects of four km long originating on the Cyamudongo isolated rain forest boundary following the slope gradient ranging from 1286 to 2015 m asl. A total number of 73 PSPs were established in the Cyamudongo study area while 3 PSPs were established in the Ruhande AF plot. The Arc Map GIS 10.4 was used to design and map the sampling areas while GPS was used for the localization of the plots. Statistical significance was analyzed through R-software. The estimated quantity of sequestrated C for 2 years and 34 years of AF species was 13.11 t C ha -1 yr-1 (equivalent to 48 t CO2 ha -1 yr-1) and 6.85 t ha-1 yr-1 (equivalent to 25.1 t CO2 ha -1 yr-1) in Cyamudongo and Ruhande respectively. The estimated quantity of C stored by the Ruhande AF plot is 232.94 t ha-1. In Cyamudongo, the overall C stored by the AF systems was 823 t ha-1 by both young tree species established by the Cyamudongo Project (35.84 t ha-1) and C stored by existing AF species before the existence of the Project (787.12 t ha-1). In all study areas, the Grevillea robusta contributed more to overall stored C. The correlation coefficients between tree diameter and living biomass ranged from moderate to very strong due to differences in terms of age, stage of growth, and tree species

An adjustable sample average approximation algorithm for the stochastic production-inventory-routing problem

We consider a stochastic single item production-inventory-routing problem with a single producer, multiple clients, and multiple vehicles. At the clients, demand is allowed to be backlogged incurring a penalty cost. Demands are considered uncertain. A recourse model is presented, and valid inequalities are introduced to enhance the model. A new general approach that explores the sample average approximation (SAA) method is introduced. In the sample average approximation method, several sample sets are generated and solved independently in order to obtain a set of candidate solutions. Then, the candidate solutions are tested on a larger sample, and the best solution is selected among the candidates. In contrast to this approach, called static, we propose an adjustable approach that explores the candidate solutions in order to identify common structures. Using that information, part of the first-stage decision variables is fixed, and the resulting restricted problem is solved for a larger size sample. Several heuristic algorithms based on the mathematical model are considered within each approach. Computational tests based on randomly generated instances are conducted to test several variants of the two approaches. The results show that the new adjustable SAA heuristic performs better than the static one for most of the instances.publishe

The Magic of Nanotechnology (invited lecture)

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