Short Biography: He is Director of Statistics Directorate at the High Commission for Planning (HCP) and  Director of the National Institute of Statistics and Applied Economics. After spending over 25 years working as a researcher and a full professor of statistics/biostatistics at Laval University and the Université du Québec à Trois-Rivières, Québec, Canada, Belkacem joined the HCP, where he is strongly involved in the production of official statistics in Morocco together with various national and international statistical activities and projects.

Throughout his career, Belkacem has been a researcher at the Montreal Mathematic Research Centre, a member of the Statistical Society of Canada, the Institute of Mathematical Statistics (IMS) and the International Statistical Institute (ISI).

Talk of the title: “On some statistical methods in public health and environmental related problems”

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Short Biography: Sophie Dabo-Niang is professor of Applied Mathematics (Statistics) at University of Lille, laboratory LEM CNRS 9221 and member of INRIA-MODAL team. She completed a 3-years PhD in Statistics from the University Paris 6 in 2002. She is  chair of the axis MeQAME (Quantitauve methods in Management and Economics) of LEM and vice-chair of EMS-CDC (European Mathematical Society-Committee of Developing Counties; http://euro-math-soc.eu/committee/developing-countries). Dr. Sophie Dabo-Niang research program is focused on the study of non(semi)-parametric inference of functional and spatio-temporal data. From an applied perspective, she is interested in economics, medical, environmental and hydrological studies. She published more than forty statistical papers.

Title of the talk: "Bridging Functional Data Analysis and Spatial Data Modeling" 

Abstract: Spatial statistics includes any (statistical) techniques which study phenomenons observed on spatial sets. Such phenomenons appear in a variety of fields: epidemiology, environmental science, physics, econometrics, image processing and many others. The modelization of spatial data is among the most interesting research subjects in dependent data analysis. This is motivated by the increasing number of situations coming from different fields of applied sciences for which the data are of spatial nature. This is the case for instance in epidemiology, where data are often spatial or space-time, and so spatial location can acts as a surrogate for risk factors. Complex issues arise in spatial analysis, many of which are neither clearly defined nor completely resolved, but form the basis for current researches. This is the case of functional data analysis techniques, which incorporate spatial dependency. We are interested here in semi(non)-parametric spatial regression estimation. More precisely, we estimate regression functions where the explanatory variable is real-valued while covariates are functional random fields. Asymptotic results of the proposed estimator are established. The skills of the methods are illustrated on simulations and real data analysis.

Short Biography: : Researcher at the University of Coimbra in the Energy for Sustainability initiative and INESCC, he holds a Ph.D. in Sustainable Energy Systems from the University of Coimbra (UC) and a MBA from Porto Business School. His research interests, are in the areas of energy economics, energy markets and sustainable energy systems. He is author of articles in international peer-reviewed journals and conferences, and also a founding member of the Portuguese Association for Energy Economics. He is the Operations Manager in a combined cycle gas turbine power plant, representing Trustenergy (a joint venture between Engie and Marubeni).

Title of the talk:  “Interconnection congestion determinants: the Iberian case” 

(with Patrícia Pereira da Silva and Pedro A. Cerqueira)

Abstract: Cross-border interconnection infrastructure is fundamental to optimise daily production, increase the opportunities for operation with renewable energies, promote the competition and enhance the security of supply. The management of limited cross-border interconnections capacity constitutes a challenge, in particular if extensive renewable energy generation is available.

The probability of cross-border interconnection congestion is inhere estimated through the use of non-parametric models, as a function of wind and hydro power, together with the available transmission capacity and electricity demand. The congestion of the cross-border interconnections prevents further optimization of available renewable resources. 

Short Biography: : Agronomic Engineer from Institut Agronomique et Vétérinaire Hassan II, Rabat (1981), and Ecole Supérieure du Bois, Paris France(1980). Master of Sciences (1988) in economics and PhD in Forestry (19992) from the University of Idaho, USA. Master of Sciences in ICT, University of Louis Pasteur, Strasbourg, France (virtual campus). Full Professor at Ecole Nationale Forestière d’Ingénieurs since 1994, and visiting professor to many universities.  Research Fellow in the Royal Institute of Strategic Studies (IRES), Morocco, and in the Institute for Labor Studies (IZA), Germany. His recent research interests deal with integrated natural resources management, environmental assessment, climate change adaptation, and rural development. Author of many publications (book chapters, scientific papers, expertise reports, outreaching documents, etc.), recipient of numerous research grants and consultant for international organizations (UNESCO, ISESCO, UNDP, UNEP, World Bank, FAO, GIZ, ..) and national public and private institutions. Fulbright Alumni, president of the Regional Science Association of Morocco. Lead author for the IPCC fifth report, chapter 5 (coastal zones and low lying areas).

Title of the talk:  “Analysis of the cost of environmental degradation in coastal zones” 

Abstract: The major environmental concerns in coastal zones in Morocco are related to the rapid urbanization, the direct discharge of industrial and household solid waste into the sea, the degradation and disappearance of natural resources (wetlands, forests, coastal ecosystems, etc.), destruction of coastal dunes, erosion in sandy beaches, etc.  An important cost of environmental degradation will result from these issues. The costs will concern health losses related, water quantity and quality degradation, air pollution, loss of fish captures, loss of amenities, loss of tourism opportunities, loss of biodiversity and many ecosystem services. Evaluation of the cost (degradation, damage) of environmental impact aims to associate monetary values with these impacts. This paper deals with the identification of various costs and methodologies to compute them in order to inform the decision-making processes.  

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Short Biography: : A Dr Rachida Ouysse is a Senior Lecturer in Economics at the UNSW Business School, UNSW Sydney. She holds a PhD in Economics/Econometrics from Boston College and an MA in Econometrics from the Université de Montréal. She has published in leading A and A* econometrics, statistics, forecasting, and financial econometrics journals, including the Journal of Financial Econometrics, the International Journal of Forecasting, and the Journal of Multivariate Analysis. Her research lies at the intersection of econometric theory, financial econometrics, and big‑data modelling, with work on high‑dimensional factor models, Bayesian and bootstrap methods, and dense and sparse predictive models for economic and financial time series.

A central theme of her recent work is the measurement of environmental and sustainability risk in financial markets. She develops matrix‑factor and high‑dimensional methods for structured ESG disclosure data to quantify firms’ alignment with sector‑specific materiality standards and to detect potential greenwashing in corporate reporting, with the aim of supporting more transparent and reliable environmental and climate‑related disclosures.

Dr Ouysse is co‑leading an “AI‑Powered ESG Alignment & Greenwashing Risk Analytics Platform” funded by an AEA IGNITE grant, in collaboration with industry partners, to deliver scalable tools that can be used by financial institutions, investors, and regulators. Through her role in the UNSW Fintech AI Innovation Consortium and its regulatory roundtables, she works at the interface of econometrics, AI, and policy, contributing evidence‑based insights to debates on sustainable finance, AI governance, and the design of climate‑aligned financial regulation.

Title of the talk:  “Disclosure Architecture and Materiality Alignment: Estimation and Inference in High-Dimensional ESG Panels” 

Abstract: ThiExisting work on ESG disclosure largely measures disclosure intensity or aggregate scores, leaving the internal structure of disclosure across topics largely unexamined. That leaves open whether firms disclose the topics that are materially relevant for their industry or simply disclose more. The distinction matters for investors, lenders, regulators, and standard setters because materiality-based reporting is intended to support decision-useful information. This paper studies whether the cross-metric architecture of structured ESG disclosure aligns with sector-specific materiality benchmarks. We model firm-level disclosure matrices in a large-firm, large-metric, fixed-time setting and construct a rotation-invariant Materiality Architecture Score from the estimated metric-factor projection space. We establish consistency of the estimated metric-factor projection space, asymptotic normality of the induced architecture-level measures, and valid firm-cluster pairs-bootstrap inference under the unbalanced-panel design.

The empirical application uses Eurofidai structured ESG disclosures, with data obtained via EUROFIDAI, matched to SASB materiality maps for four sectors: Commercial Banks, Semiconductors, Software & IT Services, and Biotechnology & Pharmaceuticals. The data allow disclosure to be studied as a structured cross-metric object rather than collapsed into a scalar intensity measure, and the sectoral SASB mapping provides an external benchmark for testing whether disclosure architecture is materially aligned. The results show systematic cross-sector differences in alignment and support an architecture-level greenwashing measure that distinguishes disclosure volume from materiality alignment.

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