TRAINING SCHOOL

The Training School on Econometrics for the Environment, organized in conjunction with the CE² 2026 International Conference, is designed to provide PhD students, early-career researchers, and practitioners with advanced theoretical and practical skills for analyzing environmental, climate, energy, and natural resource issues.

The programme will focus on three major methodological areas: Structural Equation Modeling, Panel Econometrics, and Spatial Econometrics. Through a combination of lectures and hands-on applications using relevant statistical software and real-world data, participants will gain practical experience in applying these modern econometric approaches to environmental and sustainability challenges.

The Training School will provide an excellent opportunity to strengthen research capacities, exchange ideas with leading international scholars, and develop innovative empirical approaches for addressing complex environmental and climate-related issues.

Lecture #1: Panel Econometrics

December 18th, 2026

By Yassine Sbai Sassi, New York University, USA 

Yassine Sbai Sassi is an Assistant Professor of Economics at New York University. He received his Ph.D. in Economics from the University of California, Berkeley. His research focuses on econometric theory, specializing in the statistical analysis of network models, dyadic interactions, and panel data.

This course is a lecture-based introduction to the methodology and application of dynamic panel econometric models for Master and Ph.D. students.  

The objective of the course is to provide an introduction to . Students will learn how to model and incorporate spatial dependencies into their empirical analyses.

The course will cover topics such as: ..

 Lecture #2: Spatial Econometrics

December 18th, 2026

Title: “....”

By ..............................

Bibliography (tba):

English:

1.  Elhorst, J. P. (2014): Spatial econometrics : from cross-sectional data to spatial panels, Springer.

2.  LeSage and Pace (2009), Introduction to spatial econometrics, s. Boca Raton, Taylor & Francis;

3.  Anselin L. (1988), Spatial econometrics: Methods and models. Kluwer Academic Publishers.

French:

1.  Le Gallo J. (2004), Hétérogénéité spatiale, principes et méthodes, Economie et Prévision, vol. 162, pp. 151-172.

2. Le Gallo J. (2002), Econométrie spatiale : l’autocorrélation spatiale dans les modèles de régression linéaire, Economie  et Prévision, vol. 155, pp. 139-158.

3. Jayet H. (2001), Econométrie des données spatiales. Une introduction à la pratique, Cahiers d’Economie et de Sociologie Rurale, vol. 58-59, pp. 105-129.

          https://www.insee.fr/en/information/3635545

Lecture #3: Structural Equation Modeling

December 18th, 2026

Title: Structural Equation Modeling for Environmental and Sustainability Research 

By Prof. Zouhair El Hadri, Mohammed V University in Rabat

Short Bio: Zouhair El Hadri is a Full Professor of Mathematics at the Faculty of Sciences of Mohammed V University in Rabat, Morocco. He is a member of the Laboratory of Mathematics, Statistics and Applications.

His research focuses on multivariate statistics and statistical modeling, with particular emphasis on Structural Equation Modeling (SEM), including both Partial Least Squares SEM (PLS-SEM) and Covariance-Based SEM (CB-SEM). His research interests also include Path Analysis, Factor Analysis, and Multiblock Data Analysis.

1. Course Description and Learning Objectives

This advanced training course provides a methodological and applied introduction to Structural Equation Modeling (SEM) for empirical research in environmental economics, sustainability science, climate change, and related fields. It is designed for Master’s and Ph.D. students, researchers, and academics who wish to develop the theoretical understanding and practical skills required to formulate, estimate, assess, and interpret SEM models.

The course presents SEM as an integrated framework combining regression analysis, path analysis, factor analysis, and latent-variable modeling. Particular attention is given to the distinction between the measurement model and the structural model, model specification and identification, estimation and model assessment, and the conceptual differences between Covariance-Based SEM (CB-SEM) and Partial Least Squares SEM (PLS-SEM).

Throughout the course, environmental and sustainability-related examples will be used to illustrate how SEM can be employed to analyze complex relationships involving environmental awareness, sustainable behavior, climate change perceptions, green finance, corporate social responsibility, environmental policy, renewable energy, water management, and other sustainability outcomes.

Learning Objectives

By the end of the training, participants should be able to:

1. Understand the transition from conventional regression models to Structural Equation Modeling (SEM).

2. Distinguish between observed/manifest variables and latent/unobserved constructs, and understand their roles in empirical research.

3. Understand and interpret PCA, EFA, and CFA as complementary tools for dimensionality reduction, construct identification, and measurement validation.

4. Specify, estimate, and interpret Path Analysis models for analyzing direct and indirect relationships among observed variables.

5. Distinguish clearly between the measurement and structural components of an SEM and understand how they interact.

6. Master the fundamental terminology, notation, assumptions, and graphical representation of SEM.

7. Understand the main stages of SEM analysis, including model specification, identification, estimation, evaluation, and interpretation.

8. Understand the conceptual and methodological differences between CB-SEM and PLS-SEM, including their respective objectives, assumptions, and fields of application.

9. Apply SEM techniques to environmental and sustainability research questions, using real or realistic empirical examples.

10. Use SEM software to implement empirical models, with practical demonstrations using lavaan and plspm R packages. 

11. Critically evaluate and communicate SEM results in the context of academic research and applied environmental policy analysis.

2. Pedagogical Structure and Program

The training combines methodological lectures, graphical illustrations, empirical examples, and software demonstrations. The program is organized progressively, moving from conventional multivariate methods to advanced SEM applications.

Part I - From Regression to Structural Equation Modeling

1. Multiple Regression Model - 15 min

• From regression analysis to multivariate modeling

• Direct relationships among observed variables

• Limitations of conventional regression for complex causal structures

• Motivation for Structural Equation Modeling

2. Path Analysis - 20 min

• Concept and graphical representation

• Direct and indirect effects

• Recursive path models

• Path coefficients and interpretation

• Environmental applications: determinants of sustainable behavior and environmental outcomes

3. Principal Component Analysis (PCA) - 15 min

• Motivation and objectives

• Dimensionality reduction

• Principal components and interpretation

• Applications to environmental and sustainability indicators

4. Exploratory Factor Analysis (EFA) - 15 min

• Identifying underlying dimensions and latent constructs

• Factor extraction and rotation

• Factor loadings and interpretation

• Applications to environmental attitudes, perceptions, and sustainability-related indicators

5. Confirmatory Factor Analysis (CFA) - 15 min

• From exploratory to confirmatory analysis

• Specification of measurement models

• Factor loadings and measurement errors

• Environmental and sustainability measurement examples

6. SEM: Vocabulary, Notation, and Graphical Representation - 10 min

• Latent and observed variables

• Exogenous and endogenous variables

• Indicators and measurement errors

• Structural paths and covariances

• Model diagrams and SEM notation

Coffee & Networking Break - 30 min

Part II - Covariance-Based Structural Equation Modeling

7. CB-SEM: From Model Specification to Empirical Estimation - 35 min

• Model specification

• Identification conditions

• Estimation methods

• Model fit and goodness-of-fit measures

• Assessment of measurement models

• Structural model evaluation

• Direct, indirect, and total effects

• Interpretation and reporting of SEM results

8. Applied Example 1: Sustainable Water Management - 10 min

Understanding Users’ Behavioral Intention to Adopt a Search Engine that Promotes Sustainable Water Management

• Conceptual model and hypotheses

• Latent constructs and observed indicators

• Measurement and structural models

• Estimation and model assessment

• Interpretation of direct and indirect effects

• Software demonstration: lavaan R package

Part III - Partial Least Squares Structural Equation Modeling

9. PLS-SEM: Concepts, Procedures, and Estimation - 35 min

• Objectives and characteristics of PLS-SEM

• Reflective and formative measurement models

• PLS-SEM modes

• Weighting schemes

• PLS algorithm

• Parameter estimation

• Assessment of measurement models

• Structural model assessment

10. Applied Example 2: Green Finance, Green Taxation, and Corporate Social Responsibility - 10 min

Unveiling the Impact of Green Taxation and Green Financing on Corporate Social Responsibility

• Conceptual framework

• Latent constructs and indicators

• PLS-SEM estimation

• Interpretation of structural relationships

• Assessment of mediation and/or indirect effects

• Software demonstration: plspm R package

3. Expected Outcomes

At the end of the training, participants will have developed a practical and conceptual understanding of SEM, enabling them to:

• formulate SEM-based research questions and conceptual frameworks;

• translate theoretical concepts into measurable latent constructs;

• design and assess measurement instruments;

• specify and estimate CB-SEM and PLS-SEM models;

• evaluate measurement and structural models;

• interpret direct, indirect, and total effects;

• use lavaan and plspm for empirical applications; and

• apply SEM methodology to environmental, climate, sustainability, and green-economy research.

The training is particularly relevant for empirical studies dealing with environmental behavior, climate-change perceptions, sustainable consumption, water and natural-resource management, renewable energy adoption, green finance, environmental policy, corporate sustainability, and ESG-related research.