75587 - Environmetrics

Course Unit Page

  • Teacher Athanassios Stengos

  • Credits 6

  • SSD SECS-S/01

  • Teaching Mode Traditional lectures

  • Language English

  • Campus of Rimini

  • Degree Programme Second cycle degree programme (LM) in Resource Economics and Sustainable Development (cod. 8839)

Academic Year 2022/2023

Learning outcomes

By the end of the course the student should have gained the knowledge of basic statistical tools for managing quantitative information concerning environmental phenomena. In particular, the student will be able to perform explorative analyses of spatial and spatio-temporal data highlighting correlation patterns and to estimate basic suitable models. For both explorative analyses and model estimation, the student will be provided with basic knowledge concerning widespread statistical software. Moreover, the student will be aware of official environmental data sources at regional, national and European level.

Course contents

The course will be presenting an overview of the most recent methods in the current empirical literature in environmental and energy economics. We will cover topics in time series econometrics and nonparametric and semi-parametric methods that have been used in the recent literature to analyse issues relating to energy demand, the Environmental Kuznets Curve and greenhouse emissions to mention a few. The course will include a self-contained discussion of time series and nonparametric techniques and their application to estimating nonlinearities in environmental data. The issue of nonlinearities has recently emerged as one of the most salient features of empirical work in the modelling of economic series at large and we use the above mentioned methods with real data to replicate and/or extend existing studies from the recent literature. The course aims to target students who would like a comprehensive exposure to the empirical aspects of the energy and environmental economics literature.


R. Godby, Lintner, T. Stengos and B. Wandschneider ‘Testing for Asymmetric Pricing in the Canadian Gasoline Market’ Energy Economics, 2000, 22, 3, 349-368. J. List, D. Millimet and T. Stengos, “The Environmental Kuznets Curve: Real Progress or Misspecified Models?” The Review of Economics and Statistics, 2003, 85, 1038-1047. C. Ordas, T. Stengos and S. Valente “Growth and the Pollution Convergence Hypothesis: a Nonparametric Approach”, Journal of Environmental Economics and Management, 2011, 62, 199-214. E. Agliardi, M. Pinar and T. Stengos "An environmental degradation index based on stochastic dominance" Empirical Economics, 2015, 48, 439-459. M. Polemis and T. Stengos "Electricity sector performance: A panel threshold analysis" Energy Journal, 2017, 38, 3, 141-158. E. Agliardi, M. Pinar and T. Stengos "Air and water pollution over time and industries with stochastic dominance" Stochastic Environmental Research and Risk Assessment, 2017, 31, 1389-1408. M. Polemis and T. Stengos "Taming the SO₂ and NOx emissions: evidence from a SUR model for the US", Letters in Spatial and Resource Sciences, 2018, 11, 95-104.

Teaching methods

Classroom lectures covering a number of techniques in econometric methods that will be useful in conducting research in the area of empirical environmental economics.

Assessment methods

The assessment will be based on research individual project paper that will cover the in class material that will be submitted by the time of the exam date (50%) and all the intermediate work that is weekly assigned to complete the above project that will be also worth 50% of the final mark.

Teaching tools

Lecture notes distributed by the instructor.

Office hours

See the website of Athanassios Stengos