Objectives & Learning Outcomes

The program aims to educate graduates who are able to understand, evaluate, manage, and support the implementation of clean technologies within complex technical, economic, environmental, and organisational settings.

It combines engineering-informed knowledge of clean technology pathways with management, innovation, quantitative analysis, and digital decision-making skills.

Program Objectives

  • To provide advanced interdisciplinary knowledge in clean technologies and sustainability.
  • To develop graduates’ ability to evaluate clean-technology options using technical, environmental, economic, and feasibility criteria.
  • To strengthen analytical and data-driven decision-making skills through applied statistics, econometrics, operational research, machine learning, and artificial intelligence.
  • To promote innovation, entrepreneurship, and responsible implementation of clean technologies.
  • To prepare graduates for professional and research-oriented roles in clean technologies, sustainability, energy systems, circular economy, and digital business environments.

Intended Learning Outcomes

Upon successful completion of the program, graduates will be able to:

  • Demonstrate integrated knowledge of clean technology domains, including energy systems, emerging technologies, and circular-economy solutions.
  • Critically evaluate clean-technology options and deployment strategies using sustainability and techno-economic reasoning.
  • Apply quantitative and data-driven methods to support planning, monitoring, optimisation, and decision-making.
  • Use appropriate digital tools, data analytics, machine learning, and artificial intelligence methods for clean-technology management problems.
  • Design and communicate evidence-based solutions for real-world clean-technology challenges.
  • Work effectively in international, multicultural, and interdisciplinary environments.
  • Produce academic and professional work that respects academic integrity, ethical conduct, proper citation, responsible data handling, and transparent use of digital and AI-enabled tools.
  • Conduct an independent MSc thesis in English with methodological rigour, analytical depth, and real-world relevance.