Study options
- Starting in
- September 2025
- Location
- Mile End
- Fees
- Home: £21,500
Overseas: £33,500
EU/EEA/Swiss students
What you'll study
The end goal of this course is to train dynamic and creative future managers, researchers in AI for sustainability and AI for climate change, public policy practitioners, financial sector professionals and prospective PhD students.
You will come to understand the complexity of climate change and the associated trade-offs.
You will master the use of AI and big data to identify trends and dynamics of fundamental aspects of climate change, such as the environmental risks organisations are exposed to, the quality of natural capital, GHG emissions, and diffusion of best management practices, behaviours and policy.
You will explore environmental and sustainability datasets from a variety of sources, including earth, space, economic and financial data.
You will critically evaluate actions put in place by the public and private sector to promote sustainability and address environmental problems.
You will acquire skills and knowledge to design effective sustainable management strategies and policies.
You will leave with both the knowledge and confidence to hold governments and businesses accountable for their climate pledges.
Structure
- Eight compulsory modules.
- Two elective modules.
- One Master project module.
Compulsory/Core modules
Financing the transition to a low carbon economy, in ways which are aligned with environmental, social and governance (ESG) criteria, will present, at the same time, significant opportunities and challenges. This module will introduce students to the vast landscape of climate finance tools, both at the global level (eg. clean development mechanism and other carbon offset platforms) and the regional level (eg. renewable energy funds, green bonds and carbon markets), with an emphasis on their economic and financial foundations. The module will provide a thorough analysis of the efficiency and effectiveness of these tools by evaluating their impact on the economy, the environment and society at large. The course will describe the role of key actors in the climate finance scenario, namely, central banks, supervisory authorities, national and multilateral development banks, corporate banks, and institutional investors. Students will have the opportunity to strengthen their empirical skills by using statistical softwares, such as R and Stata, to run impact evaluation studies on climate finance instruments.
The module introduces students to the phenomenon of environmental degradation, and its extreme example, climate change, through the lenses of applied economics and data analysis. First, students will receive good foundations on the science behind the phenomenon of environmental (and climate) change. Second, students will be equipped with strong theoretical and empirical tools and will develop a comprehensive view on the determinants and consequences of global environmental change. The emphasis of the module will be on the linkages between the environment, the economy and society, and the evaluation of the main environmental and climate policy available to policy-makers (eg emission trading schemes, carbon price, green taxes, renewable energy subsidies). The module will focus on environmental change in both developed and developing countries and will study prominent and complex phenomena such as climate induced migration, conflict, poverty and inequality. Students will learn how to empirically assess environmental policy and evaluate its effects on a broad range of economic, environmental and social outcomes, using the latest causal inference methods.
The course will provide students with the theoretical and empirical tools to understand the effects of global environmental change on macroeconomic variables such as GDP, inflation and unemployment, by considering the physical risks induced by climate change and their implications for economic growth. The course will then analyse the transitions risks linked to climate change, by describing the effects of a stringent climate policy on market supply, and it will equip students with an understanding of the role of central banks and how monetary policy can be leveraged to overcome the macroeconomic risks driven by climate change. Students will strenghten their empirical skills by using statistical softwares, such as R and Stata, to run empirical macroeconomic analysis.
The module will apply the structure and dynamics of complex networks analysis to various aspects of climate change mitigation and adaptation. Examples include the collaboration network among countries co-signing environmental treaties and citation networks among treaties. The module will focus on the main topological properties of networks, including degree, clustering, centrality, and shortest distances. These properties will then be combined into models of growth that explain how real-world networks acquire and sever links over time. Each network model will be discussed and assessed against a number of real-world problems. For example, which cooperation structures most facilitate the diffusion of climate policies among countries? Do multilateral environmental agreements lead to the emergence of exclusive groups of countries collaborating on environmental issues, and how do these groups evolve over time? How vulnerable is the system to countries¿ misconduct or withdrawal from agreements? To address these and many other problems, the course will develop an interdisciplinary approach to networks by combining current research literature on complex social networks with relevant contributions from environmental economics.
AI is changing the way we work and live and has become an essential part of business and culture. Data analytics 1 course is a core module that introduces basic concepts of artificial intelligence and machine learning. We will cover supervised, unsupervised and reinforcement learning methods. Alongside with studying theoretical aspects of machine learning, students will have an opportunity to develop their own machine learning models based on their preferences (computer vision, natural language processing or time series forecasting).
Data analytics 2 course is a core module that introduces advanced concepts of artificial intelligence and machine learning. Students will learn the following machine learning methods: computer vision with convolutional neural networks, time series analysis with long-short term memory networks, generative adversarial networks for data augmentation and data fusion from different sources. Alongside with studying theoretical aspects of machine learning, students will have an opportunity to apply these methods to climate-related data: historical weather data, ground measurements and satellite imagery. Students will learn how to download and process satellite imagery and provide quantitative analysis of this data to solve a variety of climate-related problems.
This module is the capstone module for the MSc in Environmental Analytics. Students will work in groups and will be required to provide analysis of a problem or question using complex data from an environmental context. This problem can be of micro/macro economics nature, finance or risk management, AI for climate change, or mixed. Each group will be assigned a mentor who will guide the group through the process of structuring the analytical problem, obtaining and organizing the data, data analysis and presentation of results. Students will present initial results as a group to an audience consisting of mentors and practitioners. Final assessment of the module will then be based on individual essays which cover specific aspects of the case and in which the students will be required to reflect on their work in the light of the methods and theories which their learning in the MSc has touched upon.
This course will provide the necessary foundations in Microeconomics and Behavioural Economics to understand potential mitigation and adaptation strategies addressing environmental change and climate emergency. It will start by discussing alternative mechanisms to efficiently manage common-pool resources and public goods, and continue by unraveling the role of incentives and behavioural policies within the environmental policy framework. It will also identify behavioural and micro-economic drivers that can potentially hinder climate action. By employing academic and practical examples, case studies and quantitative analyses it will provide a comprehensive approach to cover key issues and challenges.
Many market stakeholders including firms, corporations, banks, insurance companies, and government agencies are becoming increasingly interested in sustainability, climate risk resilience and climate change mitigation. This module will provide the necessary analytical and empirical fundamentals to unpack and measure climate related factors that result in risk for businesses and corporations. It will then help students understand, evaluate and develop business management practices that are helpful in carbon emission reduction and to address climate risks. Moreover, students will learn analytical tools to measure environmental, social and governance impact of instruments and policies that can be employed by businesses and organisations. This course will cover the steps necessary to: i) define and measure climate risks for businesses, ii) define sustainability of business and organisations, developing metrics to measure their environmental, social and governance impact, ii) set and achieve a carbon reduction goal, including: understanding GHG accounting techniques and standards and reporting GHG, iii) analyse the impact of climate policy on business, and iv) design business management practices conducive to carbon emission reduction.
This 0-credit module covers Mathematics and Statistics topics which are useful for the different quantitative modules and MSc dissertations. The Mathematics topics include: linear and non linear equations, differentiation, growth and discounting and logarithms. The Statistics topics include: descriptive statistics, probabilities and distributions.
Elective modules
This course introduces contemporary theories and the empirical literature of the economics of developing countries with specific reference to public policy delivery. The course will address the problems with public policy delivery in developing countries and what solutions and strategies have been identified in the literature. The course will deal with debates such as centralised and decentralised delivery methods, political economy issues of corruption and state capture, and the role of incentives among politicians and bureaucrats in service delivery.
The module provides a non-technical overview of quantitative methodologies frequently used in finance and international business research. The module is data driven and covers the basics of: Hypotheses testing, OLS and Logistic Regression Analysis, Instrumental Variables, Time Series Analysis, Panel Data Models and Differences-in Differences. The module also teaches how to apply these methods using STATA (a leading econometrics software).
This compulsory module is taught in Semester 2 building on statistical methods in Data Analytics module in Semester 1. The module introduces students to the problem of causal inference, theories of causality and causal effects empirical methods. The focus is on randomised controlled trials in similar settings. Students learn about different econometric techniques to identify causal effects and their strengths and weaknesses. Data collection and organisation of real or natural experiments, data analysis and reporting results to non-specialists is covered.
In this module, we discuss the current trends and analytical frontiers in supply chain management. We have a particular focus on technological innovations that are transforming and restructuring supply chains, including Industry 4.0, IoT, blockchain and other traceability solutions, big data, and robotics. We will discuss the applications of advanced operations research, machine learning, data science, and network science methods, particularly in such data-rich and digital environments. The module will combine reading of academic literature, discussion of case studies, investigation of industrial projects and initiatives, and industry guest lectures.
Risk management is key to an organisation's sustainability. It allows a business to plan for situations ranging from global pandemics to volatile marketplaces, and to put solutions in place that enable them to continue to thrive. The module is designed to provide a complete set of essential management skills to assess and tackle risks.
This module examines aspects of Sustainability Reporting of companies as well as the determinants and the extent of the Integrated Reporting. Specifically the Module focuses on determinants and consequences of Integrated Reporting, Green House Gases regulatory framework and guidance and GreenHouse Gases measurement and reporting
Assessment
Assessment methods include academic coursework and examinations, as well as short class presentations, analyses of data, short written exercises and group work.
Teaching
The School of Business and Management aims to provide a high-quality teaching and learning environment.
This programme's modules have been developed and will be taught by academics who are experts in their fields. You’ll have the opportunity to gain invaluable insight from industry speakers in guest presentations and talks.
You’ll be assigned an Academic Advisor who will guide and support you in both academic and pastoral matters, throughout your course. Our lecturers also publicise their office hours, when they are available to give feedback and advice on coursework, on their online staff profiles.
Where you'll learn
Facilities
- ThinkPod interactive collaboration space with presentation, recording and video conferencing facilities.
- Free subscriptions to publications such as Bloomberg and Financial Times.
- Additional self-study content available through DataCamp.
- Access to a wide range of software packages.
- 24-hour library on campus.
Campus
Teaching is based at Queen Mary’s Mile End campus, one of the largest self-contained residential campuses in the capital. Our location in the heart of London’s East End offers a rich cultural environment.
We have invested £105m in new facilities over the past five years to offer our students an exceptional learning environment. Recent developments include the £39m Graduate Centre, providing 7,700 square metres of learning and teaching space.
The campus is 15 minutes from Central London by tube, where you will have access to many of the University of London’s facilities, including the Senate House library.

About the School
School of Business and Management
The School of Business and Management has a reputation as a socially engaged management school, with an innovative, multidisciplinary, mindful and responsible approach. We invite our students to ask incisive questions, to challenge their assumptions, and to search for solutions to real-world challenges.
We ensure students experience innovative and engaging educational pathways, alongside supportive staff and excellent research facilities.
The School is accredited by the Association to Advance Collegiate Schools of Business (AACSB), which ensures that the highest standards of excellence in teaching, research, curriculum, and learner success are met.
In the most recent Research Excellence Framework (REF 2021), the School of Business and Management dramatically moved up the Times Higher Education rankings. Among 108 UK business schools, the School now ranks:
- 22nd for overall research quality (up from 39th in REF2014)
- 28th for research outputs (up from 34th)
- 12th for research impact (up from 24th)
- 21st for research environment (up from 59th)
Queen Mary is also part of the Russell Group - a body of leading UK universities dedicated to research and teaching excellence.
Career paths
Graduates from this programme will have developed a range of cognitive and practical skills together which will be applicable to different contexts beyond academia. Graduates will be well placed to pursue roles in environmental research and consulting in a variety of industries. This course could lead you to specialised roles such as ESG manager, researcher in AI for sustainability, researcher in AI for climate change, public policy practitioner in climate change and sustainability, and financial sector analyst in ESG.
This course will also give you the foundation and academic knowledge to pursue a PhD in a related field.
Fees and funding
Full-time study
September 2025 | 1 year
- Home: £21,500
- Overseas: £33,500
EU/EEA/Swiss students
Conditional deposit
Home: Not applicable
Overseas: £2000
Information about deposits
Queen Mary alumni can get a £1000, 10% or 20% discount on their fees depending on the programme of study. Find out more about the Alumni Loyalty Award
Funding
There are a number of ways you can fund your postgraduate degree.
- Scholarships and bursaries
- Postgraduate loans (UK students)
- Country-specific scholarships for international students
Our Advice and Counselling service offers specialist support on financial issues, which you can access as soon as you apply for a place at Queen Mary. Before you apply, you can access our funding guides and advice on managing your money:
Entry requirements
UK
Degree requirements
A 2:1 or above at undergraduate level in in any subject, provided the degree contains satisfactory study of Mathematics and Statistics. Subjects likely to contain sufficient quantitative elements include Accounting, Mathematics, Sciences, Engineering, Computer Science, Economics and Finance.
Additional information
Students from less quantitatively oriented degrees, such as Management and Politics, are welcome if they have focused on the more quantitative elements of those degrees.
Find out more about how to apply for our postgraduate taught courses.
International
English language requirements
The English language requirements for our programmes are indicated by English bands, and therefore the specific test and score acceptable is based on the band assigned to the academic department within which your chosen course of study is administered. Note that for some academic departments there are programmes with non-standard English language requirements.
The English Language requirements for entry to postgraduate taught in the School of Business and Management falls within the following English band:
Band 4: IELTS (Academic) minimum score 6.5 overall with 6.0 in each of Writing, Listening, Reading and Speaking
We accept a range of English tests and qualifications categorised in our English bands for you to demonstrate your level of English Language proficiency. See all accepted English tests that we deem equivalent to these IELTS scores.
Visas and immigration
Find out how to apply for a student visa.