A data-driven system research programme to curb corporate environmental impact
I am an Advanced Research Fellow working at the interface of sustainability science, social science, and data science. The overarching goal of my research agenda is to use quantitative data, statistics and causal reasoning to provide knowledge and tools that can be used by scholars, industry leaders and policymakers to facilitate the transition towards a sustainable economy.
Economic activities are the primary sources of greenhouse gas emissions and depletion of natural resources. Over the last few years, and in particular from the 2015 COP 21, an increasing number of companies have pledged to reduce their environmental impact by aligning their emissions with global climate targets, reducing deforestation, water consumption and land pollution. Yet, the emissions pathways of most firms are still misaligned with climate goals and biodiversity loss, deforestation, and ocean acidification due to unsustainable production are increasing at unprecedented speed. Hence, despite a decade-long series of commitments and the unparalleled flow of resources toward supposedly sustainable firms, the private sector is failing to deliver the transition toward a sustainable economy.
The ineffectiveness of voluntary businesses environmental actions, such as the inability to transition away from fossil fuels in the hard-to-abate sectors, or to set ambitious emission targets compatible with the goal of the Paris Agreement, shows that businesses alone are not able to make the necessary organisational transformations to align their operations with societal expectations. Indeed, the challenges associated with lowering firms’ environmental impact are affected by many interacting factors.
Overall, transitioning to a sustainable economy requires implementing systemic changes across multiple societal actors. In this context, my research agenda aims to identify how interconnected changes in management practices, investors’ preferences, and environmental policies can foster the transition to a sustainable economy.
The overarching goal of my research agenda is to use system thinking, statistics and causal inference to identify the root causes of the private sector’s inability to deliver the transition to a sustainable economy.
To achieve this goal, I lead a data-driven system research programme which aims to: (a) develop machine learning approaches for identifying and quantitative characterising what companies do to lower their environmental impact; (b) investigate the relationship between firms’ actions and their outcomes (i.e., their environmental impact); (c) identify the systemic and internal drivers of corporate sustainability behaviour. In particular, the role of investors, policymakers and group dynamics as driving factors of sustainable management practices.
My approach is designed to help (1) business leaders to better understand how to change their firm’s climate strategies to reduce their environmental impact, (2) market participants, to allocate capital towards companies that enact effective sustainability behaviours, and (3) to policymakers to devise effective intervention strategies that incentive effective behavioural changes.
Currently, I am working on several research problems summarised below.
- What do companies do to lower their emissions?
- How do competitive forces shape firms’ sustainability behaviour?
- How can companies sustain economic growth while tackling climate change?
- What is the effect of environmental policies on corporate sustainability behaviour?
- Can system thinking help organisations align their emissions with global climate targets?
- When do reporting on sustainability initiatives convey material information to investors?
Research areas: Sustainable management practices, System research, Sustainable finance, Sustainable Development Goals, Climate change, Data science, Applied machine learning
My background is in applied statistical and causal inference in sustainability science and finance. Before joining Imperial College, I was a postdoctoral research associate at DCI, LLC, an independent asset management firm now acquired by Blackstone Credit. At DCI, I worked on statistical and causal inference methods to explain and predict firms’ capital structure dynamics. I obtained my Ph.D. at the Massachusetts Institute of Technology. My Ph.D. research focused on developing on nonparametric statistical inference methods to study ho biological populations respond and adapt to changing environments (link to Ph.D. thesis). I have a Master’s degree in Physics from the University of Pisa and a Bachelor’s degree in Physics and Astrophysics from the University of Rome “Sapienza”