Statistical modelling and applications for sustainable-development goals
Abstract
In 2015, all United Nations member states adopted the Agenda 2030, which consists of 17 Sustainable Development Goals to create a better world in terms of social, economic, and environmental development by 2030. Numerous countries and governments have initiated measures to achieve these goals, including curbing traditional energy consumption, advancing renewable energy development, cutting carbon emissions, and addressing climate risks. In this thesis, statistical modelling techniques are developed to examine economic and financial challenges encountered in the pursuit of sustainable development.
The first study in this thesis explored the dynamic link between Canada's economic growth and renewable energy use using a panel autoregressive distributed lag model framed within the neoclassical production function. We incorporated an OECD-based indicator to identify Canada's economic phases and used the Pooled Mean Group method to analyse long-term and short-term correlations. The findings show a unidirectional causality going from renewable energy to economic growth only during expansion phases in the short-term, highlighting the need for policies that recognise the nonlinear connection between renewable energy and economic growth.
In our second study, we analysed the US stock market's reaction to both types of physical climate risks (chronic and acute) as well as transition climate risk. Using a multivariate hidden Markov model, we employ two climate variables and one sentiment variable to build an indicator for the chronic risk. Acute and transition risks are deemed to be reflected in the natural disaster and policy news, respectively. We evaluated their effects on stock returns using an event study methodology. Our results indicate that some sectors are more susceptible to climate risks than others. Firms with lower environmental scores face greater exposure, affecting their stock returns negatively. This implies that enhancing environmental performance can boost a company's financial resilience against climate risks.
We modelled the Emission Allowance price dynamics in the EU Emission Trading System in our third study. Capitalising on prior studies, we integrated a Markov-switching mechanism into four stochastic models. Parameters were estimated using change of reference probability measures alongside the EM algorithm. The fitting accuracy and usefulness of our model were assessed through an out-of-sample forecasting and the pricing of European-style call options. Notably, the Markov-switching Geometric Brownian motion model surpassed both the non-Markov switching and other Markov-switching stochastic models in in-sample and out-of-sample performance.
The fourth study in this research work investigated the influence of green bond issuance on an issuer's environmental performance, utilising an interrupted time series with a control group. This study also looked into how companies' financial characteristics and specific green bond data can enhance an issuer's environmental performance, using both the Random Forest and Generalised Additive models. Our findings indicate that the environmental performance of most issuers improves following the issuance of green bonds. Additionally, we discovered that certain company's characteristics as well as the specific features of the green bonds play significant roles in determining the efficacy of green bonds in enhancing a company's environmental performance. These findings are valuable for investors when selecting green bonds.