Conference Agenda

This paper examines optimal monetary policy in response to weather shocks in a two-sector New Keynesian model calibrated for Peru, a climate-prone economy where a rural agricultural sector coexists with a modern manufacturing sector. While adverse weather shocks disproportionately impact the agricultural sector, monetary policy primarily influences the modern sector. Following an adverse weather event that triggers a recession and inflationary pressures, targeting the production price index (PPI) inflation in the manufacturing sector rather than the consumption price index (CPI) inflation appears to be optimal for the Central Bank, as it reproduces the dynamics of the Ramsey planner.

Shifts in climate policy stringency will have heterogeneous effects on firms' profitability. Does the market price this risk? This paper provides new evidence on this question, utilising a supervised machine learning algorithm to construct a firm-level measure of climate policy risk exposure. Firms exposed to climate policy risk will have negative abnormal returns on climate policy announcement days. I build a set of such dates and characterize abnormal return responses using Risk Factors discussions in 10-K filings. The algorithm uncovers predictors of policy risk exposure in the text which are used to construct an exposure score for each firm. This exposure score is correlated with emissions, environmental lobbying behaviour, and is predictive out of sample. Higher exposure is not associated with a premium. Green preference shifts are considered as a mechanism to rationalize this result. I find that empirically identified preference shocks can partly explain the lack of a climate policy risk premium.

We explore how carbon pricing affects corporate financial performance during Phase 3 of the European Union Emissions Trading Scheme (EU ETS). We find that the relationship between carbon prices and stock prices depends critically on the proportion of verified emissions covered by freely allocated ETS allowances: For firms with greater permit coverage (shortfall), an increase in daily carbon prices is associated with higher (lower) contemporaneous stock prices. We provide additional evidence that firms with a significant permit shortfall reduce verified emissions in the EU, but not global emissions.

A fundamental and seemingly easy question in climate finance remains unanswered: how to best measure companies’ climate transition risk. Most authors glimpse over this first order question, however, as we show in this paper, choosing different transition risk metrics can lead to significantly different results. We employ a brand-new dataset containing for the first time reported EU taxonomy alignment as a proxy for companies transition risk. We compare taxonomy alignment to commonly used CO2 emission data and E scores. We also utilize TRBC codes as a granular sector/technology classification to measure transition risk. We find a strong divergence in transition risk metrics for similar companies. Most notably, taxonomy-based risk measures are negatively correlated with inverted emissions and uncorrelated to E-scores. In a subsequent step we go beyond merely documenting this divergence by also evaluating the different transition risk proxies. Our empirical approach uses the return sensitivity of 6 transition risk metric based Brown Minus Green portfolios against news-based indexes which track unexpected shocks to transition risk. Thereby, we are able to show which transition risk metric is more/less sensitive to transition risk shocks and therefore better suited to ultimately measure climate transition risk of firms. We find that only taxonomy and TRBC based portfolios are able to measure green firms’ climate transition risk. Neither the widely popular emission intensity based or E-score based transition risk measures can accurately measure transition risk of green companies. Interestingly, no chosen risk metric is able to create a brown portfolio, which is significantly related to negative transition risk shocks. This might either show weaknesses across all risk metrics or alternatively, might be explained by investors expectation that brown firms will successfully lobby against strong climate policy or that governments will bail out firms in case transition risk shocks will lead to stranded assets. The contributions of this paper are manifold. First, we empirically compare different proxies for climate transition risk with the most comprehensive dataset available. Second, we provide recommendations on which transition risk metric performs better/worse and under which circumstances. Third, we propose an approach for evaluating the quality of transition risk measures, which can also be utilized for comparing other risk measures.