Conference Agenda

In this paper, we present a new methodological approach to examine the long-term cost burden of carbon pricing, considering heterogeneous technology adjustments and two different price paths. In particular, we link an energy systems model with a micro-simulation model based on random forests and analyze the distributional effects in the German heating and transportation sectors up to 2050. Our analysis shows that there will be a sharp decline in the use of fossil fuels after 2030, especially among low-income households. We find that initially, carbon pricing has largely regressive effects that can be reversed by redistributing revenues via a lump-sum payment. Due to the heterogeneous nature of technology adoption rates, we still observe distributional effects within income quintiles for both price paths. A high carbon price path leads to a higher cost impact in 2030, which afterwards declines due to investments in climate neutral technologies. The low price path has a steady average cost burden, which is significantly lower in 2030, but remains high up until 2050.

We introduce a novel, fully nonparametric estimation framework to process data from survey-based environmental valuation with a binary, referendum-style choice question, traditionally referred to as Contingent Valuation. Our approach combines the construction of choice probabilities via Random Forests (RFs) with welfare predictions via common distribution-free estimators. While popular as back-of-envelope alternatives to parametric estimation, these distribution-free methods are poorly suited for the incorporation of observation-specific heterogeneity. In contrast, our Random Forest Non-Parametric (RFNP) approach produces willingness-to-pay (WTP) estimates at the individual level, conditioned on a potentially large set of explanatory variables. Furthermore, our predicted choice probabilities as well as welfare estimates come with well-defined asymptotic properties. Using simulated data, we find that the RFNP estimator is robust to nonlinearities in the WTP function and can compete with correctly specified parametric models in terms of asymptotic efficiency. In our empirical application within the context of biodiversity enhancements on open land in the United Kingdom, we show that the RFNP is immune to negative WTP predictions by construction, and produces reasonable and efficient lower bound estimates for individual and sample-aggregated WTP. It can also generate welfare predictions that allow for long tails in individual WTP, without having to impose this feature on all observations. Our framework is well-suited for numerous extension, and readily implemented with existing software packages.

Benefit Transfer (BT) has evolved as the dominant non-market valuation method for large-scale environmental benefit-cost analyses, including those required of U.S. federal agencies. Yet, even best-practice approaches for BT based on Meta-Regression Models (MRMs) typically exhibit poor predictive fit and out-of-sample efficiency. This article introduces Random Forests (RFs) for nonparametric estimation of MRMs and construction of BT predictions. We compare the performance of a variety of RF models to current best practice approaches for BT, including a globally-linear MRM and Locally-Weighted MRM (LWR). We find that forest-based models substantially improve the within-sample accuracy of welfare predictions and tighten confidence intervals of predicted benefits for out-of-sample transfers. The best-performers reside within the family of Local Linear Forests (LLFs), essentially a hybrid approach that combines elements of RFs and LWR. We also examine the utility-theoretic properties of each specification. Results suggest that this new approach has the potential to substantially improve BT accuracy for environmental policymaking without sacrificing theoretic properties, while simultaneously reducing econometric and computational difficulties relative to leading alternatives.

The Circular Economy (CE) paradigm has emerged as a novel approach aimed at advancing sustainable development by concurrently enhancing economic, environmental, and social objectives. However, despite the growing interest therein, many areas remain underexplored, particularly regarding the relationship between the dynamics of agricultural commodity markets and the circular transition. This study aims to address this gap by investigating the influence of commodity market dynamics on the circular transition of publicly-listed commodity-producing companies. A panel data of the 60 largest listed companies in the agricultural sector worldwide, with yearly observations over a 2018-22 timespan, was selected to compute a Circular Performance Corporate-level Indicator (CPCI); then, both corporate and market variables were employed to explain the latter’s level. Results provide insights into the interplay between agricultural commodity markets and corporate circular practices, shedding light on the role of commodity markets in influencing circularity. These findings contribute to a deeper understanding of the driving forces behind the adoption of more circular practices, thereby offering invaluable insights for policymakers and businesses striving to navigate towards a circular future and fostering a sustainable global economy.

Snow and red king crabs are established invasive species in the Barents Sea that support significant commercial fisheries. Invasive species have ecological impacts but represent opportunities for value creation as climate change may undermine more traditional fisheries. However, climate change could also have negative impacts on the crabs. Landing or ex-vessel prices exhibit large variations over short time spans, suggesting a non-standard or complicated market structure, and we consider regression trees in random forests to identfy important factors for prediction of these prices. The most important variables for snow crab prices are landing site and prices of other landings the same day. We find little evidence of impact on snow crab prices from landed volumes and prices of red king crab, suggesting limited substitution in the market. An index of marine heatwaves, a phenomenon correlated with climate change, is the last variable on the ranking of importance for price determination. Transition to quota regulation, however, shifted the price up with 17% on average.