Climate change is predicted to substantially alter forest growth. Optimally, forest owners should take these future changes into account when making rotation decisions today. However, the fundamental uncertainty surrounding climate change makes predicting these shifts hard. Hence, this paper...

Climate change is predicted to substantially alter forest growth. Optimally, forest owners should take these future changes into account when making rotation decisions today. However, the fundamental uncertainty surrounding climate change makes predicting these shifts hard. Hence, this paper...

Modeling the price risk of CO2 certificates is one important aspect of integral corporate risk management related to emissions trading. The paper presents a risk model which may be the basis for evaluating the risk of emission certificate prices. We assume that the certificate price is...

The empirical literature is very far from any consensus about the appropriate model for oil price forecasting that should be implemented. Relative to the previous literature, this paper is novel in several respects. First of all, we test and systematically evaluate the ability of several...

The empirical literature is very far from any consensus about the appropriate model for oil price forecasting that should be implemented. Relative to the previous literature, this paper is novel in several respects. First of all, we test and systematically evaluate the ability of several...

This research uses annual time series data on CO2 emissions in India from 1960 to 2017, to model and forecast CO2 using the Box – Jenkins ARIMA approach. Our diagnostic tests indicate that India CO2 emission data is I (2). The study presents the ARIMA (2, 2, 0) model. The diagnostic tests...

We propose a model based on statistical learning techniques to predict unreported corporate greenhouse gas emissions, which generates considerably better results than existing approaches. The model uses one linear and one non-linear learners only, which reduces its complexity to the minimum...

This paper uses machine learning to improve the prediction of corporate emissions so that financial regulators and investors can make better decisions about climate transition risk. The need for predictions arises because only a subset of global companies report emissions. The novelty is to use...

This study investigates the impact of environmental variables, such as carbon emissions and temperature anomalies, on cryptocurrency returns. While existing research has primarily focused on economic and financial determinants, the influence of environmental factors remains underexplored. Using...

Modeling the price risk of CO2 certificates is one important aspect of integral corporate risk management related to emissions trading. The paper presents a risk model which may be the basis for evaluating the risk of emission certificate prices. We assume that the certificate price is...