Global institutional changes (GICs), having influenced energy prices, led to a steady upward trend in carbon prices on the EU ETS. The aim of the article is to assess the changes in the relationship between carbon prices and energy prices under GICs. The Bai–Perron tests for structural breaks identified two dates as the breakpoint, 21 April 2016 and 21 September 2020. We test the hypothesis that powerful external factors (GIC) are changing the trend pattern of the carbon price time series. New pricing rules of the carbon price are being formed after the breakpoint. We use daily observations from 4 January 2010 to 1 September 2022. We use GARCH models with multiple stationary time series to discover a relationship energy price with the carbon price before and after the break points. We found that three models for two breakpoints better describe the relationship between carbon prices and energy prices than two models for one breakpoint, much less one model for the entire period. We find that the carbon price depends on energy prices, especially on the price of oil, in a statistically significant way, but the gas price is not statistically significant after 21 September 2020.

Russia is taking the first steps in the formation of an emissions trading system. In this article, we studied the impact of carbon risk on Russian stock returns. We link carbon risk to CO₂ emissions and air protection costs. We suggest that carbon firms are exposed to carbon risk and hence require a premium in stock returns. We use an approach based on the asset pricing methodology for carbon, carbon-free, and “carbon-minus-carbon-free” portfolios. Based on the Newey–West estimate, we perform a linear regression analysis for the period from January 2014 to December 2021. We find a positive and statistically significant carbon premium. This means that carbon firms show higher expected returns. Carbon risk does not have a statistically significant impact on the carbon premium. The carbon firms’ stock returns are not sensitive to CO₂ emissions and air protection costs. Our analysis shows that a quarter of the carbon premium is explained by the market premium and is not sensitive to size, value, and momentum premiums. Our results inform policymakers and investors about the implications of environmental regulation. Policymakers should take into account the results obtained in the development of national climate and, in general, environmental policies.

Active anthropogenic activity is one of the main causes of serious environmental problems that hinder the development of the Arctic zone of Western Siberia — the most important resource potential of the country. The implementation of climate projects based on natural solutions is one of the areas of envi-ronmentally oriented economic growth. The formation and development of sequestration business is possible if there are conditions ensuring its economic efficiency. The article estimates the costs of absorbing of 1 ton of greenhouse gases during the implementation of climate projects in the northern taiga of Western Siberia. To achieve the goal, the predicted values of carbon sequestered from the atmosphere were calculated for different project scenarios. The CO2 effect was measured and the costs of carbon sequestration by tree and shrub communities of willow, alder and pine were analyzed. Alder monocultures showed the largest volumes of CO2 uptake and the lowest costs per carbon unit according to the carbon discounting model. CO2 duration analysis shows the sensitivity of the cost per carbon unit of a climate project based on alder monocultures to changes in the discount rate. The break-even price of a carbon unit is substantiated, which allows comparing it with the market price and drawing conclusions about the economic efficiency of the climate project on carbon sequestration. The study results provide practical recommendations for making decisions about investing in nature-based climate projects for low-carbon development in northern Western Siberia. The methodological approaches disclosed in the article can be used in other regions of Russia.

The key instrument for regulating the reduction of greenhouse gas (CO2) emissions is the emissions trading system (ETS), which Russia chose to create. One of the key issues for the ETS participants is the mechanism of CO2 pricing. Energy sources are the most important factors in the CO2 price. The study of the relationship between them and the CO2 price at foreign ETSs gives contradictory results. The paper investigates the relationship between energy variables and CO2 price using exploratory analysis tools as the first stage of machine learning. The univariate analysis showed that the laws of price distribution of futures contracts for CO2 and coal are further from normal in comparison with the prices of futures contracts for gas and Brent oil. Logarithmization improved the statistics of the data. Bivariate analysis showed a close relationship between the prices of CO2 and coal futures contracts. The price data for the other energy variables showed a weak to moderate relationship. Correlation analysis, taking into account the different time lags between the energy variables and CO2, indicates that it is appropriate to include past energy price information in the model. The close linear relationships of the energy variables suggest exploring opportunities to reduce the dimensionality of the data. Exploratory analysis revealed groups of data that would be appropriate to describe with different machine learning models. The presence of data groups in the resulting variable indicates the presence of other CO2 pricing factors that should be identified and taken into account in modeling.