This study using VECM methodology constructs a model of the Russian economy from 2010 through 2022 across four variables: consumer price index, ruble exchange rate against the US dollar, consumer demand, and the RUONIA interest rate. A short-term inflation forecast through the end of 2022, which predicts that annual inflation will drop to 10.1 % by December 2022, is arrived at based on this model. The model is then applied to determine the contribution to March inflation from the price shock that was not attributable to the dynamics of the fundamental variables. The point estimate of the shock came to 6.6% of the 7.4% seasonally adjusted March inflation, and this implies that about 89% of the inflation surge was due to one-off factors (logistics, switching to other suppliers, etc.). The accuracy of the VECM model forecast in the current economic situation (high inflation volatility) turns out to be higher than the accuracy of univariate benchmark models over a horizon of one to three months. Forecasts derived from the proposed VECM model applied to vintage data for the period from December 2021 to June 2022 turned out to be close to the consensus forecasts of analysts and the Bank of Russia, which had been based on a comparable information set. The forecasts constructed with the model project a significant slowdown in inflation based on data starting from April, an outcome which explains the rapid key rate cut by the Bank of Russia from 20% in early April to 8% by the end of July. Even when inflationary trends are rapidly changing, the proposed factor model facilitates prompt and relatively accurate short-term inflation forecasts, which can be used to inform monetary policy choices.

This study examines the usefulness of forest climate projects as a vehicle for investment in the domestic carbon market. The main objective is to identify the prerequisites for establishing a Russian carbon market, which would set the stage for sustainable reduction of greenhouse gas emissions. The article begins by analyzing the global climate agenda in detail, highlighting the functions and significance of forest climate projects in regulated carbon markets, and describing the market dynamics for carbon units. Since 2021 the Paris Agreement’s provisions have been implemented, the EU Green Deal has been initiated, forestry and land use regulation have became part of the EU emissions trading system, and the CORSIA compensation mechanism for emissions from international air travel has been launched. The article assesses the potential for a Russian carbon market in forestry units. The Siberian regions and Far Eastern federal districts offer the greatest opportunity for carrying out forest climate projects. That opportunity depends upon improving crucial components of forest management in Russia’s state forest holdings in order to increase the carbon stock and reduce greenhouse gas emissions; this would entail reforestation of clearcut areas (with additional measures that enhance carbon storage and prevent emissions from unfavorable factors) as well as planting new forests. The article then elaborates a method for estimating and developing the carbon market potential of forestry units in Russia in order to evaluate the prospects for implementing such projects (a carbon budget). A calculator for carbon credits has been prepared for use by potential Far East investors, and services are available for them to select a forest site via online auctions and execute transactions online.

Introduction of the EU Carbon Border Adjustment Mechanism over the period from 2023 to 2026 together with corporate commitments to achieve carbon neutrality and carry out commercial decarbonization have markedly increased interest in assessing the potential of carbon sequestration by Russian forests as a possible way to achieve decarbonization and facilitate Russian exports. The prevailing opinion in business circles is that a significant net positive carbon balance from Russia’s forests could circumvent the need for businesses to make costly reductions in their direct CO₂ emissions. However, international decarbonization strategies and standards do not concur with that idea. Direct emissions will have to be reduced. Offset mechanisms, whose benefits are calculated as the difference between a baseline and an improved scenario for forest management (the principle of additionality), will compensate for only a part of the emissions. The experience of Canada is indicative, as it consistently implements measures to decarbonize industry without regard to the absorption of CO₂ by its forests. Even though Canada has climatic conditions, forest growth, and population density similar to Russia’s, its policy is not dependent upon revising estimates of net CO₂ absorption by forests upward. Forestry priorities in Russia, including reforestation, should instead be gradually shifted from managing commercial forests for harvesting timber to reducing all forest fires. Leased and non-leased forests should both be included, and reforestation that favors deciduous species and mixed forests should be given a higher priority. It is also necessary to remove barriers to forestry in agricultural forests and to plan for implementation of projects directed at improving both forestry and climate on the land leased out from the holdings of the State Forest Fund as well as on agricultural tracts, including those now overgrown by forests.

The article examines the effect of growth rates for gross regional product (GRP) and of fluctuations in oil prices and currency exchange on the sustainability of Russia’s regional budgets from 2005 to 2019. Sustainability estimates for various regions are calculated with the sectoral structure of their GRPs taken into account. The hypothesis that the response of the primary budget balance to changes in macroeconomic variables depends upon the amount of a region’s debt is also explored. In order to test that hypothesis, a fiscal reaction function is arrived at using both a two-way fixed effects model and also a dynamic model on panel data. The results demonstrate that regions specializing in agriculture and those with emerging economies exhibit relatively weak fiscal sustainability when sustainability is judged by how the accumulation of debt during the previous period affects the primary budget balance. However, regions with extractive and manufacturing industries, as well as those without obvious specialization, have a significant positive fiscal reaction, which indicates that their budgets are sustainable. The primary budget balance reacts positively to increasing oil prices regardless of the amount of regional debt in any of the regions, except for the regions with emerging economies. The response to changes in the exchange rate (currency depreciation) is negative for all regions on average, while there is no significant response in the primary budget balance to changes in GRP growth rates. The robustness of these results is tested by evaluating alternative specifications for models. The findings of the study can be useful in making recommendations for improving the resilience of regional budgets when faced with external macroeconomic shocks or with the possible development of fiscal rules at the subnational level.

This study analyzes factors influencing the bankruptcy of companies in the Russian manufacturing industry during the period from 2012 to 2020. Logistic regression was used as an econometric tool for modelling the probability of default by companies. Because there was no standardized database indicating the dates when bankruptcy proceedings for Russian companies commenced, that information had to be obtained independently by the authors from data provided by the SPARK system. Both legal bankruptcy proceedings and the economic reasons for a company’s insolvency, which can be ascertained from financial statements, are treated as a dependent variable. This approach in effect broadens the definition of bankruptcy by permitting a greater number of data points derived from instances in which a company is in financial difficulties but does not go through a legal bankruptcy procedure. The results indicate that financial indicators of profitability, liquidity and business activity play a significant role in explaining the probability of default by Russian manufacturing com­panies. Two definitions (simple and extended) are applied to corporate governance and ownership structure in order to assess their impact on the probability of bankruptcy. One result is that including these indicators increases the predictive power of the models under either definition. A second outcome is that these indicators have a consistent and significant correlation with the probability of bankruptcy when the causes of economic insolvency are examined. However, that significance is not evident for all sub-sectors of manufacturing in models which apply a simple definition of default. Combining ownership with management tends to increase a company’s stability, but extremely large concentrations of share ownership increase the probability of bankruptcy.