Cost and benefits of deep decarbonization in Russia

With the new Paris climate agreement, 185 of 197 nations have committed to lower emissions of planet-warming greenhouse gases. The intent is to limit global temperature growth within 2 degrees Celsius (°C), with a hopeful target of 1.5°C. At the same time, a special report from the International Panel on Climate Change (IPCC) indicates that large emission reductions, in fact, must be achieved by 2030 if the temperature increase is to remain below 1.5°C. This goal requires every country to radically cut their greenhouse gas emissions by rebuilding both their energy supply and end-use sectors. Even bigger challenges confront those countries which export fossil fuel resources, as they must also find new sources of economic activity to replace revenues that will be lost from the significantly reduced energy sales. The overall economic impact of this transformation is hard to quantify. On the one hand, decarbonization requires an initial set of large-scale policy, program, and research and development expenditures. It will also entail higher upfront investments in energy efficiency and alternative energy resources. Based on conventional wisdom, these outlays will create an initial burden on the economy. On the other hand, the additional infrastructure investments will also stimulate economic activity, reduce future energy expenditures and also provide an array of other non-energy benefits. In this paper, we propose a thought experiment that explores the idea of prospective positive net economic impacts of decarbonization strategies for an energy-producing nation. Our results suggest that the positive productivity benefits of decarbonization strategies can overcome negative costs in both the short and long terms. We also note additional effects that are consistent with the officially announced long-term goals of modernization and reducing the Russian economy’s dependence on revenues from energy and raw material exports

Updating of Input-Output tables in Russia by machine learning methods

Relevance: Input-output tables are the basis for many types of analysis of the real sector, which are necessary to build a well-thought-out long-term and short-term policy. Evaluation of input-output tables is an expensive and time-consuming procedure. At the same time, national statistical agencies publish additional forecast information, which makes it possible to extend the input-output tables, for example, output and intermediate consumption by sector. The main methods of extending the RAS tables (or its modification GRAS) and Cross Entropy, use data on intermediate demand, the calculation of which requires additional time-consuming work. The use of information only for the previous period and the current period is the main disadvantage of this method. In recent decades, machine learning methods have been gaining popularity, the main advantage of which is finding relationships that can be hard to identify, for example, due to the large dimension of the task or the lack of evidence of cause-and-effect relationships. These methods have proven themselves well in all kinds of image recognition tasks, voice-to-text conversion, and so on. Currently, attempts are being made to apply machine learning methods to economic problems. The application of machine learning methods to the task of updating inputoutput tables carries a scientific novelty. The purpose of the study is to extend the input-output tables by machine learning methods. The method of extending the input-output tables using convolutional neural networks is the result of the work, as well as the forecast of the coefficients of the direct cost matrix for Russia. Conclusion: the use of input-output tables can improve the quality of forecasts of input-output tables. Recommendations: it is necessary to continue the research in this direction

Updating of Input-Output tables in Russia by machine learning methods

 Input-output tables are the basis for many types of analysis of the real sector, which are necessary to build a well-thought-out long-term and short-term policy. Evaluation of input-output tables is an expensive and time-consuming procedure. At the same time, national statistical agencies publish additional forecast information, which makes it possible to extend the input-output tables, for example, output and intermediate consumption by sector. The main methods of extending the RAS tables (or its modification GRAS) and Cross Entropy, use data on intermediate demand, the calculation of which requires additional time-consuming work. The use of information only for the previous period and the current period is the main disadvantage of this method. In recent decades, machine learning methods have been gaining popularity, the main advantage of which is finding relationships that can be hard to identify, for example, due to the large dimension of the task or the lack of evidence of cause-and-effect relationships. These methods have proven themselves well in all kinds of image recognition tasks, voice-to-text conversion, and so on. Currently, attempts are being made to apply machine learning methods to economic problems. The application of machine learning methods to the task of updating inputoutput tables carries a scientific novelty. The purpose of the study is to extend the input-output tables by machine learning methods. The method of extending the input-output tables using convolutional neural networks is the result of the work, as well as the forecast of the coefficients of the direct cost matrix for Russia. Conclusion: the use of input-output tables can improve the quality of forecasts of input-output tables. Recommendations: it is necessary to continue the research in this directio

Environmental tradeoffs of agricultural growth in Russian regions and possible sustainable pathways for 2030

The paper analyses the current ecological consequences of agricultural growth in Russia’s main regions (oblast level) during 2011–2019. Our main hypothesis was that local environ­mental risks, like waste concentration, would be closely related to global climate risks such as greenhouse gas (GHG) emissions from the production of crops, meat, milk, eggs, and from land use change (LUC) activities leading to a larger carbon footprint. We first analyze official data for agricultural waste and find that 30% of it is concentrated in just two regions (Belgorod and Kursk), while they produce only 10% of agricultural value of Russia. Next, we find that manure nutrients have a high concentration in regions where the livestock production is not balanced with appropriate nutrient use on croplands (Dagestan, Astrakhan, Leningrad, and Pskov regions) which might lead to the pollution of soils and local waters. Next, we test the GLOBIOM partial equilibrium model to evaluate proper agricultural protein production quantities in Russian regions and respective GHG emissions from crop, livestock and land use change activities. We find that 21% of the GHG emission in 2019 came from the conversion of former abandoned agricultural land into cropland (starting from 2011). While some regions such as Krasnodar, Rostov, and Stavropol increase productivity with low carbon footprint, others, like Amur and Bryansk, increase production by cropland expansion without respective productivity growth which leads to higher carbon footprint. Our results for livestock operations show that the main hypothesis did not hold up because regions which increase meat production, like Belgorod, Kursk, Pskov, and Leningrad, have a lower carbon footprint due to the production of pork meat and poultry which have lower GHG emissions due to specific digestion. On the other hand, these regions experience a higher environmental footprint due to the large concentration of waste which could be harmful for local eco­systems. Finally, we use the model to project possible future development up to 2030. Our results show the possible growth of crop and livestock products in most of the regions driven by external demand for food. The extensive scenario shows additional GHG emissions from cropland expansion, while the intensive scenario reveals a larger growth rate accompanied by productivity growth and lower carbon footprint, which is essential in harmonizing the current agricultural and climate policy of Russia

Forward-Looking Energy Elasticity Parameters for Nested CES Production Function

Elasticities of substitution are the key parameters in CGE modeling. However an estimation and validation of the parameters is not straightforward. One ways is to use a historical data. A data on technological shifts between types of fuels and capital and fuels, observed in the past, could be used to calibrate or econometrically estimate the elasticity coefficients. However, historical trends do not describe all the possible investment options which were available. Moreover such estimates will be based on investment decisions made in particular economic condition, available technological/investment options in the time of decision, and under existing policies. Using such parameters for evaluation of future policy options involves undesirable (and unavoidable) assumption that future technological options are equal or similar to those in the past. A variety of new technological options will be disregarded from the analysis. In the paper we develop a methodology to estimate energy elasticity parameters for a nested CES production function involving “Bottom-Up” technological forward looking models. The bottom-up energy modes have an extensive representation of energy sector, they take into account currently available and expected technological options, but consider only part of an economy and lack connectivity with other sectors, f.i. do not provide a demand respond. Therefore their application is usually limited to the energy sector. There are several attempts to connect the top-down (CGE/AGE) and bottom-up models known as “soft” or “hard link”. However both methodologies require significant reduction of the models’ scale or some compromise in connectivity between the models. The methodology proposed in the paper is another way of hybrid modeling where bottom-up model is required to calibrate parameters for a top-down model. It is expected, the energy nest of a CGE model should provide results similar to the bottom-up model

Bayesian Estimation of Input-Output Tables for Russia

The paper gives an overview of a research started in December 2011 on stochastic estimation of input-output dat

Bayesian Estimation of Input-Output Tables for Russia

The paper gives an overview of a research started in December 2011 on stochastic estimation of input-output dat

Bayesian Updating of Input-Output Tables

The paper continues efforts on developing Bayesian method of updating IO tables, presented by the authors on the 16th Annual Conference on Global Economic Analysis, and extends the methodology and results in several ways. In the current paper, we test our methodology on the “long” survey based IRIOS tables. We compare two point estimates of the Bayesian method of “unknown” IO table: posterior mode and posterior mean with estimates, which come from alternative methods popular in the literature. Than we discuss how to construct an appropriate creditable set for IO coefficients. We also upgrade and extend estimates of SUT tables for Russia. The work consists of three parts. The first part of the paper devoted to conceptual framework for updating, disaggregating and balancing IO tables. Compared to previous paper we improve our sampling methodology by using conjugate vector of Hessian matrix of prior distribution to avoid high autocorrelation of MCMC chains. In addition we use eigen values for proposal density for MCMC algorithm. This technique provides good convergence properties of Markov chains. In the second part of the current paper, we test our methodology on the “long” survey based IRIOS tables (van der Linden and Oosterhaven, 1995). We treat the last table for each country as unknown and estimate it with the Bayesian method using all previously available matrixes for constructing prior distribution. We consider two point estimates of “unknown” IO table: posterior mode and posterior mean. To find posterior mode we use nonlinear optimization techniques, to explore posterior distribution we use proposed MCMC method. Posterior mode robustly outperforms competitive methods, popular in the literature, according to different closeness statistics. Posterior mean perform slightly worse than posterior mode. We conclude that point estimate of Bayesian method at least is compatible with the other methods on real data examples. The main contribution of our method i..

Decarbonizing Russia: Leapfrogging from Fossil Fuel to Hydrogen

We examine a different approach to complete the decarbonization of the Russian economy in a world where climate policy increasingly requires the radical reduction of emissions wherever possible. We propose an energy system that can supply solar and wind-generated electricity to fulfill demand and which accounts for intermittency problems. This is instead of the common approach of planning for expensive carbon capture and storage, and a massive increase in energy efficiency and, therefore, a drastic reduction in energy use per unit of Gross Domestic Product (GDP). Coupled with this massive increase in alternative energy, we also propose using excess electricity to generate green hydrogen. Hydrogen technology can function as storage for future electricity needs or for potential fuel use. Importantly, green hydrogen can potentially be used as a replacement export for Russia’s current fossil fuel exports. The analysis was carried out using the highly detailed modeling framework, the High-Resolution Renewable Energy System for Russia (HIRES-RUS) representative energy system. The modeling showed that there are a number of feasible combinations of wind and solar power generation coupled with green hydrogen production to achieve 100% decarbonization of the Russian economy

Exploring national decarbonization pathways and global energy trade flows: a multi-scale analysis

<p>The role of fossils fuels in national economies will change radically over the next 40 years under a strong climate regime. However, capturing this changing role through national-based analyses is challenging due to the global nature of fossil fuel demand and resulting trade patterns. This article sets out the limitations of existing national-scale decarbonization analyses in adequately capturing global conditions and explores how the introduction of a global modelling framework could provide vital insights, particularly for those countries that are dependent on fossil fuel exports or imports.</p> <p>The article shows that fossil fuel use will significantly decline by 2050, although gas will have an important transition role. This leaves large fossil fuel exporters exposed, the extent of which is determined by mitigation action in different regions and especially by the pathways adopted by the larger Asian economies. We find that global-scale models provide critical insights that complement the more detailed national analyses and should play a stronger role in informing deep decarbonization pathways (DDPs). They also provide an important basis for exploring key uncertainties around technology uptake, mitigation rates and how this plays out in the demand for fossil fuels. However, use of global models also calls for improved representation of country specifics in global models, which can oversimplify national economic and political realities. Using both model scales provides important insights that are complementary but that can challenge the other’s orthodoxy. However, neither can replace the other’s strengths.</p> <p><b>Policy relevance</b>:</p> <p>In recent years, how global fossil fuel markets will evolve under different climate regimes has been subject to much debate and analysis. This debate includes whether investments in fossil fuel production still make sense or will be exposed in the future to liabilities associated with high carbon prices. This is important for governments who need to develop coherent policy in relation to fossil fuel sectors and their role as drivers of economic growth and in providing for domestic energy needs. This article argues that national analyses need to be fully cognizant of the global-scale transition, which can be informed by using a multi-scale modelling approach.</p