Байесовский подход к построению таблиц «затраты-выпуск»

Methods of updating, balancing, disaggregation of Input-Output Tables (IOT) are widely used in applied economic and statistical research (for example, for the calibration of computable general equilibrium models), as well as by statistical services for compilation of IOTs. As compared to the well-known popular methods (RAS, cross-entropy minimization, and their analogs), which provide point estimates of unknown tables, the proposed approach targets estimation of joint probability distribution of input-output (IO) coefficients. With this goal we develop a probabilistic model ofjoint distribution of the IO coefficients as a likelihood function of observed information (for example, output, value added, intermediate demand). This information from newly arrived data is being mixed with prior information of IO parameters (for example, known IOTs from former years) by Bayes rule. The resulting posterior joint distribution can be estimated using Markov chain Monte Carlo (MCMC) sampling methods. The sample of IOTs from the targeted distribution is a set of IO matrices consistent with the observed data, constrains, and also near to the prior information. In contrary to the point estimates, the stochastic IOTs naturally incorporate uncertain information of each estimated IO parameter, taking into account all the multivariate correlation between the cells. The proposed methodology can be applied to updating, interpolation, disaggregation, and balancing of IOTs, and more widely - national accounts. We test the methodology with experimental updating of IO table for the Russian economy for 2003 year, based on tables from 1998 to 2002 years. The results suggest adequacy and computational accessibility of the proposed methodology.С задачами обновления, балансировки, дезагрегации таблиц «затраты-выпуск» сталкиваются как исследователи в сфере экономики и статистики (например, для калибровки вычислимых моделей общего равновесия), так и статистические службы (при построении таблиц). В отличие от известных популярных методов (RAS, минимизации перекрестной энтропии и их аналогов) в предлагаемом подходе вместо точечных оценок коэффициентов прямых затрат оценивается их совместное вероятностное распределение. Для этого строится вероятностная модель совместного распределения ячеек, являющаяся функцией правдоподобия новой наблюдаемой информации (например, выпуск, добавленная стоимость, промежуточный спрос), которая с помощью формулы Байеса объединяется с априорной информацией о ячейках (например, известных таблиц предыдущих лет). Получаемая в итоге апостериорная совместная плотность вероятности оценивается методами сэмплирования Монте-Карло по схеме цепи Маркова. Характеристики апостериорного распределения определяются набором (выборкой) искомых таблиц из этого распределения. При этом каждая из полученных таблиц не противоречит имеющимся данным, ограничениям и не слишком далека от априорно заданной таблицы или любой другой информации о ячейках. В отличие от точечных оценок стохастические таблицы напрямую инкорпорируют информацию о неопределенности каждого оцененного коэффициента прямых затрат таблиц «затраты-выпуск», учитывая существующие между ними взаимосвязи. Предлагаемая методика может использоваться для экстраполяции, интерполяции, дезагрегации и балансировки таблиц «затраты-выпуск» и более широко - матриц социальных счетов. С целью апробации метода проводится экспериментальная оценка таблиц «затраты-выпуск» российской экономики за 2003 г. на основе таблиц 1998-2002 гг. Экспериментальное применение метода Байеса на реальных данных продемонстрировало адекватность и вычислительную доступность предлагаемой методики

A decentralized approach to model national and global food and land use systems

The achievement of several sustainable development goals and the Paris Climate Agreement depends on rapid progress towards sustainable food and land systems in all countries. We have built a flexible, collaborative modeling framework to foster the development of national pathways by local research teams and their integration up to global scale. Local researchers independently customize national models to explore mid-century pathways of the food and land use system transformation in collaboration with stakeholders. An online platform connects the national models, iteratively balances global exports and imports, and aggregates results to the global level. Our results show that actions toward greater sustainability in countries could sum up to 1 Mha net forest gain per year, 950 Mha net gain in the land where natural processes predominate, and an increased CO2 sink of 3.7 GtCO2e yr−1 over the period 2020–2050 compared to current trends, while average food consumption per capita remains above the adequate food requirements in all countries. We show examples of how the global linkage impacts national results and how different assumptions in national pathways impact global results. This modeling setup acknowledges the broad heterogeneity of socio-ecological contexts and the fact that people who live in these different contexts should be empowered to design the future they want. But it also demonstrates to local decision-makers the interconnectedness of our food and land use system and the urgent need for more collaboration to converge local and global priorities

How can diverse national food and land-use priorities be reconciled with global sustainability targets? Lessons from the FABLE initiative

There is an urgent need for countries to transition their national food and land-use systems toward food and nutritional security, climate stability, and environmental integrity. How can countries satisfy their demands while jointly delivering the required transformative change to achieve global sustainability targets? Here, we present a collaborative approach developed with the FABLE—Food, Agriculture, Biodiversity, Land, and Energy—Consortium to reconcile both global and national elements for developing national food and land-use system pathways. This approach includes three key features: (1) global targets, (2) country-driven multi-objective pathways, and (3) multiple iterations of pathway refinement informed by both national and international impacts. This approach strengthens policy coherence and highlights where greater national and international ambition is needed to achieve global goals (e.g., the SDGs). We discuss how this could be used to support future climate and biodiversity negotiations and what further developments would be needed

MODELLING SCENARIOS OF SUSTAINABLE DEVELOPMENT OF THE LIVESTOCK SECTOR

The relevance of the study is determined by the need to analyze the consequences of the concentration of livestock farms, not only from an economic point of view, but also from an environmental one. The objective of the research is to develop ways for sustainable development of animal husbandry in Russia, taking into account the possibilities of continuous growth in production and export of meat and milk, balanced by current environmental problems (growth of farm waste) and the possibilities of reducing greenhouse gas (GHG) emissions. The subject of the research is the environmental indicators characterizing the externalities arising from the production of livestock products (meat, milk, eggs). The work uses traditional scientific methods - descriptive, analytical, statistical and methods of economic and mathematical modeling. The sources of information were Russian and foreign scientific publications, official publications of regulatory documents and statistical data of state authorities of Russia, as well as foreign databases on agricultural statistics. The reported results conclude that the concentration of livestock production facilities leads to different environmental consequences. Among the leading regions specializing in livestock products, the highest concentration is in the Central Black Earth Region: the Belgorod and Kursk regions account for almost 30% of all agricultural waste in Russia. The greenhouse gas emissions were estimated using the GLOBIOM partial equilibrium model. The calculation results showed that Belgorod region has one of the lowest carbon footprint indicators in livestock production: 8 tons of СО2 equiv. per one ton of protein, which is associated with the lowcarbon development of poultry farming. In other regions, which specialize in dairy and beef cattle breeding, the carbon footprint is at least twice as high, for example, in the Krasnodarsky krai; however, the indicators of waste output per unit of production, on the contrary, are lower there. Thus, the scientific novelty of the research lies in the development of scientific and analytical tools for the correct identification of local, regional and global environmental risks when assessing the efficiency of meat, milk and eggs production. So, in our study, local risks were assessed through the concentration of manure (nitrogen) elements per unit of agricultural land and cultivated area at the level of municipalities. Regional risks were assessed through the indicator of waste from agricultural products. And global risks were assessed through an indicator of greenhouse gas emissions, which also allowed us to estimate the so-called cumulative carbon footprint of each region per unit of animal protein produced. Based on the results of the study, recommendations were developed to improve statistical reporting on production waste in the agricultural sector; to differentially collect and publish data on various types of feeding of farm animals in different categories of farms, which will subsequently help to better calculate animal diets and their potential waste and greenhouse gas emissions, in order to identify the most "wasteful” and “sustainable” animal husbandry practices

DETECTION SYSTEM OF A NEW-GENERATION BIOANALYTICAL DEVICE

We designed and manufactured a system for detection of antibodies/antigenes in biological fluids. This system records the kinetics of free immunodiffusion of fluorescent nanocomplexes inside the channels of a microfluid module of a new-generation bioanalytical device. The system consists of four parallel excitation channels, a system for fluorescence detection, and an image-processing program. Antibody/antigen concentrations below 0,1 μg/ml can be detected in biological fluids