Critical infrastructure in the system of production and financial and economic relations "input-output"

The production process in the system of economic cycle of the national economy, as well as the relevant tools for the study of production processes, is considered. The production infrastructure of the national economy is studied as an important part of critical infrastructure, reproduction processes. Production is considered in the context of the formation of added value by large groups of types of economic activity, at the institutional and regional levels, in the context of making management decisions at the macro- and microeconomic levels. The inter-industry balance sheet tools are defined as the basic one for assessing production processes and their criticality. To this end, aggregated three-sector input-output tables in producer’s prices for a number of countries have been prepared. Within their framework, the sectoral structure of the economy is considered, direct costs and full costs (Leontief matrix) are determined, indicators of criticality of the production infrastructure are determined. It is proposed to assess the criticality of production infrastructure using such parameters as the share of gross added value in output, the cost of sectors and the economy as a whole, the coefficients of impact and recall, the classification of industries into key, with direct and inverse impact, etc. Comparative studies of criticality indicators for a number of countries were carried out, in particular, direct costs and derivative indicators of criticality of the production system (Frobenius number, Frobenius vector, etc.) were determined. The proposed approaches to improving the parameters of criticality of the production infrastructure of the Ukrainian economy were worked out.

The carbon footprint of Brazilian households through the Consumer Expenditure Survey (POF)

The main objective of this article is to assess the carbon footprint of Brazilian families based on the Household Expenditure Survey (POF), 2008-2009, from the Brazilian Institute of Geography and Statistics (IBGE). The methodology used to quantify the Brazilian households’ carbon footprint has comprised three data sources: i) Household expenditure Survey, 2008-2009; ii) Leontief matrix of Brazilian accounts, year 2010; and iii) 2009 World Input-Output Database (WIOD) environmental inventory. Initially, an input-output (IO) model was created. Next, the results from the IO model were combined with the information of the POF. The poorest families, with a monthly income of up to $ 415.00, emit less than 1.5 metric tons per year, in contrast, the wealthiest families, with a monthly income above $ 5,187.50, release around 18.5 tons, almost 12 times more. It was found that an increase in income, results in an increase in CO2eq emissions. Thus, while the poorest families, which represent more than 20% of the total Brazilian families, cause 7% of the total emissions, the wealthiest families cause 16% of the total emissions although they represent only 4% of the total families. It was observed that the food category loses importance as income increases, while the opposite occurs in the services category. For the poorest families, there is great importance in the housing category, mostly caused by the consumption of electricity and LPG. Therefore, important measures for economic growth supported by actions become particularly relevant.

Antalya İli Tarıma Dayalı Sanayinin Yapısal Analizi

In the province of Antalya, agriculture and tourism sectors with their added value come into the prominence in this economic integration. Agro-industries accelerated from agriculture sector have taken their places in this integration. For this purpose, a survey was carried out with agricultural-based industrial enterprises operating in Antalya province. With the help of the data obtained from the questionnaires, an industrial transaction table was created. In this table, input-output coefficients matrix was reached and production multipliers on the sectors were calculated through the operations of the leontief matrix. In this study, agriculture-based industrial sectors were grouped and reduced to 9 sectors. According to the results obtained, the sector that provides the highest contribution in terms of production multipliers is vegetable production. This means that the total of the Leontif reverse matrix column of this sector is 3.26904, which means that the increase in production in the vegetable production sector will be 3.26904 times higher than the increase in production in the other nine sectors in response to a one-unit increase in production in the vegetable production sector. This sector is followed by the fruit and vegetable processing industry with 3.07305 coefficient vegetable oils and 2.736016. Sugary products industry 1.76583 coefficient was calculated. With these coefficients, it can be said that these sub-sectors have made significant contributions. In all of the studied sectors, if production is increased by one unit, there is another reproduction which gives rise to the production of the sector in the related line. Accordingly, the coefficient of bakery products sector is 2.660875 and is the highest. According to this figure, if the production of the other eight sectors examined increase one unit, it can be said that bakery products will be required to increase the input level of the industry to eight sectors by 2.66 times.

An Analysis of Carbon Footprint of Beijing Based on Input-Output Model

Climate change caused by increasing carbon emission is harmful to global environment and human society. Developing low-carbon economy through reasonable industries planning and effective utilization of resources is a significant path to achieve the aim of energy saving and carbon emission reduction. The word carbon footprint means carbon emission caused by a certain industry, activity, product or individual, and the issue of carbon emission should be linked with economic activity to analyze, while input-output model is a reliable method to contact two factors. Based on input-output model, this paper calculated direct or indirect carbon emission which is demanded for the products of final consumption in Beijing, and analyzed carbon footprint of each industrial sector in 2005, 2007 and 2010 by operating Leontief matrix. The result demonstrates annual carbon emission of Beijing increased from 10482.68×104ton to 17407.28×104ton during 2000-2011. Manufacturing industry, excavating industry, transportation and postal industry exert supreme impact on carbon emission in Beijing. Carbon footprint of transportation and postal industry and other tertiary industries such as information, business, service, education, science researching industries in 2010 had an obvious rise compare with the data of 2005 and 2007.