An Italian input–output model for the assessment of energy and environmental benefits arising from retrofit actions of buildings

This paper is concerned with the application of operations research in defining the optimal lockdown of economic activities to contain epidemic. The problem of optimal lockdown consists in deciding as best economic sectors can be lockdown with respect to fundamental sectors (essential goods and services) while disruptive impacts are minimized on the economy as a whole. Many countries around the world are currently implementing the lockdown of most economic activities to contain the spread of the Covid-19 pandemic. The lockdown brings health benefits for the society as it contains the spread of the virus, reducing the number of infections and allowing the health system to treat those infected better. This paper describes a Boolean linear programming model to deal with the problem of selecting several economic sectors to be shutdown. The objective function is linear and the constraints are linear inequalities related to the Leontief’s input-output table. The model permits to analyze the feasibility of national economic system in which some elements of the input-output table are set equal to zero. The mathematical approach to the shutdown problem permits to identify the greatest number of economic sectors that can be closed without destroying the fundamental sectors. Since solution of the shutdown problem is the greatest number of lockdown economic sectors, author believes the model allows to oppose effectively the spread of virus. Once spread of the virus decreases, another feature of the model is to support decision makers in assigning priorities to the economic sectors to be gradually unlocked. Standard input-output models are able to reveal how different sectors of an economy are interconnected and how changes in one sector affect all other sectors. Besides the use of the input-output data for descriptive analyses of economic interrelations, this table also provides the empirical fundament for a wide scale of impact analysis. input- output models greatly differ in size and possible applications. Simple static input-output models are used for comparative-static impact (scenario) analysis. With the help of input-output quantity models, statements can be made about direct and indirect effects based on exogenous changes in demand. More complex dynamic input-output models largely resolve the limitations and inherent assumptions of static input-output models. Time is considered explicitly; quantity and price reactions are modelled endogenously in a holistic approach and feedback effects are captured. Last models are not only suitable for scenario analysis and for forecasting as well. In “A Linear Programming Solution to Dynamic Leontief type Models” Harvey M. Wagner presents a general dynamic model of an economy and investigates a number of questions related to the feasibility of certain time profiles of demand and the rate of substitution between economic activities. The Inoperability Input-Output Model [1-4]. was developed to understand better the infrastructure interdependencies. Based on the Leontief Input- Output Model, the model is demand-driven, wherein perturbations to the final demand levels are considered the initiating event, and the impact to sectors’ production outputs are the direct and indirect effects resulting from sector interdependencies. Case study shows that the different perturbation classes (demand or supply, quantity or price) yield different rank orders of sectors impacted by the initiating event, thus providing various perspectives of impacts. The present study differs from most previous studies in two aspects. First, linear programming techniques have been widely used in Input-Output model: from the primary and dual formulation of the problem to the analysis of Lagrange multipliers in order to assess the mutual relative importance of sectors. Second, the impact of lockdown is measured in terms of annihilated interdependencies rather than in terms of canceled economic values such as revenues and monetary exchanges. For this reason, the proposed model takes into account the adjacency matrix got from input-output Table rather than the Table itself. This analysis is static in character (it is a static I-O model) because it does not take care explicitly of time, quantity and price reactions, consumption and production lags, growth of final demand (these ones are known as dynamic I-O models). The study simply considers the interruption of several activity as a consequence of a government measure adopted to reduce the risk of contagion. The proposed model answers to the basic question if the annihilation of N sectors is feasible, secondary the model identifies in the Lagrange multipliers the means to sequencing the sectors to be reopened.

Embedded energy and total greenhouse gas emissions in final consumptions within Thailand

The transport sector is the largest energy-consuming sector in Thailand, and the primary energy supply in this sector is heavily depended on imported oil. Since 2005 world crude oil price has been rising and had reached a record of a triple-digit price per barrel at 147 $/barrel. Since 2005, the Ministry of Energy has set a target to promote the use biofuel in the transportation sector to reduce the oil consumption and increase energy independency. Therefore the policy on promotion of biofuel was initiated in 2005; however, the economywide impacts on CO2 emission factors have been rarely assessed. In order to measure the total Greenhouse Gas (GHG) emission factors from different economic sectors, the contribution of emission has to be considered. In this paper, the focus is placed on CO2 emissions. To calculate the amount of CO2 emitted, the emission factors of various final consumptions in the economy evaluated by the Input-Output Analysis (IOA) must be applied. The direct CO2 emissions in final energy consumptions in Thailand are evaluated by using conversion factors from Guidelines to Defra's GHG conversion factors, Annexes updated June 2007. This paper is aimed to measure the CO2 emission factors in various economic sectors and to compare its factors in 2015 when the policy of promotion of biofuel utilization is fully implemented.

Comprehensive economic loss assessment of disaster based on CGE model and IO model—A case study on Beijing “7.21 Rainstorm”

: The preparation of input output tables aims to present a picture of the interrelationships and interconnections among sector units in the economy as a whole, so that the input output model is a complete and comprehensive analysis tool. For the purposes of planning and evaluating the overall development outcomes of both national and smaller scale (district / city level), the regional development planning approach model can use an input-output analysis model. Through the analysis of the location quention can be seen the diversity of the economic base of a region's society. Against the areas analyzed can be classified sectors that are able to compete in their own territory or that can compete with other regions. Based on the analysis of 40 economic sectors in Bandung, as many as 16 sectors able to compete with other regions, and the rest can only compete in the area of Bandung . Keywords : O utput I nput M odel, Location Quotient, I ndustry C lassification, O utput, L abor

Objective. Closed-loop electrical brain stimulation systems may enable a precisely-tailored treatment for neurological and neuropsychiatric disorders by controlling the stimulation based on neural activity feedback in real time. Developing model-based closed-loop systems requires a principled system identification framework to quantify the effect of input stimulation on output neural activity by learning an input-output (IO) dynamic model from data. Further, developing these systems needs a realistic clinical simulation testbed to design and validate the closed-loop controllers derived from the IO models before testing in human patients. Approach. First, we design a control-theoretic system identification framework to build dynamic IO models for neural activity that are amenable to closed-loop control design. To enable tractable model-based control, we use a data-driven linear state-space IO model that characterizes the effect of input on neural activity in terms of a low-dimensional hidden neural state. To learn the model parameters, we design a novel input waveform—a pulse train modulated by stochastic binary noise (BN) parameters—that we show is optimal for collecting informative IO datasets in system identification and conforms to clinical safety requirements. Second, we further extend this waveform to a generalized BN (GBN)-modulated waveform to reduce the required system identification time. Third, to enable extensive testing of system identification and closed-loop control, we develop a real-time closed-loop clinical hardware-in-the-loop (HIL) simulation testbed using the microelectrode recording and stimulation device, which incorporates stochastic noises, unknown disturbances and stimulation artifacts. Using this testbed, we implement both the system identification and the closed-loop controller by taking control of mood in depression as an example. Results. Testbed simulation results show that the closed-loop controller designed from IO models identified with the BN-modulated waveform achieves tight control, and performs similar to a controller that knows the true IO model of neural activity. When system identification time is limited, performance is further improved using the GBN-modulated waveform. Significance. The system identification framework with the new BN-modulated waveform and the clinical HIL simulation testbed can help develop future model-based closed-loop electrical brain stimulation systems for treatment of neurological and neuropsychiatric disorders.

Using composition of land multiplier to estimate ecological footprints associated with production activity

Using input–output table of China in 2007, this paper built an input–output model and tested the effect of variation of water-use efficiency on the structure of virtual water trade. As the final water consumption coefficient increases (i.e., water-use efficiency decreases) in some department, the proportion of this department’s virtual water outflow/inflow increases while those of all the other departments decrease. As the direct water consumption coefficient increases (i.e., water-use efficiency decreases), the proportion of this department's virtual water outflow/inflow increases while the sum but not each of the other departments’ proportions decreases. The final water consumption coefficient of any department is bigger than the direct one, implying indirect water consumption exists and final water consumption coefficient is a better indicator for water-use efficiency. The paper shows that China is a net exporter of virtual water, but the departments of agriculture, mining, petrochemical are the net importers of virtual water. According to the results, China should use the strategy of virtual water trade to alleviate water shortage. Also, China should adopt water-saving technology in production, and improve the level of self-sufficiency of products in order to reduce reliance on foreign trade correspondingly.

Economists are increasingly interested in forecasting future costs and benefits of policies for dealing with materials/energy fluxes, polluting emissions and environmental impacts on various scales, from sectoral to global. Computable general equilibrium (CGE) models are currently popular because they project demand and industrial structure into the future, along an equilibrium path. But they are applicable only to the extent that structural changes occur in or near equilibrium, independent of radical technological (or social) change. The alternative tool for analyzing economic implications of scenario assumptions is to use Leontief-type Input-Output (I-O) models. I-O models are unable to endogenize structural shifts (changing I-O coefficients). However, this can be a virtue when considering radical rather than incremental shifts. Postulated I-O tables can be used independently to check the internal consistency of scenarios. Or I-O models can be used to generate scenarios by linking them to econometric ‘macro-drivers’ (which can, in principle, be CGE models). Explicit process analysis can be integrated, in principle, with I-O models. This hybrid scheme provides a natural means of satisfying physical constraints, especially the first and second laws of thermodynamics. This is important, to avoid constructing scenarios based on physically impossible processes. Process analysis is really the only available tool for constructing physically plausible alternative future I-O tables, and generating materials/energy and waste emissions coefficients. Explicit process analysis also helps avoid several problems characteristic of ‘pure’ CGE or I-O models, viz. (1) aggregation errors (2) inability to handle arbitrary combinations of co-product and co-input relationships and (3) inability to reflect certain non-linearities such as internal feedback loops.

This study is an empirical study that compares the use of two types of inverse matrices in the input output model. The Input Output (IO) model is based on a system of mathematical equations that applies general equilibrium phenomena. The matrix operating system in the equation derived from the IO model allows the Output value (X) to be calculated as an effect of the final demand induction (F) with the formulation X=(I-A)-1F where A is the technical coefficient matrix. This equation model uses the Leontief Inverse Matrix to calculate the impact of output with final demand (F) as a stimulant. Calculation of the impact of the stimulus from the supply side such as added value and the value of intermediate inputs originating from imports (V) uses the Ghosian Inverse Matrix in the equation X=(I-AT)-1V where AT is the usage coefficient matrix. The data used in this study comes from the Bengkulu Province Input Output Tables in 2000 and 2016, each of which has been collected in a common set to see comparability between years of observation. Forecasting results with both types of approaches produce different levels of accuracy for each observation period.

This study aims to look at the estimated structural changes in the input output table for DKI Jakarta Province in 2024. Estimates for changes in the structure are obtained by making projections of the input-output table for DKI Jakarta Province in 2024 using the Dynamic Input Output (DIO) model, namely by embedding the econometric model into the input output model, using the 2016 based year, 52 economic sectors, and 22 data series from 2000 to 2021.DIO is a hybrid model that places more emphasis on non-survey aspects that combines macro-econometric equations with identity equations in input-output analysis. This model has many dynamic equations consisting of 425 equation model. The parameters value of the equation are estimated using a combination of three estimation methods: (1) Ordinary Least Square, (2) First Order of Autoregressive, and (3) Second Order of Autoregressive. The general balance value in the DIO model is formulated using the Gauss-Seidel iteration "RAS" method.The results of the study show that in 2024 the corporate services sector, the chemical, pharmaceutical and traditional medicine industry sector, and the transportation equipment industry sector are the dominant sectors in shaping changes in the structure of intermediate demand. The Construction Sector, the Corporate Services sector, and the transportation equipment industry sector are the dominant sectors shaping changes in the structure of intermediate input. The construction sector, the real estate sector, and the wholesale and retail trade sector, not cars and motorcycles, are the dominant sectors in shaping changes in the structure of final demand. For the gross value added component, the Wholesale and Retail Trade sector, Not Cars and Motorcycles, the Private Information and Communication sector, and the Financial Intermediary Services sector other than the Central Bank are the largest sectors in the formation of structural changes.

Comparing the contribution of commercial and recreational marine fishing to regional economies in Europe. An Input-Output approach applied to Asturias (Northwest Spain)

Although a clear definition of energy poverty has not been reported in the scientific literature or in general energy directives, this condition affects about 10% of European people. During the last three years, the COVID-19 pandemic combined with the increase in energy bill costs due to energy conflicts has determined the increment of energy poverty. The Renewable Energy Directive, that defines a new legal entity named Renewable Energy Community as a new end-users’ organization, recognizes the chance for low-income households to benefit from being able to access affordable energy tariffs and energy efficiency measures thanks to these new entities. Thus, this paper analyses the energy, economic, and environmental performances of a renewable energy community composed of three residential users distributed in two buildings located in the south of Italy, and one of these buildings is equipped by a rooftop photovoltaic plant. The plants were modelled and simulated through HOMERPRO simulation software while the building energy loads are real and were imported from an energy distributor dataset and were processed in the MATLAB simulation interface. The analysis concerned the comparison of the energy performance achieved by one case in which no renewable plants were installed, and by another case in which the end-users took part in the renewable energy community by sharing the photovoltaic “produced” electricity. The investigation was conducted in terms of the quantity of electricity imported from the power grid and consumed on-site, the avoided emissions, and the operating costs. The business plan has been devoted to defining the advantages of the energy community for vulnerable end-users in a popular neighborhood council estate by evaluating the social energy poverty indexes. The results showed that through the renewable energy community, a mitigation of energy poverty is obtained within a range of 12–16%.

Environmentally Extended Input–Output Databases (EEIOs) provide an effective tool for assessing environmental impacts around the world. These databases have yielded many scientific and policy relevant insights, especially through the national accounting of impacts embodied in trade. However, most approaches average out the spatial variation in different factors, usually at the level of the nation, but sometimes at the subnational level. It is a natural next step to connect trade with local environmental impacts and local consumption. Due to investments in earth observation many new data sets are now available, offering a huge potential for coupling environmental data sets with economic models such as Multi-Region Input–Output (MRIO) models. A key tool for linking these scales are Spatially Explicit Input–Output (SIO) models, which provide both demand and supply perspectives by linking producers and consumers. Here we define an SIO model as a model having a resolution greater than the underlying input–output transaction matrix. Given the increasing interest in this approach, we present a timely review of the methods used, insights gained, and limitations of various approaches for integrating spatial data in input–output modeling. We highlight the evolution of these approaches, and review the methodological approaches used in SIO models so far. We investigate the temporal and spatial resolution of such approaches and analyze the general advantages and limitations of the modeling framework. Finally, we make suggestions for the future development of SIO models.

Input–output analysis involves all aspects of the national accounts related to goods and services, including expenditure aggregates. Input–output analysis provides the opportunity to reconcile supply and use of goods and services, as well as reconcile GDP and expenditure on GDP. One of the goals of this analysis is to eliminate the statistical discrepancy. This is also a requirement for deriving downstream input–output tables. Compiling regional input–output table, not only identify the quantity of products in inter-regional trade, but also determine the trade flows among departments. Moreover, in the inter-regional trade, it is also necessary to distinguish how much of intermediate inputs used in the production sector and how much used in final consumption. Therefore, compiling inter-regional input–output tables require high quality data, but so far, apart from a small part of developed countries, the vast majority of countries cannot meet the need of basic data requirements compiling inter-regional input–output tables in the existing statistical system, because of a lot of manpower and material resources to carry out surveys and collect data, which makes considerable difficult to compile inter-regional input–output table at present. It requires compiling inter-regional input–output tables when the data resources are relatively low.