Dynamic Input-output Model Research Articles

Optimal Dynamic Production Planning for Supply Network with Random External and Internal Demands

This paper focuses on joint production/inventory optimization in single and multiple horizons, respectively, within a complicated supply network (CSN) consisting of firm nodes with coupled demands and firm nodes with coupled demands. We first formulate the single-epoch joint optimal output model by allowing the production of extra quantity for stock underage, considering the fixed costs incurred by having inventory over demand and shortfalls. Then, the multi-temporal dynamic joint production model is further investigated to deal with stochastic demand fluctuations among CSN nodes by constructing a dynamic input–output model. The K-convexity defined in Rn space is proved to obtain the optimal control strategy. According to physical flow links, all demands associated to the nodes of CSN are categorized into the inter-node demand inside CSN (intermediate demand) and external demand outside CSN (final demand). We exploit the meliorated input–output matrix to describe demand relations, building dynamic input–output models where demand fluctuates randomly in single-cycle CSN and finite multi-cycle CSN. The novel monocyclic and multicyclic dynamic models have been developed to minimize system-wide operational costs. Unlike existent literature, we consider fixed costs incurred by overdemand and underdemand inventory into system operational cost functions and then demonstrate the convexity of objective functions. The cost function with two fixed penalty costs due to excess and shortage of inventory is developed in a multicycle model, and the K-convexity defined in Rn is proved to find out the optimal strategy for joint dynamic production of CSNs in the case of multi-products and multicycles.

Understanding the role of energy sector in the outbreak of epidemic in China: An analysis based on SEIDR epidemic model, dynamic input-output model and resource allocation DEA model

The global epidemic has triggered severe health crises and economic downturns, while energy sector plays an important role in the complicated sector interdependence. In such context, to gain a deeper understanding of the energy sector's role during epidemic outbreaks, this study develops a new methodological framework. The framework first combines SEIDR epidemic model with dynamic input-output model and resource allocation DEA model, linking epidemiological and socioeconomic aspects. Empirically, this study focuses on China's economy by constructing an energy-manufacturing-service system. Our main conclusions are summarized as follows. First, the epidemic generates substantial effects on energy sector, while the size of effects is sensitive to the settings of some key parameters. Second, the energy sector suffers the most significant decline in total output and capital due to the epidemic, particularly in Shandong and Gansu. Conversely, Beijing and Chongqing have shown the least changes, indicating a more resilient performance in comparison to other provinces or province-equivalents. Finally, a well-designed resource allocation strategy can reduce inefficiency, and then reduce the economic losses possibly caused by the epidemic. The output of energy sector has reduced by 29.5%, indicating a need for targeted interventions and strategies to boost its performance.

Stability of price and quantity to a long-run equilibrium: a dynamic Leontief model with bounded rationality

AbstractThe notorious ‘dual stability’ paradox is stated as follows: in a closed dynamic Leontief model, when the quantity system is relatively stable, its corresponding price system will be unstable, and vice versa. This paradox arises from the neoclassical assumptions of full utilization of capacity and perfect foresight, which have caused serious complications in the dynamic Leontief model. In this study, we aim to construct a dynamic input–output model within an evolutionary framework, departing from neoclassical assumptions. Two new assumptions are introduced: incomplete utilization of capital stocks and bounded rationality in decision-making. Our findings reveal that the ‘dual stability’ paradox of the quantity and price systems can be addressed by including these two assumptions, and some special conditions are proposed for the stability properties in both the systems. Furthermore, we prove that the distance between the time paths and equilibrium position converges to a constant, which is related to the initial position.

Dynamic models for developing reference scenarios of energy system in the low-carbon transition

The proposed study is aimed at the development of dynamic input-output models in relation to energy development systems. The main task is to find optimal scenarios, defined as sequences of optimal balances between production and capital investment. These models are rooted in Leontief's conceptual concept of input-output, which emphasizes the timing of the introduction of new production systems. The article discusses the economic interpretation and mathematical conditions of intersectoral models, and examines their application to problems related to energy. The model uses the concept of balanced equilibrium growth, in which the ratio of consumption to net release is the same for all resources, and the reserve of at least one resource is fully utilized. The growth rate of the system is the most important parameter that determines the trajectory of balanced growth. Solving the system of inequalities yields balanced growth trajectories in which the dominant root and the associated vector of characteristics plays a key role. A balanced equilibrium growth rate has the properties of a minimax with the presence of a saddle point, which indicates its importance in achieving market equilibrium while preventing a reduction in inventories. The practical application of the model is described. The results of calculations of pessimistic, reference and optimistic scenarios of electricity production in the IPS of Ukraine are presented. Official statistical information and economic forecasts are used, and various restrictions are taken into account. The model aims to minimize the total cost of electricity generation while respecting environmental and operational constraints. The proposed model provides a comprehensive basis for understanding the dynamics of resource reserves, consumption and growth of electricity production of the IPS of Ukraine. It offers decision-makers a valuable tool to optimize electricity generation strategies, taking into account a range of scenarios and constraints, for the sustainable and cost-effective operation of the energy system. Keywords: Dynamic input-output models, optimal scenarios, balanced equilibrium growth, growth rate of the system.

Modelling of high performance electrocoalescer based on experimental data

Modern approaches to automatic control of crude oil dewatering and desalting are predominantly model-free, and described model-based systems lack precise mathematical description. Not a single research provides a dynamic input-output model for electric dehydrator, suitable for control system development. As far as electric dehydrator, which is the main plant to control in the process, is a non-linear system with distributed parameters, a need for linearization arises. The aim of present study is to compare different methods for obtaining approximate transfer function of an electric dehydrator from graphical experimental data.

A dynamic input-output model of the Tyumen Oblast south's development based on the von Neumann algorithm

Subject. This article deals with the problem of constructing a real trajectory of the region's development and comparing it with the stated one. Objectives. The article aims to develop an algorithm for calculating and analyzing a dynamic input-output model of the south of the Tyumen Oblast, including social and infrastructural subsystems, and households as an endogenous variable-aggregate. Methods. For the study, we used the methods of construction and analysis of dynamic models by W. Leontief and J. von Neumann. Indicators of balanced growth trajectories were estimated using the Perron–Frobenius theorem. Results. The article proposes an algorithm for the formation of an investment variable. The matrix of direct costs for each year and on average for the periods 2017–2019 and 2017–2020 was identified. Based on the available statistical data, von Neumann highways and model development trajectories were constructed. Conclusions. The shown mechanism for a possible comparison of actual and planned optimal development can help organize digital monitoring of the region's development and analyze deviations from the optimal forecast.

Dynamic Safety Assessment and Enhancement of Port Operational Infrastructure Systems during the COVID-19 Era

Seaports function as lifeline systems in maritime transportation, facilitating critical processes like shipping, distribution, and allied cargo handling. These diverse subsystems constitute the Port Infrastructure System (PIS) and have intricate functional interdependencies. The PIS is vulnerable to several external disruptions, and the impact of COVID-19 is severe and unprecedented in this domain. Therefore, this study proposes a novel general port safety framework to cope with recurring hazards and crisis events like COVID-19 and to augment PIS safety through a multi-state failure system. The PIS is divided into three critical subsystems: shipping, terminal, and distribution infrastructure, thereby capturing its functional interdependency and intricacy. A dynamic input–output model is employed, incorporating the spatial variability and average delay of the disruption, to determine the PIS resilience capacity under the stated disruptions. This study simulates three disruption scenarios and determines the functional failure capacity of the system by generating a functional change curve in Simulink. This study offers viable solutions to port managers, terminal operators, and concerned authorities in the efficient running of intricate interdependent processes and in devising efficient risk control measures to enhance overall PIS resilience and reliability. As part of future studies, given the difficulty in obtaining relevant data and the relatively limited validation of the current model, we aim to improve the accuracy and reliability of our model and enhance its practical applicability to real-world situations with data collected from a real-world case study of a PIS system.

Household Carbon Footprint Characteristics and Driving Factors: A Global Comparison Based on a Dynamic Input–Output Model

Carbon emissions are rapidly increasing with continuing global economic development, necessitating an urgent energy revolution. Often, when calculating carbon footprint, analysts have failed to account for changes in capital stock and the impact of indirect emissions caused by the consumption of imported products. Furthermore, the homogenization of industrial and resident sectors has reduced our understanding of the specific driving forces behind carbon emissions. To avoid such locational and temporal biases, this study employs a dynamic input–output model to re-estimate the carbon footprint of only residents. We deconstruct residential emissions into different consumption categories and conduct a comparative analysis between developed and developing countries from across the world. To this end, data from 44 global economies were obtained from the World Input–Output Database for the period from 2000 to 2014. For developing countries, food consumption had the highest share of embodied carbon emissions, maintaining a share of over 20%, whereas in developed countries, housing consumption had the highest share, remaining at over 30%. In most countries, the consumption level and emission intensity effects were the most important drivers of carbon emission increases and carbon emission decreases, respectively. However, the contributions of the two varied considerably in different countries, with the maximum impact of the emission intensity effect on the carbon footprint of a single category reaching 854.31% in the US and 99.34% in China. These findings will help countries tailor their emission reduction policies to local conditions and emphasize that emission reductions should start by reducing the emission intensity and consumption structure of the corresponding sectors.

Empirical evaluation of the socio-economic impacts of renewable energies in Morocco by 2035: An input-output model

The objective of the study is to identify scenarios relating to solar and wind renewable energy technologies by 2035 in Morocco, and to simulate their socio-economic effects (GDP, Value added by sector and employment). This consists in calculating the effect of these scenarios in comparison with a trend scenario that extends recent developments and takes into account the industrial integration policy already decided on both solar and wind technologies. The methodology applied is based on a dynamic Input-Output (IO) model. Three simulation sce- narios are discussed in this study for the assessment of the socio-economic impacts of con- centrated solar power, photovoltaic and wind energy on the Moroccan economy during the period 2020-2035. Also, a comparative analysis between the scenarios developed and the tar- gets indicated in the national strategies, in terms of economic and job creation indicators, reveals a significant potential in terms of job creation and value added.

ANALYSIS OF CHANGES IN THE INPUT OUTPUT STRUCTURE OF DKI JAKARTA PROVINCE IN 2016 AND 2024: USING THE DYNAMIC INPUT OUTPUT (DIO) MODEL

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.

Prospective environmental impact assessment and simulation applied to an emerging biowaste-based energy technology in Europe

The energy sector of the European Union (EU) is expected to progress fast towards a fully-fledged dependency on renewable sources. In such a goal-driven approach, a prospective assessment framework is designed to simulate the environmental consequences engendered by the gradual penetration of a novel biowaste-based energy technology in the EU energy production market. This is done by building a dynamic input-output model reflecting the implementation of the technology over time, following future energy scenarios and hypothetical targets for the EU (number of plants operating in 2030 and 2050). Total impacts, calculated for global warming, photochemical oxidation, acidification, eutrophication and human toxicity are calculated for these scenarios, and for a counterfactual scenario, represented by the same economic system operating without opting for such novel technology. The output of the simulation shows that the technology could bring carbon savings of 220–250 Mt CO2eq up to 2050 and significant changes in the economy structure such as a reduction of fossil phosphorus production and corresponding generation of revenues from phosphorus recovery in the order of 100–150 billion €. In a more general fashion and beside the case study, the factors affecting the model output, sources of uncertainty and assumptions are presented in order to appraise scope, applicability and limitations of the proposed assessment framework and to prepare its use in decision making.

A Multi Objective and Dynamic Input Output Optimization Model and Algorithm

This article analyzes the advantages of the multi-objective dynamic input-output optimization model compared to the single objective dynamic input-output optimization model, and demonstrates the reason why the multi-objective model improves the stability of the solution of the dynamic input-output equation system by constructing a goal programming model. Based on the 2021 and 200 input-output table, a multi-objective dynamic input-output optimization model was established for the actual economic and social development of City A. The model includes five objective functions, including economic growth and comprehensive economic balance, and is solved using the goal programming method. The prediction results of the city's main economic indicators, such as GDP, tertiary output added value, and so on from 2021 to 2025, are presented. This result is basically the same as the results obtained using artificial neural network methods, and is more in line with the actual situation of the city.

The Impact of Gastronomic Tourism on Thailand Economy: Under the Situation of COVID-19 Pandemic.

With the COVID-19 pandemic's complexity and inexorable devastation, this research article attempts to forecast Thailand's economic move forward through gastronomic tourism promotion. The dynamic input-output (I-O) model was the primary method for classifying gastronomic activities in tourism I-O data, which was investigated sector by sector. The Ministry of Tourism and Sports in Bangkok, Thailand, officially gathered the 2017 I-O table. To briefly explain the empirical results, it found that the main sectors of gastronomic tourism that highly impact Thailand's economy are the processing and preserving of foods, other foods, food and beverage serving activities, and other food services. In terms of forecasting during the period of the COVID-19 pandemic, the Bayesian Structural Time Series (BSTS) based on the dynamic input-output (I-O) model suggests that approximately 1% to 2% of Thailand's gastronomic tourism will be able to contribute to the GDP of this country substantially. By the way, if this research result is significant, then both the private sector and the government sector need to be concerned and promote those sectors as much as they can.

Analysis of the Multiplier Effects Produced by Investment in a Dynamic Input–Output Model

Analysis of the Multiplier Effects Produced by Investment in a Dynamic Input–Output Model

Multi-objective energy planning for China's dual carbon goals

Long-term dependence on fossil fuels for economic growth is a primary driver of carbon emissions in emerging economies such as China. To achieve China's dual carbon goals (DCGs) of carbon peaking and carbon neutrality, we developed a dynamic input-output multi-objective optimisation model, combined with scenario setting, to explore the optimization pathways of carbon emissions, economic growth, and energy consumption. With reference to policy reports, we set different economic growth scenarios, and consider ambitious, moderate, and conservative carbon emission scenarios. The results showed that large-scale expansion of the tertiary industry is essential to realize the DCGs. The cumulative carbon emissions for the ambitious scenario were approximately 1.8 times higher than those for the conservative scenario. Further, the carbon intensity in 2030 would decrease by >69.2 % compared to the 2005 level. It means that China can achieve carbon reduction targets for 2030. We projected that future Chinese energy consumption will peak in 2035, reaching 180.64 EJ. We also optimized the installed electricity generation mix to be consistent with future development scenarios for China. This study provides valuable insights into the strategies and policy requirements to meet China's newly announced DCGs to assist in mitigating global climate change.

Dynamic spillover capacity of R&D and digital investments in China's manufacturing industry under long-term technological progress based on the industry chain perspective

During the new era of digital-technology economics, R&D and digital investments have emerged as critical factors influencing technological change in the long term. At the same time, technological progress in industries is the fundamental cause of the R&D and digital investments spillover between industries. In order to study the dynamic spillover capacity evolutionary process generated by R&D and digital investments caused by the long-term technological change in China's manufacturing industry, this paper firstly integrates R&D and digital investments as the critical influencing factors affecting technological progress. It establishes a dynamic input-output model including endogenous technological progress, which is simulated by historical data from 2000 to 2014 to obtain the input-output coefficient matrix from 2015 to 2050. On this basis, this paper makes a rare attempt to focus the inter-industry spillover weights on the industry chain perspective to revisit and simulate the dynamic changes of inter-industry R&D and digital investments spillover capacity under the influence of long-term technological progress. As shown in the simulation results, there are significant differences in inter-industry R&D and digital investments spillover capacities among 18 manufacturing industries in China in both industry and time dimensions. The results of this study are presented in the following sections: (1) A horizontal comparison of the spillover capacity of R&D and digital investments in manufacturing industries in the same period from the industry dimension shows that the spillover capacity of R&D and digital investments between industries is dynamic because the development strategies and technological structures of industries may change over time. Hence, the position of each industry in the industry chain is not constant, and the R&D and digital investments spillover capacity between industries are also in a dynamic process. The manner and ability of an industry to generate R&D and digital investments spillover to its upstream and downstream industries may differ depending on its production location in the chain. Generally, for most upstream and near-upstream industries in China's manufacturing industry chain, the spillover capacity of R&D and digital investments of an industry to its downstream industries are greater than that to its upstream industries, and vice versa. Therefore, in order to maximize the technology spillover capacity, the corresponding technology guidance policy should be adjusted by the industry's technological progress characteristics and changes in the production location of the industry chain. (2) When comparing the spillover capacity of R&D and digital investments of each manufacturing industry longitudinally from the time dimension, the demand-pull effect of technological progress caused by R&D and digital investments of industries for their downstream industries is not as profound as the technology-push effect for their upstream industries. Hence, the fluctuations of R&D and digital investments spillover capacity generated by industries located in the upstream, near-upstream of China's manufacturing industry chain are greater than those generated by downstream, near-downstream industries. Therefore, it can be inspected that industries close to the upstream of the industry chain are more efficient in driving economic growth with their inputs in general-purpose technologies.

Forecasting the propagation of pandemic shocks with a dynamic input-output model

We introduce a dynamic disequilibrium input-output model that was used to forecast the economics of the COVID-19 pandemic. This model was designed to understand the upstream and downstream propagation of the industry-specific demand and supply shocks caused by COVID-19, which were exceptional in their severity, suddenness and heterogeneity across industries. The model, which was inspired in part by previous work on the response to natural disasters, includes the introduction of a new functional form for production functions, which allowed us to create bespoke production functions for each industry based on a survey of industry analysts. We also introduced new elements for modeling inventories, consumption and labor. The resulting model made accurate real-time forecasts for the decline of sectoral and aggregate economic activity in the United Kingdom in the second quarter of 2020. We examine some of the theoretical implications of our model and find that the choice of production functions and inventory levels plays a key role in the propagation of pandemic shocks. Our work demonstrates that an out of equilibrium model calibrated against national accounting data can serve as a useful real time policy evaluation and forecasting tool.

Analysis and forecast of the state of the environment of the Republic of Buryatia in the new conditions

The Republic of Buryatia is a region with a contradiction between economic development and environmental protection. Special attention is paid to this problem since industrial processes impact negatively on condition of the Baikal natural territory. The article analyzes the current state of ecological situation in the republic, considers the situation in the sphere of production and consumption waste management, air and surface water pollution. The authors made a forecast of the economic impact on the environment of Buryatia until 2030, which was built with a use of the regional dynamic input-output model with an environmental block. The forecast took into account the consequences of the economic crisis caused by recent geopolitical events. In addition, a number of measures are proposed to ensure the sustainability of “green growth” in the Republic of Buryatia.

A Model for Optimal Economic Lockdown to Contain Epidemic

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.

Industrial water network vulnerability analysis using dynamic inoperability input-output model

Industrial parks provide opportunities for Process Integration among different enterprises. Inter-Plant Water Network Integration is an effective strategy for water conservation. However, increased interplant linkages can make the entire system vulnerable to cascading failures in case of loss of water flow in some plants. The potential indirect impact of water shortages on such integrated systems may not be evident without the use of appropriate models. This work defines inoperability as the fractional loss of water flow relative to normal operations. A comparison between the applicability of demand-driven versus supply-driven Inoperability Input-output Model (IIM) is conducted. Then, a Vulnerability Assessment Framework which integrates vulnerability indicators into the Dynamic Input-Output Model (DIIM) is developed to analyse failure propagation in water networks in an industrial park. The DIIM is then applied to simulate the cascading effects of disturbances. From a time perspective, the vulnerabilities of the industrial parks With Integrated Optimal Water Network (WWN) and Without Integrated Optimal Water Network (WOWN) are assessed considering robustness, adaptability, and recoverability as the indicators. The results indicate that supply-driven IIM is more suitable for cascading failure analysis of water networks. The average inoperability at 16% from supply-driven IIM is higher than that from demand-driven IIM. In the freshwater disturbance scenario, the dependence of the plant on freshwater is proportional to the rate of inoperability change, the time to reach a new equilibrium. In this study, the robustness of WWN is about fivefold that of WOWN, but the recovery rate is only one-sixth of the latter. This work can help identify system vulnerabilities and provide a scientific insight for the development of park-wide water management strategies.