Dynamic Input Output Research Articles

Dynamic input–output balance as a tool for structural policy analysis

This article extends the array of tools available for the analysis and advancement of structural policies using dynamic inter-sectoral balance, expressed in the form of a system of ordinary differential equations. This development became achievable following the publication of the authors’ methods for digitizing and calibrating such a structural model, thereby transforming it from a set of “purely theoretical constructs” into a set of computational and pragmatically valuable models. Formally, the digitization process for the model involves the introduction and computation of the eigen (intra-industry) and mutual (inter-industry) inertia matrices, the elements of which are time-dependent. These matrices substitute the conventional incremental capital intensity matrix within the model. It is worth noting that, unlike other scientific summaries, the matrix of incremental capital intensity is nowhere to be found in global statistics. No statistical institute worldwide develops or publishes it for any economy. The second factor that enhances the practical utility of the model lies in the utilization of the matrix of financial flow coefficients for its calibration. The computations conducted and presented here lead to the conclusion that the model is proficient in providing robust forecasts of gross output in current and comparable prices within the timeframe covered by Rosstat’s basic “input–output” tables. In its current format, the model holds potential as a valuable tool for scrutinizing structural changes within the domestic economy and for comparing Russia’s economic trends with those of other developed and developing nations. Digitizing it for use at the regional level would enable similar analyses on a mesolevel scale.

An Integrated Dynamic and Quality Modeling Framework for Batch Processes

This manuscript considers batch process operations and addresses the challenge of identifying a model that synergistically captures the dynamic input–output behavior of continuously measured variables along with the quality variables measured only at batch termination. To this end, an optimization-based framework is developed to identify one model that captures both the dynamics between the inputs and the continuously measured output variables, measurements of which are available at every time step, and the relation between the dynamic “state” information and the terminal quality measurements. Existing approaches either do not identify the dynamic and the quality model simultaneously, or they simply connect the whole trajectory of the process variables with the qualities and do not address the dynamic relationship between the inputs and the process variables. The improved modelling performance of the model obtained from this approach is demonstrated using data from a Uni-axial Rotational Molding process, and compared with existing modelling approaches.

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.

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.

Systematic hyperparameter selection in Machine Learning-based engine control to minimize calibration effort

For automotive powertrain control systems, the calibration effort is exploding due to growing system complexity and increasingly strict legal requirements for greenhouse gas and real-world pollutant emissions. These powertrain systems are characterized by their highly dynamic operation, so transient performance is key. Currently applied control methods require tuning of an increasing number of look-up tables and of parameters in the applied models. Especially for transient control this state-of-the-art calibration process is unsystematic and requires a large development effort. Also, embedding models in a controller can set challenging requirements to production control hardware. In this work, we assess the potential of Machine Learning to dramatically reduce the calibration effort in transient air path control development. This is not only done for the existing benchmark controller, but also for a new preview controller. In order to efficiently realize preview, a strategy is proposed where the existing reference signal is shifted in time. These reference signals are then modeled as a function of engine torque demand using a Long Short-Term Memory (LSTM) neural network, which can capture the dynamic input–output relationship. A multi-objective optimization problem is defined to systematically select hyperparameters that optimize the trade-off between model accuracy, system performance, calibration effort and computational requirements. This problem is solved using an exhaustive search approach. The control system performance is validated over a transient driving cycle. For the LSTM-based controllers, the proposed calibration approach achieves a significant reduction of 71% in the control calibration effort compared to the benchmark process. The expert effort and turbocharger experiments used in calibrating transient compensation maps in physics-based feedforward controller are replaced by little simulation time and parametrization effort in ML-based controller, which requires significantly less expert effort and system knowledge compared to benchmark process. The best trade-off between multi-objective cost terms is achieved with one layer and 32 cells LSTM neural network for both non-preview and preview control. For non-preview control, a comparable control system performance is achieved with the LSTM-based controller, while 5% reduction in cumulative NOx emissions and similar fuel consumption is achieved with preview controller.

Retracted: Tracking Control of the Dynamic Input-Output Economic System Based on Data Fusion

Retracted: Tracking Control of the Dynamic Input-Output Economic System Based on Data Fusion

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.

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.

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

The optimal industrial carbon tax for China under carbon intensity constraints: a dynamic input-output optimization model.

To reduce carbon emissions, the Chinese government is considering introducing a differentiated industrial carbon tax on enterprises outside the carbon trading market in the future. An efficient carbon tax must consider not only how carbon taxes impact the current economy but also how the size of the tax should be adjusted across time due to external changes. To calculate the optimal industrial carbon tax for China which is subject to certain constraints, this paper investigates the economic and environmental effects of four possible industrial carbon tax rate models under carbon intensity constraints from 2021 to 2030 by a dynamic input–output optimization model. The results show that the dynamic tax rate model leads to larger fluctuations in GDP growth than the other tax models, with a low initial tax rate in the beginning and a high tax rate exceeding ¥180/t in 2030. Second, a large quantity of capital stock is distributed across the energy-intensive industries, which leads the existing capital investment structure to be path-dependent. This offsets the performance of carbon taxes. Third, indirect energy-intensive industries such as construction and transport are insensitive to the industrial carbon tax. Finally, comparing the impacts of the four tax rate models, the optimal industrial carbon tax for China is found to be a fixed differentiated tax rate, in which energy-intensive sectors are taxed ¥75/t and low-carbon sectors are taxed ¥50/t.Supplementary InformationThe online version contains supplementary material available at 10.1007/s11356-022-19162-6.

Ecological macroeconomic assessment of meeting a carbon budget without negative emissions

Non-technical summary This paper expands the range of scenarios usually explored in integrated assessment models by exploring unconventional economic scenarios (steady-state and degrowth) and assuming no use of negative emissions. It is shown, using a mathematical model of climate and economy, that keeping cumulative emissions within the 1.5 degree carbon budget is possible under all growth assumptions, assuming a rapid electrification of end use and an immediate upscaling of renewable energy investments. Under business-as-usual investment assumptions no economic trajectory corresponds with emissions reductions consistent with the 1.5 degree carbon budget. Technical summary This paper presents a stock-flow consistent input–output integrated assessment model designed to explore the dual dynamics of transitioning to renewable energy while electrifying end use subject a carbon budget constraint. Unlike the majority of conventional integrated assessment model analyses, this paper does not assume the deployment of carbon dioxide removal and examines the role that alternative economic pathways (steady-states and degrowth) may play in achieving 1.5°C consistent emissions pathways. The model is internally calibrated based on a life-cycle energy return on investment scheme and the energy transition dynamics are captured via a dynamic input–output formulation. Renewable energy investment as a fraction of gross domestic product for successful emissions pathways reaches 5%. In terms of new capital requirements and investments, degrowth trajectories impose lower transition requirements than steady-state and growth trajectories. Social media summary We explore the role that steady-state and degrowth economic trajectories may play in emissions reductions consistent with a 1.5 degree world..

A Study on Selecting Greenhouse Gas Reduction Options: A Simulation Analysis for Vietnam

When it comes to greenhouse gas (GHG) mitigation, both bottom-up and top-down policies have limitations. Bottom-up policies are region-specific and cannot be applied at the national level. Top-down policies may not balance the considerations of economic growth and the environment. Therefore, a combined approach is necessary. This Vietnamese case study investigates optimal GHG mitigation options for both economic development and emission reduction by simulating four scenarios characterized by the different carbon tax and subsidy rates. Interventions, like replacing old buses with low-carbon buses and conventional electricity generation with solar power, are considered in a dynamic input–output framework. The objective function is Green GDP—industries’ total value added reflecting GHG emissions’ social cost. The simulation model comprises four cases: business as usual, low subsidy rate (up to 10%), medium subsidy rate (up to 20%), and high subsidy rate (up to 30%), which are analyzed on parameters, including economic development, GHG emissions, and development of innovative sectors, like transportation and electricity. In three cases with different subsidy rates, the optimal carbon tax is simulated at the rate of USD 1/tCO2 equivalent, the lowest rate among the world’s current carbon prices. In addition, the medium subsidy (up to 20%) option yields the most competent scheme, with the highest GHG emission reduction and economic development effectiveness.

Analysis of the impact of dynamic economic resilience on post-disaster recovery “secondary shock” and sustainable improvement of system performance

Quantitative measuring disaster resilience especially dynamic economic resilience (DER) belongs to a frontier research in the past decades, which can effectively improve post-disaster economic recovery effects, reducing post-disaster loss. However, DER focuses more on the overall shape changes of recovery path, two features that commonly appear in the dynamic change process of recovery curve are also worth evaluating separately: the “secondary shock” caused by adverse factors during the recovery process and sustainable improvement of economic output at the end of recovery. Therefore, we adopt a dynamic input–output model that integrates capital damage and affected people and apply it to an extreme flood in 2016, Wuhan City, China, building the relationship between 7 recovery actions (rescue funds) and changes of two features, analyzing the impact of resilience on the dynamic recovery path features through 6 characteristic variables constructed in this paper. Results show that (1) economic resilience increases from no to actual action, the dropping rate caused by “secondary shock” decreases 30.4%, and Wuhan’s economic output has achieved an improvement in sustainability of 0.088% compared to the pre-disaster level, which contributes permanently to reducing vulnerability to future disasters. (2) only 21.81% (compared to the actual scenario) of the rescue funds is needed to support 30% reduction action plus 0.02% sustainable improvement. But to support the 50% recovery action, 78.92% of the rescue funds is needed. This paper aims to provide a new perspective for the refined dynamic assessment of resilience and to prove the role of resilience in promoting short-term post-disaster recovery and long-term sustainable development.

Assessing indirect economic losses of landslides along highways

Landslides along highways not only induce direct losses related to cost of replacement, repair or maintenance due to physical damage, but also induce indirect losses due to reduction in production in different sectors in an economic system. In this study, a method is suggested to assess the indirect losses of landslides along highways, where the impact of landslides on the transportation network is evaluated through the gravity model, and the output losses of different sectors are computed based on the dynamic input–output model. The suggested method is used to assess the indirect losses of landslides that occur along highways related to Yingxiu Town, Sichuan Province, China, which is the epicenter of the 2008 great Wenchuan earthquake. It is found that the impact of a landslide hazard on different sectors is not the same depending on their dependence relationship with the transportation sector. The indirect losses of a landslide increase as the repair time increases and decrease as the transportation network becomes more robust. Also, the location of a landslide will affect the caused indirect losses, and the indirect losses will increase as the transportation network is attacked by more landslides. The suggested method provides a useful instrument for estimating the indirect losses of landslides along highways, which were seldom addressed in previous studies.

Prospects for the Economic Development of the Republic of Buryatia

ABSTRACT The article analyzes the development of the economy of the Republic of Buryatia spanning 2007–2017 and provides two forecast analyses of the republic’s economic development until 2021: the baseline forecast and the optimistic forecast. To generate the forecasts the authors used a dynamic input–output model of the Buryat economy that includes fuzzy parameters. It is shown that under favorable macroeconomic conditions and a sharp increase in fixed capital investment, it is possible to significantly accelerate economic growth in Buryatia. Specific projects that will accelerate economic growth in the republic are discussed.

Baumol, Engel, and beyond: accounting for a century of structural transformation in Japan, 1885–1985†

AbstractThis article examines the drivers of the long‐run structural transformation in Japan. It uses a dynamic input–output framework that decomposes the reallocation of the total output across sectors into two components: the demand side and the supply side, or technological change. To perform this task, we employ 13 seven‐sector input–output tables spanning 100 years (1885 to 1985). The results show that the demand‐side factors, as a combination of the Baumol and Engel effects, were the key explanatory factors in more than 60 per cent of the sector‐period cases in the pre‐Second World War period, while the supply‐side effect drove structural transformation in more than 75 per cent of such cases in the post‐Second World War period. Detailed decomposition results suggest that in most of the sectors, changes in private consumption were the dominant force behind the demand‐side explanations. The demand effect was found to be strongest in the commerce and services sector, which contributed to the rapid growth of GDP in Japan throughout the twentieth century.

On the Economic Effects of a Res Local Industry Deployment in Morocco: A Case of Study Defining Scenarios from a Survey to Stakeholders

The aim of this article is to simulate the economic impact on Gross Domestic Product (GDP) and employment of renewable energy sources investment in Morocco over the next 40 years. In this sense, several potential scenarios of energy component evolution have been used based on the results of a specific survey to sector stakeholders. We obtain accurate results, avoiding speculative/theoretical assumptions in terms of scenario design. As usual in the sector, a Dynamic Input–Output Model (DI–O) is used to estimate the direct and indirect effects of such a large investment and, avoiding the criticism of this type of model in the context of long-term simulations, the alternative of de Arce et al. (2012) is used. In this framework, substantial results derive from the three scenarios considered: the increase in Moroccan GDP as a result of this investment could be around 1.2–1.7 points and, on average, 42,000 new jobs could be created.