Dynamic Input Output Research Articles

David Hawkins and the making of the Hawkins-Simon conditions

The Hawkins-Simon conditions, which are necessary and sufficient for the viability of input–output systems, are described in many encyclopedias, textbooks and papers, but always without historical details about the philosopher David Hawkins. The rich literature on the history of input–output economics has neglected Hawkins, probably because he spent only a few years among the economists. My paper fills this gap. By using the relevant archival material on Hawkins, Simon, and Leontief, I correct and expand some scarce remarks on Hawkins by Simon and Samuelson. I discuss Hawkins’s three remarkable contributions to economics. First, Hawkins’s dynamic input–output model in Econometrica in 1948 scooped Leontief. Second, I show how the correspondence between Hawkins and Simon created their famous joint note in Econometrica in 1949. Third, an overlooked chapter in Hawkins’s 1964 book The Language of Nature discussed the commodity values of commodities, generalizing Marx’s labour values and the Technocrats's energy values.

Richard Cantillon’s stabilizing market dynamics

ABSTRACT1 We consider a dynamic input output model that represents important aspects of Richard Cantillon’s discussion of economic structure and market dynamics. Merchants and inventories determine prices that diverge from (longer run) equilibrium. We contrast this stable formulation of price determination with an unstable benchmark specification in which variations in mercantile inventories play no role. Appreciated the stabilising properties of classical dynamics but did not isolate the influence of inventory behaviour. We briefly consider the role of inventories in macroeconomics, and note that balance sheet effects have rendered credit dislocations considerably more persistent than they were in Cantillon’s day.

Dynamic Input–Output Analysis of a Carbon Emission System at the Aggregated and Disaggregated Levels: A Case Study in the Northeast Industrial District

Research on carbon emissions of complex interactive activities in urban agglomerations is one of the hotspots of global climate change research. A comprehensive analysis of the urban agglomeration system’s carbon emissions is essential to reveal strategies for reduction and support sustainable development. The objective of this research is to develop an integrated carbon emission network model to explore the impact of different energy types on the Northeast Industrial District (NID), China. Four representative energy groups are considered. Specifically, at the aggregated sector-level, this research quantified the relative contributions of socioeconomic factors to carbon emission changes using structural decomposition analysis and examined the system efficiency and redundancy through robustness analysis. At the disaggregated level, the research investigated carbon emissions of different sectors from production-based, consumption-based, and income-based viewpoints. Moreover, emissions from specific categories of final demand and primary input were quantified. It was found that the increase of final demand level will proceed to push up the carbon emissions of the NID. Changing the production structure contributes to reducing emissions. The carbon emissions system has a high redundancy and low efficiency, illustrating that there are many emission pathways within the system. In addition, the use of crude oil significantly increases system redundancy and inhibits system efficiency. However, the major limitation of the model is that the long-term changes of the system are not considered. Moreover, considering the actual policies, emission reduction simulations could be added in the future.

Return of the capital coefficients matrix

A core ingredient of post-disaster input–output recovery models is the reconstruction of lost production capacity. Therefore, one would expect a set of models endowed with capital coefficients matrices to be available for analysis. However, this is not the case, possibly due to earlier negative experiences with such models. Nevertheless, in this paper, we aim to show that there is a class of problems that can be addressed successfully with a dynamic input–output model with a fully functioning capital coefficients matrix. We put forward that if reconstruction is tightly planned, investment and therewith gross output essentially become pre-determined. This also means that traditional final demand becomes an endogenous residual, with the model being transformed into a distribution and allocation model. We begin with a reordering of variables and equations as proposed in Leontief’s dynamic inverse, and then move on directly to the newly proposed model. Suggestions for further work are given.

Economic Model-Based Controller Design Framework for Hydraulic Fracturing To Optimize Shale Gas Production and Water Usage

As water issues associated with hydraulic fracturing have received much attention, several optimization approaches have been developed for effective water management. However, most of them have not considered pumping schedules for hydraulic fracturing, which determine the productivity of a shale well as well as the total amount of freshwater required. Because of this, a novel model-based control framework is proposed for hydraulic fracturing to maximize the net profit from shale gas development which simultaneously minimizes the total cost associated with water management. The framework is as follows: initially a reduced-order model and a Kalman filter are developed based on the simulation data generated from a high-fidelity hydraulic fracturing model to correlate the pumping schedule and the final fracture geometry. Then, a numerical reservoir simulator and mixed-integer nonlinear programming model are used to generate two maps describing the revenue from selling shale gas produced and cost from managing wastewater recovered, respectively. Finally, by applying a data-based dynamic input–output model to connect the two maps, an economic model predictive control system is formulated. The proposed control framework enables 62% of the generated wastewater to be reused through the application of thermal membrane distillation technology in the treatment process and results in a 11% reduction in overall freshwater consumption, while maintaining the productivity of a shale well at its theoretical maximum.

THE INPUT-OUTPUT MODEL: ONE OF THE BASIC ELEMENTS OF INFORMATION SUPPORT FOR THE WORK OF REGIONAL SITUATIONAL CENTERS

Goal. Modern situational centers are focused on the preparation of evidence-based management decisions of the executive authorities of the federal and regional levels. Their work is aimed not only at making decisions in emergency situations but also at questions of forecasting, planning and implementing socio-economic trends and development programs. Modification of input-output (IO) models is an actual task for these purposes. Materials and methods. The article proposes a modification of the IO model, focused on the simultaneous modeling of the production process and the dynamics of the consumption sphere, as well as taking into account the influence of exogenous macroeconomic parameters. A classification of models is given; methods for the identification of their parameters on the basis of publicly available global and domestic databases are described. Results. We developed methods for customizing models for solving specific problems. The model varies depending on the consideration of various macroeconomic parameters and the degree of detail of the basic balance ratios. Linear continuous and discrete models allow solving investment management problems. Variation of exogenous parameters leads to non-linear dynamic IO models. Conclusion. It is shown that the proposed approach allows providing information support to public administration associated with the analysis and correction of socio-economic trends, as well as the development of the necessary software. At the same time, its successful implementation is possible only if there are relevant statistical databases on regional economies and information infrastructure.

A control-theoretic system identification framework and a real-time closed-loop clinical simulation testbed for electrical brain stimulation

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.

Dynamic modeling application for simulating optimal policies on water conservation in Zhangjiakou City, China

Zhangjiakou City, one of the organizers of 2022 Olympic and Paralympic Winter Games, is designed as a water conservation area of Beijing because of its unique location, but it's an extremely water-deficient area and has poor economy. The dual constraints of its water resources endowment and external policies force the developers of Zhangjiakou City to confront the dual restriction of the water supply support guarantee and control of water pollutants. The purpose of this paper was to realize water conservation on the premise of sustainable economic development. A linear optimization model, based on a dynamic input–output analysis, was developed to simulate the complex interrelationships between water resources and the social economy, and explore the optimal policies. The simulation results show that from 2013 to 2025, the targets of water conservation, including water retention and water environment control, will be achieved with an annual chemical oxygen demand emissions reduction rate of 12.6% and a constant annual real gross regional product rate of 4.3%. The optimal policies contributes to increasing reclaimed water supply, easing the groundwater exploitation, and optimizing the structure of industries and water demand. The evaluation is suitable for simulating economic development with strict constraints of environmental conservation in other regions with insufficient natural resources.

Optimal control for multi-stage and continuous-time linear singular systems

ABSTRACTIn this paper, optimal control problems for multi-stage and continuous-time linear singular systems are both considered. The singular systems are assumed to be regular and impulse-free. First, a recurrence equation is derived according to Bellman's principle of optimality in dynamic programming. Then, by applying the recurrence equation, bang-bang optimal controls for the control problems with linear objective functions subject to two types of multi-stage singular systems are obtained. Second, employing the principle of optimality, a equation of optimality for settling the optimal control problem subject to a class of continuous-time singular systems is proposed. The optimal control problem may become simpler through solving this equation of optimality. Two numerical examples and a dynamic input–output model are presented to show the effectiveness of the results obtained.

A dynamic input–output method for energy system modeling and analysis

This paper proposes a dynamic input–output model, in order to present the interaction mechanism between energy supply and demand, and provide optimal design for energy supply–consumption system. Combined with mixed integer programming, the model is able to provide optimal technology development route for the energy supply–consumption system. In this model, the generational change and replacement of energy supply facilities are considered. Based on the model, a case study of China is employed. Three scenarios are designed to compare the different outcome of the model. The optimal objectives of Scenario 1 are Pareto Optimality of minimum total energy consumption and minimum total CO2 emission. The optimal objective of Scenario 2 is minimum CO2 emission. The optimal objective of Scenario 3 is minimum energy consumption. Scenario 1 leads to a balancing design of multiple objectives, and the results indicate that energy conservation and CO2 emission reduction targets can be achieved simultaneously. Compared with the optimal design with single objective of Scenarios 2 and 3, the balancing design with multiple objectives performs better in avoiding over-capacity problems during the modeling period. With well-designed development route, energy intensity can be reduced by about 58%–60%, and CO2 emission intensity can be reduced by about 34%–36% from 2012 to 2040. The optimal objectives of the model also influence the development of energy demand structure. The modeling results of transport sector are presented as an example in this paper. To achieve the targets of energy conservation and CO2 emission reduction, both the development routes for energy supply and demand should be well-designed.

On approximating the accelerator part in dynamic input–output models

We release the limitations of previous studies and instead of setting the crucial parameters of the dynamic endogenous input–output model with layers of techniques on an arbitrary basis we propose a new optimization-based approach to approximating of the elements of capital matrices on the basis of recent historical data. Using recent IO data we first formally prove that in comparison to arbitrarily adjusted dynamic IO models the new theoretical approach allows one to obtain a significantly better fit to the historical data in the short-run. This result has also some implications for the long-run analyses as it suggests that using the new approach for typical empirical applications of dynamic IO models with respect to modelling future behavior of economies seems relatively much more reasonable. Having this remark in mind, in the empirical part of the paper we use the new methodological approach in a particular case study. In an illustrative empirical application we try to forecast the possible evolution of sectoral classification in the Polish economy over the next 40 years.

A fully dynamic model of a multi-layer piezoelectric actuator incorporating the power amplifier

The dynamic input–output characteristics of the multi-layer piezoelectric actuator (PA) are intrinsically rate-dependent and hysteresis. Meanwhile, aiming at the strong capacitive impedance of multi-layer PA, the power amplifier of the actuator can greatly affect the dynamic performances of the actuator. In this paper, a novel dynamic model that includes a model of the electric circuit providing voltage to the actuator, an inverse piezoelectric effect model describing the hysteresis and creep behavior of the actuator, and a mechanical model, in which the vibration characteristics of the multi-layer PA is described, is put forward. Validation experimental tests are conducted. Experimental results show that the proposed dynamic model can accurately predict the fully dynamic behavior of the multi-layer PA with different driving power.

Evaluation of Integrated Air Pollution and Climate Change Policies: Case Study in the Thermal Power Sector in Chongqing City, China

The cost of environmental degradation has already had a dramatic impact on the Chinese economy. In order to curb these trends, the government of China has introduced stricter regulations. With this in mind, it is important to quantify the potential co-benefits of introducing air pollution and climate change mitigation policies. This study proposes relevant scenarios ranging from the current trends (baseline) to the introduction of different policies in the thermal power sector, including different carbon tax rates, technology innovation promotion, and technology cost reduction methods. We aim to comparatively evaluate the impact of the proposed policies within the thermal sector and within the entire socio-economic system. To this end, we used a dynamic input–output (I-O) model, into which high-efficiency technologies were incorporated as new thermal power industries in order to estimate policy impact during the time period 2010–2025. The results of this study demonstrated that the introduction of one or more of the following policies: carbon taxes, subsidies, technology innovation, and technology cost reduction, has no notable impact on the environment or the economy without the implementation of environment regulations. In contrast, the strong support of a government subsidy coupled with strict environmental regulations will promote technological innovation, for example through the natural gas combined cycle (NGCC) and the integrated coal gasification combined cycle (IGCC). Our study also showed that the reduction of air pollution and greenhouse gas emissions as well as energy consumption would curb economic development to a certain extent. Taking this into consideration, innovation must also be promoted in other economic sectors. This research provides a strong reference for policy-makers to identify effective polices under different types of environmental regulations.

Modelling the primary drying step for the determination of the optimal dynamic heating pad temperature in a continuous pharmaceutical freeze-drying process for unit doses

In the pharmaceutical industry, traditional freeze-drying of unit doses is a batch-wise process associated with many disadvantages. To overcome these disadvantages and to guarantee a uniform product quality and high process efficiency, a continuous freeze-drying process is developed and evaluated. The main differences between the proposed continuous freeze-drying process and traditional freeze-drying can be found firstly in the freezing step during which the vials are rotated around their longitudinal axis (spin freezing), and secondly in the drying step during which the energy for sublimation and desorption is provided through the vial wall by conduction via an electrical heating pad. To obtain a more efficient drying process, the energy transfer has to be optimised without exceeding the product and process limits (e.g. cake collapse, choked flow). Therefore, a mechanistic model describing primary drying during continuous lyophilisation of unit doses based on conduction via heating pads was developed allowing the prediction of the optimal dynamic power input and temperature output of the electric heating pads. The model was verified by experimentally testing the optimal dynamic primary drying conditions calculated for a model formulation. The primary drying endpoint of the model formulation was determined via in-line NIR spectroscopy. This endpoint was then compared with the predicted model based endpoint. The mean ratio between the experimental and model based predicted drying time for six verification runs was 1.05±0.07, indicating a good accordance between the model and the experimental data.

A value transfer GERT network model for carbon fiber industry chain based on input–output table

This paper builds an input–output table for carbon fiber industry chain in accordance with input–output theory. In order to get a clear picture of the dynamic input–output process between a variety of sectors involved in the carbon fiber industry chain and the increment of value hereinto, a Graphical Evaluation and Review Technique (GERT) network model is constructed on the basis of value flow, and an analytical algorithm for the GERT network model is proposed to get the value transfer probability, the amount of value increment, and the fluctuation variance, from which the equivalence value transfer process and results between sectors are obtained. Finally, in the case study part, this paper finds out the value transfer relationship within the carbon fiber industry chain in Jiangsu Province, China based on the empirical data gotten from field investigations, and then some constructive policy-making and investment suggestions are put forward in view of the results and analyses.

Performance Analysis of Double EWMA Controller Under Dynamic Models With Drift

This paper mainly focuses on the performance of the double exponentially weighted moving average (DEWMA) controller applied to a first-order dynamic input–output model with process drift. Under a general process disturbance, based on Jury test, the necessary and sufficient conditions involving model parameters, the estimated process gain, and weighting factors are established to ensure that the closed-loop system is asymptotically stable. In addition, we explore why we should use the DEWMA controller to adjust the process rather than the single exponentially weighted moving average controller when the process drifts considerably. Later, under the stability condition, the control performance analysis of the closed-loop system is performed in terms of the asymptotical variation and the transient deviation. Then, an optimization model is established to figure out the appropriate weighting factors for reducing the overall variation of the process output during the process of operation. Finally, extensive simulations are carried out to demonstrate the validity of the theoretical analysis in the context of chemical mechanical polishing process.

Technical note: Simultaneous fully dynamic characterization of multiple input–output relationships in climate models

Abstract. We introduce system identification techniques to climate science wherein multiple dynamic input–output relationships can be simultaneously characterized in a single simulation. This method, involving multiple small perturbations (in space and time) of an input field while monitoring output fields to quantify responses, allows for identification of different timescales of climate response to forcing without substantially pushing the climate far away from a steady state. We use this technique to determine the steady-state responses of low cloud fraction and latent heat flux to heating perturbations over 22 regions spanning Earth's oceans. We show that the response characteristics are similar to those of step-change simulations, but in this new method the responses for 22 regions can be characterized simultaneously. Furthermore, we can estimate the timescale over which the steady-state response emerges. The proposed methodology could be useful for a wide variety of purposes in climate science, including characterization of teleconnections and uncertainty quantification to identify the effects of climate model tuning parameters.

Development of algorithm for depreciation costs allocation in dynamic input-output industrial enterprise model

The article considers the issue of allocation of depreciation costs in the dynamic inputoutput model of an industrial enterprise. Accounting the depreciation costs in such a model improves the policy of fixed assets management. It is particularly relevant to develop the algorithm for the allocation of depreciation costs in the construction of dynamic input-output model of an industrial enterprise, since such enterprises have a significant amount of fixed assets. Implementation of terms of the adequacy of such an algorithm itself allows: evaluating the appropriateness of investments in fixed assets, studying the final financial results of an industrial enterprise, depending on management decisions in the depreciation policy. It is necessary to note that the model in question for the enterprise is always degenerate. It is caused by the presence of zero rows in the matrix of capital expenditures by lines of structural elements unable to generate fixed assets (part of the service units, households, corporate consumers). The paper presents the algorithm for the allocation of depreciation costs for the model. This algorithm was developed by the authors and served as the basis for further development of the flowchart for subsequent implementation with use of software. The construction of such algorithm and its use for dynamic input-output models of industrial enterprises is actualized by international acceptance of the effectiveness of the use of input-output models for national and regional economic systems. This is what allows us to consider that the solutions discussed in the article are of interest to economists of various industrial enterprises.

Study of the multiplier effect of increasing public spending on Russia’s economy

This paper examines the impact of increase in public spending in Russia on the dynamics of macroeconomic and sectoral indicators using econometric methods and a dynamic input–output model.

Intensity and economic loss assessment of the snow, low-temperature and frost disasters: a case study of Beijing City

In this paper, an ordinal regression model and a K-means clustering method are employed to directly assess the intensity of snow, low-temperature and frost disasters in non-pasture areas. Then, economic loss caused by the disasters is evaluated quantitatively by static and dynamic input–output models, respectively. Finally, an empirical analysis is conducted taking Beijing as an example. Compared to historical data in Beijing, results show that snowstorms in 2010 occurred more frequently than before when the intensity was weaker. Meanwhile, the frequency of low-temperature and frost disasters occurring did not change much, but serious low-temperature disasters happened due to heavy snowfalls. The indirect economic losses of the snow, low-temperature and frost disasters occurring in Beijing in 2010 are 2.23 times the direct economic losses. And some sectors are very sensitive to the disasters. Furthermore, it is found that the total economic losses obtained by a dynamic input–output model are less than that evaluated by a static model.