Input-output Modeling Approach Research Articles

Disinfection scheduling in water distribution networks considering input time-delay uncertainty

Abstract A significant challenge when attempting to regulate the spatial-temporal concentration of a disinfectant in a water distribution network is the large and uncertain delay between the time that the chemical is injected at the input node and the time that the concentration is measured at the monitoring output nodes. Uncertain time delays are due to varying water flows, which depend mainly on consumer water demands. Existing approaches cannot guarantee that the concentration of the disinfectant will remain within a specified range at the output, even though bounds on time-delay uncertainty may be known. In this work, given bounded water-flow uncertainty, we use the input–output modeling approach to develop a disinfectant scheduling methodology that guarantees a bounded output disinfectant concentration. The proposed methodology creates an input–output model uncertainty characterization by utilizing estimated bounds on water-quality states using the backtracking approach. An optimization problem is formulated and solved to find an input schedule that keeps the disinfectant concentration within predefined bounds for a specified time horizon. Simulation results in two case studies where water demands varied between ±20% of their nominal value show that the proposed scheduler is able to avoid lower bound violations of disinfectant concentration.

Assessing the performance of the transport sector within the global supply chain context: Decomposition of energy and environmental productivity

With globalization and industrialization, the supply chain of the transport sector has become increasingly complex and environmentally polluting. Consequently, the multidimensional and transnational nature of this sector presents numerous challenges in performance estimation, hindering previous studies. To address these issues, we propose an input-output modeling approach based on the Multi-region input-output model (MRIO) and Bounded-adjusted Measure (BAM). This approach allows for a comprehensive assessment of the sector's performance across various dimensions within a global context. Building upon this approach, we construct two indicators: the static transportation sustainability inefficiency (STSI) and the transportation sustainability productivity indicator (TSPI). Moreover, we introduce a systematic decomposition framework, encompassing both horizontal and vertical aspects, to analyze and break down the STSI and TSPI. Empirically, we apply this framework to study the sustainability performance of the global transport sector, encompassing 43 economies, over the period of 2005–2014. The results indicate that within transportation-related Global Supply Chains (GSCs), there is a potential for approximately 5% reduction in energy use, 7% reduction in CO2 emissions, 8% reduction in SO2 emissions, and 8% reduction in NOx emissions over the course of the decade. On average, a 1.0% increase in TSPI was observed, primarily driven by technological progress. From a global perspective, the sustainable development of the transport sector relies more on less developed economies. These findings point to the necessity of subsidizing these economies, which provide spillover effects through global supply chain.

The Role of the Forest Recreation Industry in China’s National Economy: An Input–Output Analysis

Forest recreation not only enables directly creates impacts on the entire tourism industry but also indirectly creates effects on other industries composed in an economic system via inter-industrial interdependence, such as backward and forward linkages or interactions. However, due to the lack of data available, few studies have been made exclusively for evaluating the impacts of forest recreation on the economy in a national dimension. Therefore, this paper attempts to analyze the economic impacts of the forest recreation industry. Using an input–output modeling approach, the industrial linkage and a cluster of economic multipliers are estimated based on new input–output tables for 2012, 2017, and 2018 that includes the forest recreation industry. The results indicate that, first, both the forward and backward linkages caused by forest recreation show rising trends over the analysis periods and the backward linkages are stronger than forward linkages. Second, the economic contribution of forest recreation has been strengthened, although the multipliers have shown a downward trend. The total output impact in 2018 is calculated to be $838.187 billion, with a total added impact of $352.713 billion. Furthermore, forest recreation could create over 18 million jobs with an average annual growth rate of 15.73%, which are mainly in the catering, accommodation, and retail industries with low skill requirements. Finally, policy applications are proposed to provide reliable and useful information for future planning and investment decision associated with the forestry and ecotourism industry.

Tracing water use by sectors in Morocco: An input–output modeling approach

AbstractIn recent decades, Morocco has experienced a strong and rapid increase in water demand due to many challenges such as population growth, urbanization sprawl, and economic development. Besides, climate change and its negative impact on natural water resources availability have put the supply–demand balance in jeopardy. This imperative has created a priority need to analyze the demand structure and identify how economic sector linkages mitigate or exacerbate water shortage. In this paper, a methodological framework based on an input–output model has been proposed to capture not only sectors with a large drag effect on water consumption of the whole economy but also the propagation of water use from both diffusion and absorption effects. The results show that the responsibility for water stress is attributed to different sectors and depends on the exogenous final demand shocks. This highlights the significance of consumers' decisions in the determination of ecological impact assessment.

Economic contribution of the U.S. catfish industry

The catfish industry is the largest U.S. aquaculture sector and a major contributor to the rural economies of the Southern states of Alabama, Arkansas, and Mississippi. Economic contributions of this industry to the tristate region were estimated using an input-output modeling approach with the IMPLAN database and software (Impact Analysis for Planning MIG, Inc.). An analysis-by-parts approach was employed as the IMPLAN database does not disaggregate the catfish sector from other livestock industries which have expenditure patterns that differ substantially from those of catfish. All major actors of the catfish industry, i.e. feed mills, hatcheries, foodfish farms, and processing plants, were surveyed to obtain their expenditure patterns and output in terms of sales. Data were obtained from 68 farms (hatcheries and foodfish combined), four feed mills, and eight processing plants in the tristate region. Sales and expenditure values were converted to coefficients employing standard enterprise budgeting techniques and imported to the IMPLAN model for estimating industry contributions during 2019. The direct output from the catfish industry ($1.10 billion) generated a total economic contribution of $1.91 billion. The industry directly employed 4,298 people and created an additional 4,868 jobs in the tristate economy for a total employment effect of 9,166 jobs. Catfish industry spending created an indirect economic effect of $552 million in other secondary sectors that supplied production inputs and services. The induced economic effect generated from household spending amounted to $254 million. Some of the key sectors influenced by the catfish industry were grain farming, banking and financial institutions, truck transportation services, electricity generation, equipment, and machinery manufacturing. The industry also generated $78 million in local, state, and federal taxes. This study provides critical insights for policymakers and others into the contribution of the U.S. farm-raised catfish industry to local and regional economies as well as its diverse-industry interconnections.

Assessing sustainability environmental performance of three urban agglomerations in China: An input–output modeling approach

As a special form of a cluster city, urban agglomeration has become an important platform to support national economic growth and to participate in international competition and cooperation, but it faces severe environmental problems. This research thus selects panel data of 60 cities in the Beijing-Tianjin-Hebei urban agglomeration (BTH), Yangtze River Delta (YRD), and Pearl River Delta (PRD) from 2013 to 2017. Based on the input–output perspective, labor and energy consumption per GDP are selected as input indicators, GDP, PM2.5, SO2, industrial smoke and dust emissions, and wastewater discharged as output indicators, and fixed assets as a carry-over variable in the dynamic DEA model to evaluate and analyze the environmental efficiencies of these three urban agglomerations. Findings show that their total environmental efficiency exhibits a wave-like development trend, and that the total efficiency score of BTH is better than that of PRD and YRD. From the viewpoint of a sub-efficiency index, PM2.5 is the lowest in unexpected output, where its average five-year efficiency score is 0.9847, while the average score of industrial smoke and dust emissions efficiency is the lowest at 0.6199. Finally, this paper offers suggestions for different problems facing the three urban agglomerations.

The Energy Climate Water Nexus: A Global Sustainability Impact Assessment of U.S. Manufacturing

This study assesses the global economic and environmental impacts of United States manufacturing industries over a 20-year period, covering the areas of economics, climate change, and water and energy use. It covers 35 industries located in 40 major economies and the Rest of the World (ROW) from 1995 to 2014. To measure the global sustainability impacts generated by the activity of U.S. manufacturing industries, a multi-region input-output (MRIO) modeling approach is proposed, and twenty MRIO models were built to conduct a time-series analysis. The findings indicate that economic impacts increased both domestically and globally, except in the year 2008, and that the intensity of environmental impacts decreased substantially, while cumulative environmental impacts exhibited a non-decreasing trend. The three categories under investigation were found to be strongly linked, which calls for further policy developments by engineering managers, who should take into account all of the categories together, rather than singly.

Assessing sustainability performance of global supply chains: An input-output modeling approach

Measuring the sustainability performance of supply chains is fundamental to sustainable supply chain management. Sustainability performance is usually evaluated from multiple aspects within the triple bottom line framework. With globalization, supply chains have also been characterized by the complex and global natures. Ignoring the multidimensional and transnational features imposes challenges on the performance assessment of global supply chains (GSCs). To resolve this issue, we propose an input-output modeling approach based on the multi-region input-output (MRIO) model and the data envelopment analysis (DEA) technique, which is able to account for the multidimensional characteristic of supply chains in a global context. Two indices are introduced to measure the status and evolvement of environmental sustainability performance of GSCs. We apply the proposed approach to empirically examine the environmental performance of GSCs of the manufacturing sectors in 16 major economies during 2005–2014. The average environmental inefficiency of the economies was considerable, and roughly 40% of the pollution could potentially be reduced along GSCs. Overall the environmental performance of GSCs averagely rose by 20.6% during the study period with fluctuations and regional/sectoral heterogeneities observed.

Assessing the Indirect Economic Losses of Sea Ice Disasters: An Adaptive Regional Input-Output Modeling Approach

Assessing the Indirect Economic Losses of Sea Ice Disasters: An Adaptive Regional Input-Output Modeling Approach

City-level household carbon footprint from purchaser point of view by a modified input-output model

Residential consumption produces carbon emissions through the combustion of primary energy (directly) as well as through the consumption of goods and services (indirectly). To capture the latter indirect emissions, it is common to use producer-price based input-output analysis associated with household monetary expenditures. However, producer-price input-output tables usually do not account for economic transactions and energy flows at the margin. To better understand household indirect emissions from the purchaser perspective, there is a need for a modified input–output modeling approach. To that end, this study attempts to measure the carbon footprint of households in the Tokyo Metropolis, Japan by integrating monthly household consumption inventories into an input–output table of Tokyo. By doing so, the carbon footprint was estimated to be 0.76 tC per household and the total estimated marginal carbon emissions for Tokyo is up to 0.56 MtC. A sectoral breakdown of the total emission coefficients is provided based on purchaser prices and temporal variations in emissions. Findings indicate that within household consumption, Clothing and Services categories contributed most to marginal emissions. The conclusions drawn in this paper are pertinent and policy relevant because they provide relatively accurate information compared to the status quo to inform appropriate policies for guiding residential consumption at the city scale of resolution.

Input-output modeling approach to measuring structural shift of output and macroeconomic productivity: the case of Slovakia

Input-output modeling approach to measuring structural shift of output and macroeconomic productivity: the case of Slovakia

Identification of dynamical systems population described by a mixed effect ARX model structure

System identification is a data-driven input-output modeling approach more and more used in biology and biomedicine. In this application context, several assays are repeated to estimate the response variability and reproducibility. The inference of the modeling conclusions to the whole population requires to account for the inter-individual variability within the modeling procedure. One solution consists of using mixed effects models but up to now no similar approach exists in the system identification literature. In this article, we propose a first solution based on an ARX (Auto Regressive model with eXternal inputs) structure using the EM (Expectation-Maximisation) algorithm for the estimation of the model parameters. Using the Fisher information matrix, the parameter standard errors are estimated; this allows for group comparison tests. Simulations show the relevance of this solution compared with a classical procedure of system identification repeated on each subject. Taking into account all the information available in the population allows to gather the parameters between individuals.

Design Operability and Retrofit Analysis (DORA) framework for energy systems

A systematic framework for Design Operability and Retrofit Analysis (DORA) is presented. DORA is a framework explicitly analyzing energy system design containing process units functioning at different levels of operability. To express the operability of individual process units, DORA uses inoperability input-output modeling (IIM) approach. Based on IIM, a simple mixed integer linear programming (MILP) model is developed to analyze the impact of individual process unit inoperability on the flexibility of an energy system design. In the case where a design is deemed to possess insufficient flexibility to meet demands, DORA framework subsequently entails a step-by-step guide to debottleneck and retrofit a given design based on benefit-cost ratio (BCR). In this work, the DORA framework is demonstrated using a biomass-based tri-generation system (BTS) case study. As shown in the case study, DORA framework is used to determine whether a BTS facing a drop in individual unit efficiency, would require debottlenecking and retrofitting to increase its energy production.

An input-output modeling approach as an effective development strategy for Sri Lanka

This paper is aimed at measuring industrial interdependence using a symmetric input-output table complied for the Sri Lankan economy for 2006 to find an effective development strategy for the country. The input-output model is constructed for thirty aggregated industrial sectors of the economy as a whole. The output, value added and income multipliers reveal that there are five key industries of the Sri Lankan economy. These are: (1) recreational, cultural, sporting services and other services; (2) manufactured products of food, beverages and tobacco sector; (3) air transport services; (4) rubber & plastic products; and (5) metallic, non-metallic and mineral products. The employment multipliers are highest in: (1) real estate services; (2) electrical products; (3) petroleum & chemical products; and (4) rubber & plastic products industries. We found that five industries (sectors) have strong upstream and downstream vertical integrations with the rest of other sectors in the economy. The results reveal that higher prices charged on the petroleum & chemical products would probably result in higher costs to most of other sectors in the economy relatively equally. As ‘mining and quarrying, electricity, gas and water’ sector has strong downstream linkages to other sectors in the economy, higher prices (or taxes) charged on these products also result in higher costs to most of other sectors in the economy. The findings of the study reveal that prioritizing industries should be done based on an input-output analysis rather than just depending on the information provided by percentage of contribution in output and value addition to GDP by the sectors. However, the results should be interpreted very carefully as the impact of some sectors such as education difficult to be practically measured in monetary terms based on an input-output model.

Theoretical formulation and experimental validation of the input–output modeling approach for large solar thermal systems

In this study, a theoretical and experimental investigation of a new modeling method for solar thermal processes, based on the input–output (I/O) approach is presented. This approach is characterized primarily by the consideration of the solar thermal system, irrespective of size, as a functionally integral set, the daily yield of which is expressed as a function of the total daily radiation, the average ambient temperature and the energy level of the storage tank at the beginning of the day. The study focuses first on the theoretical foundation of the method, based on the analysis of the evolution of the main energy quantities during a daily cycle, with emphasis on the energetically most important phase of the pseudo-steady state. By integrating the instantaneous energy balances over the entire duration of this phase, analytical expressions for the coefficients of the characteristic I/O equation arise, as a function of the main characteristics of the solar system. Subsequently, the experimental validation of the proposed approach is presented, based on the comparison of theoretical results with extensive measurements on two typical large-scale solar systems, one with a fully-stratified and the other with a fully-mixed thermal storage tank, respectively.

An adaptive input–output modeling approach for predicting the glycemia of critically ill patients

In this paper we apply system identification techniques in order to build a model suitable for the prediction of glycemia levels of critically ill patients admitted to the intensive care unit. These patients typically show increased glycemia levels, and it has been shown that glycemia control by means of insulin therapy significantly reduces morbidity and mortality. Based on a real-life dataset from 15 critically ill patients, an initial input–output model is estimated which captures the insulin effect on glycemia under different settings. To incorporate patient-specific features, an adaptive modeling strategy is also proposed in which the model is re-estimated at each time step (i.e., every hour). Both one-hour-ahead predictions and four-hours-ahead simulations are executed. The optimized adaptive modeling technique outperforms the general initial model. To avoid data selection bias, 500 permutations, in which the patients are randomly selected, are considered. The results are satisfactory both in terms of forecasting ability and in the clinical interpretation of the estimated coefficients.

Energy Efficiency, Fuel Switching, and Environmental Emissions: The Case of High Efficiency Furnaces

From the environmental policy maker's point of view, the increase in energy prices in the 1970s and early 1980s had the positive effect of reducing pollution emissions by encouraging energy conservation. In particular, increases in energy efficiency in industrial and residential furnaces reduced emissions of sulphur and nitrogen oxides (SOx and NOx). Thus, environmental regulators were able to identify improvements in environmental quality, or mitigation of environmental degradation, achieved without changes in regulatory policy or increased expenditures for enforcement. The existence of market-driven improvements in environmental quality is particularly important given the well-documented negative impacts of environmental regulations on the cost of production [9; 6], on balance of trade [7], and productivity growth [1; 2]. This paper uses an input-output modeling approach to examine the net (direct and indirect) impact on SOx and NOx emissions of one million households switching from conventional heating to the newer high-efficiency gas heaters.' As a result of the switch, environmental quality is affected in three ways: 1) Direct emissions by households are reduced as a result of increased efficiency and the resultant reduction in fuel use; 2) The reduction in household use of fuels produces corresponding reductions in energy extraction, processing, and transportation activities; and 3) Other emissions change in response to changes in consumer spending patterns. A 78 sector input-output model is used to track the effects of changes in fuel use and purchasing power on sectoral outputs and emissions, effects which are missed in a simpler partial-equilibrium approach.

The Industry Composition of Production and the Distribution of Income by Race and Ethnicity in Miami

This paper examines the empirical link between the growth of production in a central city and the distribution of income among its residents, by race and ethnicity, within an input-output modeling approach. The specific case of Miami, Florida is considered. This paper reviews the recent historical experience, which reveals considerable growth of employment in the central city at the same time that per capita income of city residents has declined. The decline in income has been particularly acute among black families. This discussion is followed by a presentation of the model and the estimation of model parameters. Income multipliers and their distributional effects are presented. The results show that in Miami, the present character of economic growth has few direct economic benefits for a large majority of the city’s residents.