Input Output Linear Programming Model Research Articles

Cutting CO2 emissions through demand side regulation: Implications from multi-regional input—output linear programming model

Cutting CO2 emissions through demand side regulation: Implications from multi-regional input—output linear programming model

Electrifying light-duty passenger transport for CO[formula omitted] emissions reduction: A stochastic-robust input–output linear programming model

Electrification of light-duty passenger vehicles has been widely considered as an important strategy for decarbonizing the transport sector. This study proposes a novel input–output linear programming model with stochastic-robust optimization methodology to identify the optimal pathways to reduce direct and embodied emissions from light-duty passenger transport under technology cost and emission intensity uncertainties. The model introduces detailed transport technologies into a well-known economic input–output model so that the carbon dioxide (CO2) emissions embodied in production inputs of vehicles in the entire economic system can be fully accounted for. We apply the model to a case study in China, which shows that tightening cumulative CO2 emissions from light-duty passenger transport by 30% of the base case requires large deployment of electric vehicles, especially plug-in hybrids and battery electric vehicles. The emission reductions mainly come from vehicle operation-related emissions. Nevertheless, the total reduction in operation-related emissions is offset by the increase in capital-related CO2 emissions by 13%–18%, depending on the variability in future emission intensity of electricity. Finally, we compare the data uncertainty handling performance of stochastic-robust model with a stochastic programming model and two deterministic optimization models under optimistic and pessimistic parameter settings. The comparison shows that the optimal technology portfolio generated from the proposed model is not only reliable in achieving the predefined emissions target, but is also able to reduce the total investment and operation costs in the light-duty passenger transport sector under data uncertainty.

Optimizing electricity mix for CO2 emissions reduction: A robust input-output linear programming model

Input-Output Linear Programming (IO-LP) model has been recently used to identify a cost-effective strategy for reduction in economy-wide CO2 emissions through a shift in the electricity generation mix. As an extension, this study further develops a robust IO-LP model to address the data uncertainties of technology cost and final demand. Compared to the deterministic IO-LP model which seeks to minimize the levelized cost of electricity (LCOE), the robust IO-LP model aims to maximize the tolerance of data uncertainty under a dynamic uncertainty setting. The modelling results in case study of China show that coal-fired and hydro generation technologies should be greatly developed from 2020 to 2050 in the Business-As-Usual (BAU) scenario with no emissions target set. In order to mitigate accumulated economy-wide CO2 emissions by 30% compared to the BAU emissions level, various types of clean generation technologies, i.e., gas-fired, hydro, nuclear, solar, wind, and biomass, should be introduced into the electricity mix. Along with the decrease in emissions target, the tolerance of data uncertainty will drop to a certain degree. Finally, we compared results of the robust IO-LP model with results of the stochastic and deterministic IO-LP models. The comparative analysis shows that the robust IO-LP model tends to select the generation technologies with smaller uncertainty in LCOE, and is able to improve the robustness of capacity planning solutions compared to the alternative models under data uncertainty.

Optimizing the Chinese Electricity Mix for CO2 Emission Reduction: An Input-Output Linear Programming Model with Endogenous Capital.

This study develops an input-output linear programming (IO-LP) model to identify a cost-effective strategy to reduce the economy-wide carbon dioxide (CO2) emissions in China from 2020 to 2050 through a shift in the electricity generation mix. In particular, the fixed capital formation of electricity technologies (FCFE) is endogenized so that the capital-related CO2 emissions of various generation technologies can be captured in the model. The modeling results show that low-carbon electricity, e.g., hydro, nuclear, wind, and solar, is associated with lower operation-related CO2 emissions but higher capital-related CO2 emissions compared to coal-fired electricity. A scenario analysis further reveals that a shift in the electricity generation mix could reduce the accumulated economy-wide CO2 emissions in China by 20% compared to the business-as-usual (BAU) level and could help peak China's CO2 emissions by 2030. The emission reduction is mainly due to a drop in operation-related CO2 emissions of electricity, contributing to a decrease in accumulated economy-wide emissions by 21.4%. The infrastructure expansion of electricity, on the other hand, causes a rise in the accumulated emissions by 1.4%. The proposed model serves as an effective tool to identify the optimal technology choice in the electricity system with the consideration of both direct and indirect CO2 emissions in the economy.

Energy-economic resilience with multi-region input–output linear programming models

In this work we propose an input–output linear programming model to study the energy resilience of a multi-region economy that accounts for the inter-sectoral and inter-regional dependencies in the economic structure. The model computes the impacts of random energy production disruptions on the entire multi-region input–output system in the form of final demand deficits. We then propose a formulation to evaluate the energy resilience of the system based on computing the largest set of disruptions, or what is termed as resilience guarantee levels, that can be sustained by the system within given demand deficit budgets. A computational study is then performed using a 3-region-42-sector model based on China's 2012 multi-regional input–output table. The results show that regional trade barriers, coal-to-gas switch policies and technological efficiency of selected energy-intensive industries can influence China's energy resilience significantly. This can provide the policy-makers important guidance on improving energy resilience in a systematic manner.

Identifying industrial restructuring strategy for CO2 emissions reduction in China: A multi-region input-output linear programming model

The current paper combines multi-regional input-output model and linear programming model to identify industrial shift strategies for CO2 emissions reduction in China. As a supplement to the previous studies, the optimal sequence of demand regulation for various products is explored. The results show that demand side regulation would pose negative effect on both GDP and CO2 emissions. However, certain strategies can be adopted to decrease CO2 emissions at the minimum decrease in GDP. According to the optimal sequence analysis, a group of key final products, such as the metallurgy products, the nonmetal products, the metal products, and the chemical products should be firstly regulated. Most of these key products concentrate in the eastern and coastal regions in China. Our model can be used to aid policy makers in design of effective industrial restructuring policy to achieve the national emissions targets.

An input–output linear programming model for assessing climate policy considering economic growth

Deploying new strategies to reduce the effect of climate change may constrain economic growth. It is thus necessary to develop a model which evaluates the trade-off between economic and environmental influences prior to a policy implementation. Recent studies have proved the effectiveness of input–output linear programming model in identifying the optimal solutions when different climate policies are considered. However, analyzing sectoral linkage to give priority sectors and then finding optimal solutions through reducing pollution from these sectors, which help avoid the economic losses from low-polluting sectors, have not been figured out in previous works. This study first uses input–output an (IO) analysis to provide a measure of structural interdependence among economic sectors and present priority sectors. An IO optimization model is then developed for minimizing the total greenhouse gas (GHG) emissions, in order to identify strategies for GHG intensity reduction in Vietnam, focusing on the priority sectors. In addition, the effect of GHG emissions on human health using the disability adjusted life years (DALY) is further evaluated. Six scenarios are considered to identify the potentials of highest GHG intensity reduction that can be obtained by the year 2020. These scenarios encompass BAU, the consideration of different GDP growth rates, differentiated economic sector growth, economic restructure, and the adaptation of lower-pollution technology implementation for the priority sectors. Each scenario quantifies sectoral final demand, sectoral gross domestic output, sectoral GHG emissions, GHG intensity, and DALY. The linkage analysis results indicate that agriculture, fishery and forestry, transport and communication, personal, community and household, manufacturing of non-metallic mineral products, and mining and quarrying are priority sectors. The optimization solutions present that the best strategy is by taking advantages of identified measures. The best solution obtains 20.3% reduction in GHG intensity compared to baseline. These obtained results become the useful suggestions for decision makers and environmental management in designing successful environmental regulations.

Energy-economic recovery resilience with Input-Output linear programming models

In this work we develop a novel Input-Output linear programming model to study the energy-economic recovery resilience of an economy by analyzing the relationships between energy production disruption, impacts on sectoral production and demands, and post-disruption recovery efforts. The proposed model evaluates the minimum level of recovery investments required to restore production levels so that total economic impacts are acceptable over a stipulated post-disruption duration. It is assumed that disruptions are uncertain and can occur at different sectors and possibly simultaneously. The optimization model is then solved using a cutting plane method which involves computing a small sequence of mixed integer programming problems of moderate dimensions. A case study using China 2012 Input-Output data is performed, and we demonstrate the model's ability to uncover critical inter-sectoral dependencies at different disruption levels. This provides decision-makers with important information in evaluating and improving the energy-economic resilience in a systematic and rigorous manner.

Reducing Brazilian greenhouse gas emissions: scenario simulations of targets and policies

ABSTRACTThis paper aims to evaluate the economic impacts of greenhouse gas emission reduction on the Brazilian economy. To this end, we developed an integrated input–output linear programming model for 2009 using the Supply and Use Tables and emissions data of the Brazilian Ministry of Science and Technology and Innovation. We simulated emissions targets for various potential scenarios in which the adopted policy design took account of sectoral composition in terms of emissions and available production technology. The results were directly affected by the high level of livestock emissions, counterbalancing this sector’s economic importance for Brazil. In the short term, sectoral emissions targets associated with taxation policy or emission permits could be developed in order to create private incentives to mitigate emissions. In this sense, the results also show that different sectoral targets may be able to balance environmental benefits with the possible economic losses incurred by such policies.

Identifying Lowest-Emission Choices and Environmental Pareto Frontiers for Wastewater Treatment Wastewater Treatment Input-Output Model based Linear Programming

This article proposes a linear programming model that is based on the wastewater treatment input-output model (W2IO) to identify the lowest-emission choice among alternative feasible options for wastewater treatment; this model can be considered as an application of the waste input-output linear programming model (WIO-LP) to wastewater issues. Using the data of the Tokyo metropolitan W2IO table, I apply this model to obtain the optimal wastewater treatment options under alternative scenarios. The Pareto frontiers of environmental loads are derived to show the trade-off relationships among various types of environmental load and the effect of the introduction of high-temperature incineration of dewatered sludge on the generation of environmental loads. The main conclusion of the study is that when all three types of environmental load (landfill level, global warming potential, and chemical oxygen demand) are considered, the introduction of high-temperature incineration causes the widening of the Pareto frontier of environmental loads and also causes it to move closer to the origin.

Waste input–output linear programming model with its application to eco-efficiency analysis

This paper is concerned with a decision analytic extension of the waste input–output model, based on the method of linear programming. The resulting model, which is named the waste input–output linear programming model, allows one to automatically obtain an ‘optimal’ waste management and recycling strategy from among a given set of alternative feasible strategies. The model can thus explore the extent to which a given measure of eco-efficiency can be maximized by an appropriate combination of existing (technological and resource) potentials. An application to Japanese data is also presented.