Decision making under uncertainty and inertia constraints: sectoral implications of the when flexibility
نویسندگان
چکیده
Current debates on mitigation emphasize the role of the inertia of the economic system. Our aim in this paper is to study in more depth how sectorally dif ferentiated inertia impacts on optimal CO2-emission abatement policies. Using the STARTS model, we show that optimal abatement levels and costs differ sensibly among sectors. Differential inertia is the critical determinant of this trade-off, especially in the case of a 20-year delay in the action, or in an underestimation of the growth of the transportation sector. In particular, the burden of any additional abatement efforts falls on the most flexible sector, i.e. the industry. Debates on mitigation emphasize the role of inertia of the economic system. This paper aims at studying more in depth how sectorally differentiated inertia should influence, optimal CO2 emission abatement policies. Using a two-sector version of STARTS, we show that under perfect expectations, optimal abatement profiles and associated costs differ sensibly between a flexible and a rigid sector (transportation).In a second step, we scrutinize the role of the uncertainty by testing the case of a 20-year delay of action and an underestimated growth of the transportation sector. We do this for three concentration ceilings and we point out the magnitude of the burden which falls on the flexible sector. We derive some policy implications for the ranking of public policies and for incentive instruments to be set up at international level. © 1998 Elsevier Science B.V. All rights reserved.
منابع مشابه
Optimal Cropping Pattern Modifications with the Aim of Environmental-Economic Decision Making Under Uncertainty
Sustainability in agricultural is determined by aspects like economy, society and environment. Multi-objective programming (MOP) model has been a widely used tool for studying and analyzing the sustainability of agricultural system. However, optimization models in most applications are forced to use data which is uncertain. Recently, robust optimization has been used as an optimization model th...
متن کاملA New Compromise Decision-making Model based on TOPSIS and VIKOR for Solving Multi-objective Large-scale Programming Problems with a Block Angular Structure under Uncertainty
This paper proposes a compromise model, based on a new method, to solve the multi-objective large-scale linear programming (MOLSLP) problems with block angular structure involving fuzzy parameters. The problem involves fuzzy parameters in the objective functions and constraints. In this compromise programming method, two concepts are considered simultaneously. First of them is that the optimal ...
متن کاملA novel risk-based analysis for the production system under epistemic uncertainty
Risk analysis of production system, while the actual and appropriate data is not available, will cause wrong system parameters prediction and wrong decision making. In uncertainty condition, there are no appropriate measures for decision making. In epistemic uncertainty, we are confronted by the lack of data. Therefore, in calculating the system risk, we encounter vagueness that we have to use ...
متن کاملUtilizing Decision Making Methods and Optimization Techniques to Develop a Model for International Facility Location Problem under Uncertainty
Abstract The purpose of this study is to consider an international facility location problem under uncertainty and present an integrated model for strategic and operational planning. The paper offers two methodologies for the location selection decision. First the extended VIKOR method for decision making problem with interval numbers is presented as a methodology for strategic evaluation of po...
متن کاملA New Balancing and Ranking Method based on Hesitant Fuzzy Sets for Solving Decision-making Problems under Uncertainty
The purpose of this paper is to extend a new balancing and ranking method to handle uncertainty for a multiple attribute analysis under a hesitant fuzzy environment. The presented hesitant fuzzy balancing and ranking (HF-BR) method does not require attributes’ weights through the process of multiple attribute decision making (MADM) under hesitant conditions. For the rating of possible alternati...
متن کامل