Michael Angelo B. Promentilla

A VULNERABILITY INDEX FOR POST-DISASTER KEY SECTOR PRIORITIZATION

Input–output-based techniques have proven to be effective in modeling how disasters lead to economic disruptions, while taking into account the structural connectivity of economic systems. In particular, through the inoperability input–output model (IIM), the degree of failure in an economic system can be quantified on a scale from 0 (normal state) to 1 (complete failure). This paper develops a vulnerability index that builds upon the foundations of the Leontief input–output model and the IIM, which is capable of identifying and prioritizing the key sectors in the aftermath of disasters. The key sector prioritization framework proposed in this paper is expected to contribute to the domain of disaster preparedness planning, such as enhancing the efficiency of resource allocation across various sectors. The proposed vulnerability index is formulated in terms of three underlying components: (1) economic impact, (2) propagation length, and (3) sector size. The vulnerability index captures the impact of investments to various sectors in times of disaster in order to yield the maximum benefits to the entire economy. This paper considers a baseline scenario that assumes that the decision-maker has an equal preference for all index components. Using Monte Carlo simulation and sensitivity analysis, we investigated the extent to which the key sector rankings could fluctuate with respect to variations in the decision-maker preferences. Key sectors tend to be sensitive to the weight assignments across the three vulnerability index components; nevertheless, some sectors are less sensitive to such weight variations and may persist on their level of priority, independent of the scenario. Using the Philippine input–output data, we found that the private services sector is consistently a high-priority sector, the trade sector is a mid-priority sector while the real estate and ownership of dwellings sector tend to be a low-priority sector.

A methodology for augmenting sparse pairwise comparison matrices in AHP: applications to energy systems

Multiple-attribute decision making (MADM) techniques can be used to provide a systematic approach to selection problems in energy engineering and management. They may be used for selecting the best technologies or policies based on environmental, technical, and socio-economic criteria. Among the many available MADM techniques, the analytic hierarchy process (AHP) has become one of the most widely used due to its effective hierarchical decomposition of complex problems. However, AHP may be tedious due to the large number of pairwise comparisons needed in large problems. Furthermore, in many cases, relevant information may also be available for determining criteria weights based on past decisions that have proven satisfactory in retrospect. Thus, we propose a simple methodology for augmenting sparse pairwise comparisons in AHP through a non-linear programming model that extracts a set of consistent weights from a priori ranking of a subset of alternatives. Two case studies on the ranking of bioethanol feedstocks and of CO2 storage sites are then shown to illustrate this technique.

Optimizing and Characterizing Geopolymers from Ternary Blend of Philippine Coal Fly Ash, Coal Bottom Ash and Rice Hull Ash

Geopolymers are inorganic polymers formed from the alkaline activation of amorphous alumino-silicate materials resulting in a three-dimensional polymeric network. As a class of materials, it is seen to have the potential of replacing ordinary Portland cement (OPC), which for more than a hundred years has been the binder of choice for structural and building applications. Geopolymers have emerged as a sustainable option vis-à-vis OPC for three reasons: (1) their technical properties are comparable if not better; (2) they can be produced from industrial wastes; and (3) within reasonable constraints, their production requires less energy and emits significantly less CO2. In the Philippines, the use of coal ash, as the alumina- and silica- rich geopolymer precursor, is being considered as one of the options for sustainable management of coal ash generation from coal-fired power plants. However, most geopolymer mixes (and the prevalent blended OPC) use only coal fly ash. The coal bottom ash, having very few applications, remains relegated to dumpsites. Rice hull ash, from biomass-fired plants, is another silica-rich geopolymer precursor material from another significantly produced waste in the country with only minimal utilization. In this study, geopolymer samples were formed from the mixture of coal ash, using both coal fly ash (CFA) and coal bottom ash (CBA), and rice hull ash (RHA). The raw materials used for the geopolymerization process were characterized using X-ray fluorescence spectroscopy (XRF) for elemental and X-ray diffraction (XRD) for mineralogical composition. The raw materials’ thermal stability and loss on ignition (LOI) were determined using thermogravimetric analysis (TGA) and reactivity via dissolution tests and inductively-coupled plasma mass spectrometry (ICP) analysis. The mechanical, thermal and microstructural properties of the geopolymers formed were analyzed using compression tests, Fourier transform infra-red spectroscopy (FTIR), scanning electron microscopy (SEM) and thermogravimetric analysis (TGA). Using a Scheffé-based mixture design, targeting applications with low thermal conductivity, light weight and moderate strength and allowing for a maximum of five percent by mass of rice hull ash in consideration of the waste utilization of all three components, it has been determined that an 85-10-5 by weight ratio of CFA-CBA-RHA activated with 80-20 by mass ratio of 12 M NaOH and sodium silicate (55% H2O, modulus = 3) produced geopolymers with a compressive strength of 18.5 MPa, a volumetric weight of 1660 kg/m3 and a thermal conductivity of 0.457 W/m-°C at 28-day curing when pre-cured at 80 °C for 24 h. For this study, the estimates of embodied energy and CO2 were all below 1.7 MJ/kg and 0.12 kg CO2/kg, respectively.

Computing sustainable manufacturing index with fuzzy analytic hierarchy process

Abstract The growing industrial interest in adopting sustainability programmes has ushered in studies regarding sustainability indicators which have continually flourished in current literature. However, limited attention is given to the development of priority ranking, which is an important input for any adopting firm. This paper presents a hybrid multi-criteria approach in determining priority areas in sustainable manufacturing (SM). Using fuzzy analytic hierarchy process to address uncertainty in hierarchical decision-making, this paper determines SM priority strategies and eventually identifies even lower level strategies. The computed sustainable manufacturing index is presented at both the organizational and operational levels for a real case study of an industrial plastic manufacturing firm. This work provides a detailed and transparent hierarchical decision-making approach based on SM framework, the use of which could be valuable to practicing managers across industries in their pursuit of greater sustainability.

A SHOCK ABSORPTION INDEX FOR INOPERABILITY INPUT–OUTPUT MODELS

Recent disasters have underscored the importance of enhancing resilience in economic systems. In this work, we propose a novel shock absorption index, which provides a measure of the ability of an economic system to tolerate disruptions. It is assumed that there are externally defined initial levels of system failure or disruption, as well as maximum allowable levels of inoperability for each sector. The shock absorption index is defined as the largest fraction of the anticipated initial disruption that can be absorbed by the predefined robustness limits. It provides an overall measure of the robustness of an economic system towards a disruptive event, which is driven by both the economic structure and the individual robustness of different sectors. The results of two case studies illustrate policy-making insights in identifying and prioritizing risk management strategies for critical systems.

A Fuzzy Programming Approach to Multi-Objective Optimization for Geopolymer Product Design

Abstract Geopolymer is an inorganic polymer binder formed from the alkaline activation of reactive alumino-silicate materials resulting in two- or three-dimensional polymeric network. It is a promising alternative to Portland cement-based materials because of its lower embodied energy and carbon footprint with potential for waste valorization. Studies have been done to develop such material with desired engineering specification by using statistical design of experiment and optimizing the process conditions or mix formulation of waste materials. However, it is not only the engineering properties such as its mechanical and thermal properties, but also other properties pertaining to green materials (e.g., embodied energy and carbon footprint) have to be considered. Conflicting objectives may also have to be satisfied simultaneously to find a compromised solution in the product design such as that of maximizing the strength and minimizing the volumetric weight. This work thus proposes a weighted max-min aggregation approach to multi-objective optimization of the geopolymer product using fuzzy programming approach. The optimization formulation was introduced such that fuzzy sets represent both the aspired product desirability and soft constraints; the optimal mix is then found by maximizing the simultaneous satisfaction of target properties of the desired product. This work also proposes an extension of such fuzzy optimization formulation wherein the nature of trade-off between improving the product desirability and satisfying the fuzzy constraints are made explicit. The relative importance of the properties as represented by priority weights were derived systematically using Analytic Hierarchy Process (AHP). A case study on a ternary blended geopolymer from coal fly ash, coal bottom ash, and rice hull ash is presented to illustrate the proposed method.

Application of stochastic analytic hierarchy process for evaluating algal cultivation systems for sustainable biofuel production

Algal biomass is considered as a promising source of alternative fuel energy given its high yield per land area and other potential benefits. Categorized as an advanced generation biofuel feedstock, microalgae are grown in non-conventional ways through different cultivation systems. A preference of a cultivation system may vary depending on a given scenario and its inherent configuration (strength and weakness). Hence, the usage of a specific cultivation system to sustainably produce algal biofuels depends on various factors. Thus, a multi-criteria approach based on analytic hierarchy process (AHP) is proposed for evaluating alternative cultivation systems for sustainable production of algal biofuels. The main criteria considered to evaluate the alternatives based on consultation with a panel of expert and from literature are environmental impact, energy consumption, economic viability, social acceptability, and system robustness. Sub-criteria were identified under each main criterion to further qualify the analysis into relevant sub-factors in the sustainable production of algal biofuels. Three cultivations systems were used as an example to demonstrate the developed decision model using qualitative data and quantitative data. Probabilistic scenarios were analyzed using stochastic approach via Monte Carlo simulation. The results of the stochastic-based AHP showed which cultivation system is preferred for conservative (risk-averse) and optimistic (risk-inclined) scenarios.

Development of Geopolymer-Based Materials from Coal Bottom Ash and Rice Husk Ash with Sodium Silicate Solutions

The coal bottom ash is a solid waste generated from coal-fired thermal power plants that contains high alumino-silicates resources. Rice husk ash was burned from rice husk which has over 80% silica in its chemical composition. The alumino silicates resources in these materials have high reactivity in various conditions such as that of alkaline reactions and thermal reactions. Therefore, both coal bottom ash (CBA) and rice husk ash (RHA) are promising raw materials for synthesizing alkali activated materials through geopolymerization. This study focuses on utilization of CBA and RHA to produce geopolymer – based materials using sodium silicate solution as an alkali activator. This is one of the potential solutions that would not only manage the coal bottom ash but also an avenue to utilize the waste to produce green materials. The production of geopolymer-based materials results to lower energy consumption, minimal CO2 emissions and lower production cost as it valorizes industrial waste. The CBA and RHA were mixed with sodium silicate solution to obtain the geopolymeric pastes. The pastes were molded in 5-cm cube molds according to ASTM C109/C109 M 99, and then cured at room temperature for 28 days. The 28-day geopolymer specimens were tested for engineering properties such as compressive strength (MPa), volumetric weight (kg/m3), water absorption (kg/m3) and thermal conductivity (W/m.K). Microstructure of the best geopolymer sample was characterized by using X-ray diffraction (XRD), Fourier transform infrared spectroscopy (FTIR), and scanning electron microscope (SEM).

A weighted fuzzy linear programming model in economic input–output analysis: an application to risk management of energy system disruptions

Climate change exposes economic systems to numerous risks, including reduced agricultural production and electric power supply shortages. The interdependent nature of economic systems causes disruptions in any sector to cascade to other sectors via forward and backward linkages. This work develops an optimization model with which allocation of scarce goods or resources can be optimized; the model uses an overall index of satisfaction of fuzzy economic output goals under conditions of scarcity caused by climatic disruptions. The proposed model includes a vulnerability measure that integrates information elicited from expert judgment. A case study based on a scenario of drought-induced electricity shortage in the Philippine economy is examined. Results show that trade, transportation and service-oriented industries suffer losses in gross domestic product in the Philippine case.

Multi-criteria approach to assess stakeholders preferences for selection of biodiesel feedstock in Vietnam

Biodiesel has been proposed as an alternative to petroleum diesel fuel in Vietnam but Vietnam is a net importer of vegetable oil. Decision-makers face challenges in crafting a long-term policy for sustainable development of biodiesel that balances social, technological, economic and environmental aspects. The analytic hierarchy process (AHP) was applied in this study to find the most appropriate feedstock for biodiesel production in Vietnam among three possible options: jatropha oil, fish fat and waste cooking oil. The judgments of different Vietnamese stakeholders, such as academics, heads of biodiesel projects, managers in Petrovietnam Corporation, and engineers were incorporated. The stakeholders showed different preferences as reflected in the priority weights of criteria and alternatives. The priority weights of alternatives under the judgments of multiple stakeholders indicate that waste cooking oil is the most preferred feedstock to produce biodiesel in Vietnam followed by jatropha oil and fish fat.