• Energy Research

Abstract Climate change impacts may manifest via multiple pathways, often leading to a shortage of resources, reduction in production capacities, or reduction in available labor inputs that are vital for economic activities. Effective climate change adaptation strategies are needed to determine the optimal allocation of scarce resources, commodities or capital under crisis conditions to minimize the economic consequences. In such cases, it is necessary to account for structural properties of economic systems to ensure that rational distribution policies are implemented. Input–output models are used to illustrate interdependencies among economic sectors and to assess both direct and indirect effects of disruptive events. Alternatively, these interdependencies may be exploited for developing effective recovery efforts to minimize the ripple effects of a crisis. In this paper, a process graph representation of the input–output model is developed to generate a rational procedure for the allocation of scarce resources, commodities or capital during crisis conditions. The process graph model is a graph-theoretic approach originally developed for chemical process design applications. The analogous problem structure allows it to be used for the input–output system. The method is demonstrated through several case studies to identify allocation policies geared towards reducing the impact of disruptions attributed to critical resources, commodities, or capital. Results show that depending on the economic structure, the optimal allocation of scarce resources, commodities or capital will satisfy the final demands of some economic sectors and reduce the production capacity of others in order to minimize the reduction of total gross domestic product. Though similar results can be obtained through traditional mathematical programming models, the process graph platform has the advantage to visually present the distribution of scarce resources, commodities or capital within the system. This work is a first attempt to implement the process graph approach in the fields of economics and climate change adaptation. In conclusion, the process graph based approach developed in this work can be used to provide policymakers with insights in developing appropriate risk mitigation plans associated with climate change-induced crisis conditions. Potential applications include both the development of disaster preparedness measures for anticipated disruptions, as well as the implementation of real-time emergency response in the midst of a crisis.

Implementing new strategies to mitigate the impacts of climate change may influence an economy’s vulnerability to natural disasters. It is thus important to develop mechanisms for evaluating the impact of these changes prior to their implementation. Recent works have demonstrated the effectiveness of inoperability input–output models in assessing the impact of natural disasters on interconnected economic systems. This study develops a multi-criteria framework that measures the vulnerability of economic sectors by considering plausible disaster scenarios and the resulting “ripple effects” of such disruptions. The approach proposed here uses three metrics: average propagation length, economic loss, and inoperability. The model then uses the analytic hierarchy process to measure the importance of each component in a hierarchical framework to derive a composite vulnerability index. The method is used to assess the implications of implementing the mandatory bioethanol blending program in Vietnam, using cassava and sugarcane as bioethanol feedstocks. The disaster scenarios assessed include the incidence of typhoons, floods, and pest infestation. Results show that the cassava, sugarcane, and other manufacturing sectors are the key economic sectors which are most affected by these disasters. Furthermore, sensitivity analysis on different bioethanol blend rates indicates that a 5 % bioethanol blend policy does not significantly affect Vietnam’s economy, while raising the blend to 10 % bioethanol or more may considerably change the country’s economic structure and disaster vulnerability.

Polygeneration systems have been utilized to simultaneously generate a number of energy and utility products such as heat, power, cooling and treated water. Its implementation has proven to increase fuel efficiency and to reduce the associated carbon footprint in products in comparison to stand-alone production systems. The polygeneration system consists of interdependent process units whose design capacities will depend on the expected product demands. Because of the multiple product streams generated and the associated demands, it is necessary to design a system which aims to simultaneously meet potentially conflicting product demand targets. Fuzzy optimization has initially been used to identify the optimal solution which simultaneously satisfices multiple product demand targets. However, real life decision-making may require an evaluation of alternative solutions. This aspect can be addressed by the P-graph methodology which is able to provide both optimal and sub-optimal network designs. This work thus proposes the development of a fuzzy P-graph model for the design of a polygeneration system. The capabilities of the model are demonstrated in a case study. The model results identify both optimal and sub-optimal design options which generate products within the defined demand targets and which can be further evaluated for other parameters such as robustness for final decision-making.

Community-based off-grid polygeneration plants based on micro-hydropower are a practical solution to provide clean energy and other essential utilities for rural areas with access to suitable rivers. Such plants can deliver co-products such as purified water and ice for refrigeration, which can improve standards of living in such remote locations. Although polygeneration gives advantages with respect to system efficiency, the interdependencies of the integrated process units may come as a potential disadvantage, due to susceptibility to cascading failures when one of the system components is partially or completely inoperable. In the case of a micro-hydropower-based polygeneration plant, a drought may reduce electricity output, which can, in turn, reduce the level of utilities available for use by the community. The study proposes a fuzzy mixed-integer linear programming model for the optimal operational adjustment of an off-grid micro-hydropower-based polygeneration plant seeking to maximize the satisfaction levels of the community utility demands, which are represented as fuzzy constraints. Three case studies are considered to demonstrate the developed model. The use of a diesel generator for back-up power is considered as an option to mitigate inoperability during extreme drought conditions.

The advent of combined cooling, heating, and power (CCHP) plants introduces a new field of research and experimentation to optimize the use of such a system. A CCHP plant offers higher flexibility and efficiency, and lower greenhouse gas emissions in comparison to conventional stand-alone power production systems. As such, this paper aims to utilize actual data of power producing units to design and optimize a CCHP plant. A fuzzy mixed integer linear programming (MILP) model is proposed to select the appropriate processes to be deployed given product demand and environmental footprint constraints. The results would aid plant owners in the design of a CCHP plant. The results showed an optimal configuration of the plant consisting of a gas internal-combustion generator, a gas boiler, and a vapor absorption chiller. The environmental footprint limit was seen to be the limiting factor for the proposed optimized model to produce powers near the lower limit of the product demand constraints.

Selection of the best green technology for a given application is a multi-criterion decision analysis problem. The tradeoffs that need to be made among conflicting attributes of the alternatives are inherently subjective and require the use of interactive decision analysis tools. In this work, a machine-learning based methodology is developed for ranking green technologies based on multiple criteria. First, an expert generates a training data set by ranking a subset of the alternative technologies using his/her tacit knowledge and preferences. Then, a machine learning procedure known as logical analysis of data (LAD) is used to detect patterns in these rankings. These patterns are approximations of the mental rules used by the expert to compare and rank the competing alternatives; after validation, they can be used to rank other technologies in the same class as those in the training data. This novel methodology is illustrated here for the case of ranking alternative energy storage technologies for stationary applications. This technique provides a rapid means of eliciting expert knowledge for ranking alternatives based on multiple criteria.

Carbon Emissions Pinch Analysis (CEPA) was first introduced for carbon-constrained energy planning purposes. Since its inception, CEPA has been modified to apply to various systems at different scales, utilized in different geographic contexts, and extended through the use of alternative sustainability metrics or footprints. In this paper, CEPA is further extended to economy-wide systems where the segments of composite curves are comprised of sectors in a national or regional economy. This approach uses input- output analysis (IOA) to calculate carbon footprints of major sectors per unit of economic value; these are then subjected to pinch analysis using established CEPA methodology. The methodology is shown to yield important insights for carbon management by means of a case study using Philippine statistical data.

Enhanced weathering is a negative emissions technology based on the accelerated weathering of alkaline minerals. Such materials can be reduced to a fine powder and applied to land sinks to maximize the area exposed for reaction with rainwater and dissolved CO2. The carbon is captured in the form of bicarbonate ions in the runoff, which ultimately carries it to the ocean for virtually permanent sequestration. Enhanced weathering has been demonstrated in proof-of-concept laboratory and field tests, but scale-up to a level that delivers significant CO2 removal is still an engineering challenge that requires a system-level perspective. Future enhanced weathering networks should be planned like industrial supply chains, taking into account constraints in the supply of alkaline minerals and the availability of land sinks. Optimization of such networks should also take into account techno-economic uncertainties that are inherent in any emerging technology. To fill this research gap, this work develops a fuzzy mixed-integer linear programming model for optimal planning of enhanced weathering networks. The model is capable of handling multiple conflicting objectives and accounting for system uncertainties. The use of the model is illustrated with two case studies. First, a pedagogical example is solved; then, the model is demonstrated for a realistic scenario which shows that 0.69% of Taiwan’s CO2 emissions can be offset by the use of blast furnace slag for enhanced weathering.