Network Modeling for Post-Entry Management of Invasive Pest Species in the Philippines: The Case of the Colorado Potato Beetle, Leptinotarsa decemlineata (Say, 1824) (Coleoptera: Chrysomelidae).

Crop shifting is considered as an important strategy to secure future food supply in the face of climate change. However, use of this adaptation strategy needs to consider the risk posed by changes in the geographic range of pests that feed on selected crops. Failure to account for this threat can lead to disastrous results. Models can be used to give insights on how best to manage these risks. In this paper, the socioecological process graph technique is used to develop a network model of interactions among crops, invasive pests, and biological control agents. The model is applied to a prospective analysis of the potential entry of the Colorado potato beetle into the Philippines just as efforts are being made to scale up potato cultivation as a food security measure. The modeling scenarios indicate the existence of alternative viable pest control strategies based on the use of biological control agents. Insights drawn from the model can be used as the basis to ecologically engineer agricultural systems that are resistant to pests.

Prediction model for biochar energy potential based on biomass properties and pyrolysis conditions derived from rough set machine learning.

Biochar is a high-carbon-content organic compound that has potential applications in the field of energy storage and conversion. It can be produced from a variety of biomass feedstocks such as plant-based, animal-based, and municipal waste at different pyrolysis conditions. However, it is difficult to produce biochar on a large scale if the relationship between the type of biomass, operating conditions, and biochar properties is not understood well. Hence, the use of machine learning-based data analysis is necessary to find the relationship between biochar production parameters and feedstock properties with biochar energy properties. In this work, a rough set-based machine learning (RSML) approach has been applied to generate decision rules and classify biochar properties. The conditional attributes were biomass properties (volatile matter, fixed carbon, ash content, carbon, hydrogen, nitrogen, and oxygen) and pyrolysis conditions (operating temperature, heating rate residence time), while the decision attributes considered were yield, carbon content, and higher heating values. The rules generated were tested against a set of validation data and evaluated for their scientific coherency. Based on the decision rules generated, biomass with ash content of 11-14 wt%, volatile matter of 60-62 wt% and carbon content of 42-45.3 wt% can generate biochar with promising yield, carbon content and higher heating value via a pyrolysis process at an operating temperature of 425°C-475°C. This work provided the optimal biomass feedstock properties and pyrolysis conditions for biochar production with high mass and energy yield.

Towards a low carbon ASEAN: an environmentally extended MRIO optimization model.

BACKGROUND: Economic growth is dependent on economic activity, which often translates to higher levels of carbon emissions. With the emergence of technologies that promote sustainable production, governments are working towards achieving their target economic growth while minimizing environmental emissions to meet their commitments to the international community. The IPCC reports that economic activities associated with electricity and heat production contributed most to GHG emissions and it led to the steady increase in global average temperatures. Currently, more than 90% of the total GHG emissions of the ASEAN region is attributable to Indonesia, Malaysia, the Philippines, Thailand, and Vietnam. These regions are expected to be greatly affected with climate change. This work analyzes how ASEAN nations can achieve carbon reduction targets while aspiring for economic growth rates in consideration of interdependencies between nations. We thus develop a multi-regional input-output model which can either minimize collective or individual carbon emissions. A high-level eight-sector economy is used for analyzing different economic strategies. RESULTS: This model shows that minimizing collective carbon emissions can still yield economic growth. Countries can focus on developing sectors that have potentials for growth and lower carbon intensity as new technologies become available. In the case study examined, results indicate that the services sector, agriculture, and food manufacturing sector have higher potential for economic growth under carbon reduction emission constraints. In addition, the simultaneous implementation of multiple carbon emission reduction strategies provides the largest reduction in regional carbon emissions. CONCLUSIONS: This model provides a more holistic view of how the generation of carbon emissions are influenced by the interdependence of nations. The emissions reduction achieved by each country varied depending on the state of technology and the level of economic development in the different regions. Though the presented case focused on the ASEAN region, the model framework can be used for the analysis of other multi-regional systems at various levels of resolution if data is available. Insights obtained from the model results can be used to help nations identify more appropriate and achievable carbon reduction targets and to develop coordinated and more customized policies to target priority sectors in a country. This model is currently limited by the assumption of fixed technical coefficients in the exchange and interdependence of different regions. Future work can investigate modelling flexible multi-regional trade where regions have the option of substituting goods and products in its import or export structure. Other strategies for reducing carbon emission intensity can also be explored, such as modelling transport mode choices, or establishing sectors for waste management. Hybrid models which integrate the multi-regional input-output linear program model with data envelopment analysis can also be developed.

Industrial water network vulnerability analysis using dynamic inoperability input-output model.

Industrial parks provide opportunities for Process Integration among different enterprises. Inter-Plant Water Network Integration is an effective strategy for water conservation. However, increased interplant linkages can make the entire system vulnerable to cascading failures in case of loss of water flow in some plants. The potential indirect impact of water shortages on such integrated systems may not be evident without the use of appropriate models. This work defines inoperability as the fractional loss of water flow relative to normal operations. A comparison between the applicability of demand-driven versus supply-driven Inoperability Input-output Model (IIM) is conducted. Then, a Vulnerability Assessment Framework which integrates vulnerability indicators into the Dynamic Input-Output Model (DIIM) is developed to analyse failure propagation in water networks in an industrial park. The DIIM is then applied to simulate the cascading effects of disturbances. From a time perspective, the vulnerabilities of the industrial parks With Integrated Optimal Water Network (WWN) and Without Integrated Optimal Water Network (WOWN) are assessed considering robustness, adaptability, and recoverability as the indicators. The results indicate that supply-driven IIM is more suitable for cascading failure analysis of water networks. The average inoperability at 16% from supply-driven IIM is higher than that from demand-driven IIM. In the freshwater disturbance scenario, the dependence of the plant on freshwater is proportional to the rate of inoperability change, the time to reach a new equilibrium. In this study, the robustness of WWN is about fivefold that of WOWN, but the recovery rate is only one-sixth of the latter. This work can help identify system vulnerabilities and provide a scientific insight for the development of park-wide water management strategies.

Modelling vicious networks with P-graph causality maps.

P-graph causality maps were recently proposed as a methodology for systematic analysis of intertwined causal chains forming network-like structures. This approach uses the bipartite representation of P-graph to distinguish system components ("objects" represented by O-type nodes) from the functions they perform ("mechanisms" represented by M-type nodes). The P-graph causality map methodology was originally applied for determining structurally feasible causal networks to enable a desirable outcome to be achieved. In this work, the P-graph causality map methodology is extended to the analysis of vicious networks (i.e., causal networks with adverse outcomes). The maximal structure generation algorithm is first used to assemble the problem elements into a complete causal network; the solution structure generation algorithm is then used to enumerate all structurally feasible causal networks. Such comprehensive analysis gives insights on how to deactivate vicious networks through the removal of keystone objects and mechanisms. The extended methodology is illustrated with an ex post analysis of the 1984 Bhopal industrial disaster. Prospects for other applications to sustainability issues are also discussed.

Forecasting plastic waste generation and interventions for environmental hazard mitigation.

Plastic waste and its environmental hazards have been attracting public attention as a global sustainability issue. This study builds a neural network model to forecast plastic waste generation of the EU-27 in 2030 and evaluates how the interventions could mitigate the adverse impact of plastic waste on the environment. The black-box model is interpreted using SHapley Additive exPlanations (SHAP) for managerial insights. The dependence on predictors (i.e., energy consumption, circular material use rate, economic complexity index, population, and real gross domestic product) and their interactions are discussed. The projected plastic waste generation of the EU-27 is estimated to reach 17 Mt/y in 2030. With an EU targeted recycling rate (55%) in 2030, the environmental impacts would still be higher than in 2018, especially global warming potential and plastic marine pollution. This result highlights the importance of plastic waste reduction, especially for the clustering algorithm-based grouped countries with a high amount of untreated plastic waste per capita. Compared to the other assessed scenarios, Scenario 4 with waste reduction (50% recycling, 47.6% energy recovery, 2.4% landfill) shows the lowest impact in acidification, eutrophication, marine aquatic toxicity, plastic marine pollution, and abiotic depletion. However, the global warming potential (8.78 Gt CO2eq) is higher than that in 2018, while Scenario 3 (55% recycling, 42.6% energy recovery, 2.4% landfill) is better in this aspect than Scenario 4. This comprehensive analysis provides pertinent insights into policy interventions towards environmental hazard mitigation.

Dataset for off-grid micro-hydroelectric community system under drought conditions.

This submission contains the complete balanced process matrix of an off-grid community system primarily powered by a micro-hydroelectric powerplant. The system is meant to provide the needs of the community for electricity, potable water and ice. The system also considers the provision of a diesel engine generator set as a back-up to provide electricity. The data serves as inputs to simulate the performance of the system under different drought scenarios. The data provided here is in support of the co-submitted article of Aviso et al. [1].

Data set and model code on the optimal operating state of a negative emission polygeneration system.

This article contains the data set and model code for the negative emission polygeneration system described in Tan et al. (2019). The data was generated utilizing an optimization model implemented in LINGO 18.0 and includes information on the operating state of each process unit in the system. The maximum annual profit of the system was determined at different carbon footprint targets. The data set and model code can be utilized for further analysis on the interdependence between the process units of this polygeneration system, its operational and environmental performance, and the potential impact of integrating new process units into the network.

An AHP-based evaluation method for teacher training workshop on information and communication technology.

The emergence of information and communication technology (ICT) has created opportunities for enhancing the learning process at different educational levels. However, its potential benefits can only be fully realized if teachers are properly trained to utilize such tools. The rapid evolution of ICT also necessitates rigorous assessment of training programs by participants. Thus, this study proposes an evaluation framework based on the Analytic Hierarchy Process (AHP) to systematically evaluate such workshops designed for teachers. The evaluation model is decomposed hierarchically into four main criteria namely: (1) workshop design, (2) quality of content of the workshop, (3) quality of delivery of the content of the workshop, and the (4) relevance of the workshop. These criteria are further disaggregated into 24 sub-indicators to measure the effectiveness of the workshop as perceived by the participants based on their own expectations. This framework is applied to a case study of ICT workshops done in the Philippines. In this case, relevance of the workshop is found to be the most important main criterion identified by the participants, particularly on the new ICT knowledge that promotes teachers' professional growth and development. The workshop evaluation index (WEI) is also proposed as a metric to support decision-making by providing a mechanism for benchmarking performance, tracking improvement over time, and developing strategies for the design and improvement of training programs or workshops on ICT for teachers.