Search Results (17)

Natural gas (NG) is a key transitional energy source for clean energy transition. Against the backdrop of a grim climate change situation, the sustainable development of the Chinese NG industry is emphasized. Climate change disclosure (CCD) has become an important way for corporations to fulfill their social responsibility and demonstrate their capacity for sustainable development. In order to understand the current status of CCD in the Chinese NG industry and to improve the deficiencies, this paper assesses the quality of CCD in the Chinese NG industry. Climate change information is not fully covered by the existing quality evaluation systems. This study establishes a highly applicable system for evaluating the quality of CCD based on the theory pillar perspective. It includes the following five dimensions: completeness, balance, reliability, comparability, and understandability. This study evaluates the quality of CCD of 58 NG corporations using content analysis and quality evaluation index methods, incorporating Skip-Gram and CRITIC models. The evaluation results indicate that the quality of climate reports in the Chinese NG industry has shown general improvement over time; however, inconsistencies remain, making comparisons challenging. There are differences in the level of quality of CCD in the Chinese NG industry. Policy incentives with clear guidance and regional economic development conditions have a notable impact on the quality of CCD. For Chinese NG corporations themselves, disclosing climate change information related to risk management is the focus of narrowing the reporting gap. The CCD quality evaluation system constructed in this paper provides a theoretical reference for all industries to accurately promote disclosure quality. It also provides practical guidelines for corporations to identify weak links in CCD. Full article

Interest in hydrogen has grown as a means to decarbonize future energy systems. To maximize hydrogen’s potential as the main energy carrier, the infrastructure for hydrogen production, distribution, and storage needs to be designed and developed at a global scale. Carbon footprint analysis is an important metric for ensuring that the environmental impact of the developed plant is kept at a minimum. However, application of conventional methods during the conceptual design stage is challenging due to lack of detailed process data coupled with the large number of potential designs to be vetted. As a result, there is a need to develop rapid screening techniques that can be used during the conceptual design stage to gauge potential carbon footprints. To address this issue, a simplified carbon footprint analysis method is proposed in this work. Two indices are introduced, i.e., “product carbon intensity” and “economic carbon intensity”, to allow comprehensive analysis of the performance of design alternatives. By limiting the scope and basic economic analysis, the simplified carbon footprint analysis requires less data, and hence expedite the analysis process. The methodology is demonstrated through analysis of four design scenarios for blue hydrogen production. Among the scenarios, hydrogen production with both carbon capture and pre-reforming yielded better results based on product carbon intensity (2.43 kg CO₂/kg H₂), while design with only carbon capture performed better based on economic carbon intensity (11.25 kg CO₂/USD). Thus, high potential design scenarios were successfully identified based on the newly introduced indices. Full article

Developing a chemical reaction network is considered the first and most crucial step of process synthesis. Many methods have been employed for process synthesis, such as the attainable region (AR) theory. AR states that a region of all possible configurations can be defined with all the potential products and reactants. The second method is process network synthesis (PNS), a technique used to optimise a flowsheet based on the feasible materials and energy flow. P-graph is an algorithmic framework for PNS problems. P-graph attainable region technique (PART) is introduced here as an integration of both AR and P-graph to generate optimal reaction pathways for a given process. A descriptive AR plot is also developed to represent all the possible solution structures or reaction pathways. A case study of a conventional nitric acid synthesis process was used to demonstrate this technique. Full article

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. Full article

The deployment of CO₂ capture and storage (CCS) and negative emissions technologies (NETs) are crucial to meeting the net-zero emissions target by the year 2050, as emphasised by the Glasgow Climate Pact. Over the years, several energy planning models have been developed to address the temporal aspects of carbon management. However, limited works have incorporated CCS and NETs for bottom-up energy planning at the individual plant scale, which is considered in this work. The novel formulation is implemented in an open-source energy system software that has been developed in this work for optimal decarbonisation planning. The DECarbonation Options Optimisation (DECO2) software considers multiperiod energy planning with a superstructural model and was developed in Python with an integrated user interface in Microsoft Excel. The software application is demonstrated with two scenarios that differ in terms of the availabilities of mitigation technologies. For the more conservative Scenario 1, in which CCS is only available in later years, and other NETs are assumed not to be available, all coal plants were replaced with biomass. Meanwhile, only 38% of natural gas plants are CCS retrofitted. The remaining natural gas plants are replaced with biogas. For the more aggressive Scenario 2, which includes all mitigation technologies, once again, all coal plants undergo fuel substitution. However, close to half of the natural gas plants are CCS retrofitted. The results demonstrated the potential of fuel substitutions for low-carbon alternatives in existing coal and natural gas power plants. Additionally, once NETs are mature and are available for commercial deployment, their deployment is crucial in aiding CO₂ removal in minimal investment costs scenarios. However, the results indicate that the deployment of energy-producing NETs (EP-NETs), e.g., biochar and biomass with CCS, are far more beneficial in CO₂ removal versus energy-consuming NETs (EC-NETs), e.g., enhanced weathering. The newly developed open-source software demonstrates the importance of determining the optimal deployment of mitigation technologies in meeting climate change targets for each period, as well as driving the achievement of net-zero emissions by mid-century. Full article

The demand for new novel flavour and fragrance (F&F) molecules has boosted the need for a systematic approach to designing fragrance molecules. However, the F&F-related industry still relies heavily on experimental approaches or on existing databases without considering the consequences resulting from changes in concentration, which could omit potential fragrances. Computer-aided molecular design (CAMD) has great potential to identify novel molecular structures to be used as fragrances. Using CAMD for this purpose requires models to predict the olfaction properties of molecules. A rough set-based machine learning (RSML) approach is used to develop an interpretable predictive model for odour characteristics in this work. New rule-based models are generated from RSML based on the dilution and a number of different topological indices which identify the structure-odour relationship of fragrance molecules. The most prominent rules are selected and formulated as constraints in a CAMD optimisation model. The combination of several rules was able to increase the coverage of different classes of molecules. To model the performance indicators that vary over a range of properties, a disjunctive programming model is also incorporated into the CAMD framework. A case study demonstrates the utilisation of this methodology to design fragrance additives in dishwashing liquid. The results illustrate the capability of the novel RSML and CAMD framework to identify potential fragrance molecules that can be used in consumer products. Full article

In this study, we utilize an input–output (I–O) model to perform an ex-post analysis of the COVID-19 pandemic workforce disruptions in the Philippines. Unlike most disasters that debilitate physical infrastructure systems, the impact of disease pandemics like COVID-19 is mostly concentrated on the workforce. Workforce availability was adversely affected by lockdowns as well as by actual illness. The approach in this paper is to use Philippine I–O data for multiple years and generate Dirichlet probability distributions for the Leontief requirements matrix (i.e., the normalized sectoral transactions matrix) to address uncertainties in the parameters. Then, we estimated the workforce dependency ratio based on a literature survey and then computed the resilience index in each economic sector. For example, sectors that depend heavily on the physical presence of their workforce (e.g., construction, agriculture, manufacturing) incur more opportunity losses compared to sectors where workforce can telework (e.g., online retail, education, business process outsourcing). Our study estimated the 50th percentile economic losses in the range of PhP 3.3 trillion (with telework) to PhP 4.8 trillion (without telework), which is consistent with independently published reports. The study provides insights into the direct and indirect economic impacts of workforce disruptions in emerging economies and will contribute to the general domain of disaster risk management. Full article

Reduction of CO₂ emissions from industrial facilities is of utmost importance for sustainable development. Novel process systems with the capability to remove CO₂ will be useful for carbon management in the future. It is well-known that major determinants of performance in process systems are established during the design stage. Thus, it is important to employ a systematic tool for process synthesis. This work approaches the design of polygeneration plants with negative emission technologies (NETs) by means of the graph-theoretic approach known as the P-graph framework. As a case study, a polygeneration plant is synthesized for multiperiod operations. Optimal and alternative near-optimal designs in terms of profit are identified, and the influence of network structure on CO₂ emissions is assessed for five scenarios. The integration of NETs is considered during synthesis to further reduce carbon footprint. For the scenario without constraint on CO₂ emissions, 200 structures with profit differences up to 1.5% compared to the optimal design were generated. The best structures and some alternative designs are evaluated and compared for each case. Alternative solutions prove to have additional practical features that can make them more desirable than the nominal optimum, thus demonstrating the benefits of the analysis of near-optimal solutions in process design. Full article

The COVID-19 pandemic has forced governments around the world to implement unprecedented lockdowns, mandating businesses to shut down for extended periods of time. Previous studies have modeled the impact of disruptions to the economy at static and dynamic settings. This study develops a model to fulfil the need to account for the sustained disruption resulting from the extended shutdown of business operations. Using a persistent inoperability input-output model (PIIM), we are able to show that (1) sectors that suffer higher levels of inoperability during quarantine period may recover faster depending on their resilience; (2) initially unaffected sectors can suffer inoperability levels higher than directly affected sectors over time; (3) the economic impact on other regions not under lockdown is also significant. Full article

Machine learning tools can be applied to peptide-mediated biomineralization, which is an emerging biomimetic technique of creating functional nanomaterials. In particular, they can be used for the discovery of biomineralization peptides, which currently relies on combinatorial enumeration approaches. In this work, an enhanced hyperbox classifier is developed which can predict if a given peptide sequence has a strong or weak binding affinity towards a gold surface. A mixed-integer linear program is formulated to generate the rule-based classification model. The classifier is optimized to account for false positives and false negatives, and clearly articulates how the classification decision is made. This feature makes the decision-making process transparent, and the results easy to interpret for decision support. The method developed can help accelerate the discovery of more biomineralization peptide sequences, which may expand the utility of peptide-mediated biomineralization as a means for nanomaterial synthesis. Full article

The production of primary aluminum is an energy-intensive industry which produces large amounts of direct and indirect greenhouse gas emissions, especially from electricity consumption. Carbon Emissions Constrained Energy Planning proved to be an efficient tool for reducing energy-related greenhouse gas emissions. This study focuses on energy planning constrained by CO₂ emissions and determines the required amount of CO₂ emissions from electricity sources in order to meet specified CO₂ emission benchmark. The study is demonstrated on and applied to specific aluminum products, aluminum slugs and aluminum evaporator panels. Three different approaches of energy planning are considered: (i) an insight-based, graphical targeting approach, (ii) an algebraic targeting approach of cascade analysis, and (iii) an optimization-based approach, using a transportation model. The results of the three approaches show that approximately 2.15 MWh of fossil energy source should be replaced with a zero-carbon or 2.22 MWh with a low-carbon energy source to satisfy the benchmark of CO₂ emissions to produce 1 t of aluminum slug; however, this substitution results in higher costs. This study is the first of its kind demonstrated on and applied to specific aluminum products, and represents a step forward in the development of more sustainable practices in this field. Full article

To achieve sustainability, the circular economy (CE) concept is challenging traditional linear enterprise models due to the need to manage geographically distributed product life cycle and value chains. Concurrently, Industry 4.0 is being used to bring productivity to higher levels by reducing waste and improving the efficiency of production processes via more precise real-time planning. There is significant potential to combine these two frameworks to enhance the sustainability of manufacturing sectors. This paper discusses the fundamental concepts of Industry 4.0 and explores the influential factors of Industry 4.0 that accelerate the sharing economy in the CE context via a case of electric scooters in Taiwan. The result shows Industry 4.0 can provide an enabling framework for the sharing economy in CE implementation. Full article

In photovoltaic and wind power storage systems, the reliability of the battery directly affects the overall reliability of the energy storage system. Failed batteries can seriously affect the stable operation of energy storage systems. This paper aims to improve the reliability of the storage systems by accurately predicting battery life and identifying failing batteries in time. The current prediction models mainly use artificial neural networks, Gaussian process regression and hybrid models. Although these models can achieve high prediction accuracy, the computational cost is high due to model complexity. Least squares support vector machine (LSSVM) is a computationally efficient alternative. Hence, this study combines the improved chicken swarm optimization algorithm (ICSO) and LSSVM into a hybrid ICSO-LSSVM model for the reliability of photovoltaic and wind power storage systems. The following are the contributions of this work. First, the optimal penalty parameter and kernel width are determined. Second, the chicken swarm optimization algorithm (CSO) is improved by introducing chaotic search behavior in the hen and an adaptive learning factor in the chicks. The performance of the ICSO algorithm is shown to be better than CSO using standard test problems. Third, the prediction accuracy of the three models is compared. For NMC1 battery, the predicted relative error of ICSO-LSSVM is 0.94%; for NMC2 battery, the relative error of ICSO-LSSVM is 1%. These findings show that the proposed model is suitable for predicting the failure of batteries in energy storage systems, which can improve preventive and predictive maintenance of such systems. Full article

This study aims to identify the causal attributes of sustainable agritourism in Thailand. Agritourism is a systematic approach based on farm diversification for tourism purposes. Agritourism is usually assessed with qualitative information. However, the assessment of agritourism attributes involves considering the interrelationships among the attributes. Prior studies on sustainable agritourism do not identify and address interrelated attributes using qualitative information. This study applies the Delphi method to identify a set of valid attributes. Moreover, this study applies triangular fuzzy numbers to transform the qualitative information into comparable values and uses a decision-making trial and evaluation laboratory to identify the interrelationships among the attributes in the causal model. The results show that sustainable business performance and rural economic conditions are the key drivers of environmental sustainability. This result suggests that the attributes that may potentially stimulate sustainable agritourism are the development and implementation of an agritourism-specific plan, the development of a local business value chain, and government-led tourism promotion. Full article

The growth in energy demand, coupled with declining fossil fuel resources and the onset of climate change, has resulted in increased interest in renewable energy, particularly from biomass. Co-firing, which is the joint use of coal and biomass to generate electricity, is seen to be a practical immediate solution for reducing coal use and the associated emissions. However, biomass is difficult to manage because of its seasonal availability and variable quality. This study proposes a biomass co-firing supply chain optimization model that simultaneously minimizes costs and environmental emissions through goal programming. The economic costs considered include retrofitting investment costs, together with fuel, transport, and processing costs, while environmental emissions may come from transport, treatment, and combustion activities. This model incorporates the consideration of feedstock quality and its impact on storage, transportation, and pre-treatment requirements, as well as conversion yield and equipment efficiency. These considerations are shown to be important drivers of network decisions, emphasizing the importance of managing biomass and coal blend ratios to ensure that acceptable fuel properties are obtained. Full article