Minimize Greenhouse Gas Emissions Research Articles

Optimizing smart grid performance: A stochastic approach to renewable energy integration

Smart grids offer numerous possibilities for developing and executing sustainable and efficient electricity supply chains that exhibit increased resilience to disturbances. The exploration of the Electric Supply Chain Network (ESCND) design problem using smart grids has been limited. This article aims to tackle this challenge through the application of a robust multi-objective optimization approach, encompassing three economic goals (maximizing profit), environmental goals (minimizing greenhouse gas emissions), and resilience goals (maximizing network resilience). Additionally, the optimization process takes into account the dual objectives of maximizing efficiency and minimizing energy loss by incorporating relevant cost considerations. The proposed methodology incorporates distinctive features of smart grids, such as demandside management programs, microgrid structures, bidirectional distribution lines, and consumer–supplier nodes. It addresses various interconnected decisions, including location placement, capacity expansion, load allocation, and pricing. To solve the formulated model, a potent combination of multi-objective optimization methods based on cutting-plane algorithms and AUGMECON2 is introduced. Subsequently, the proposed model and solution approach are applied to a practical case study, and the obtained results are comprehensively examined. The findings suggest that, despite potential contradictions between economic and environmental objectives, the implementation of smart grids can concurrently improve environmental performance and network flexibility.

Sustainability of biochar amendment in a tropical paddy soil

Biochar use as a soil amendment has attracted increased attention from researchers worldwide but its effects on soil properties, nutrients and greenhouse gas emissions remain scanty especially for tropical paddy soils. Biochar amendment increased initial total P from 500.11 in soil (S) to 978.90 µg/g of soil in soil amended with biochar (SB) but significantly reduced mean DOC. Biochar, however, had no effect on total nitrogen, total organic carbon, TON, organic–N and ammoniacal N. The initial soil pH was raised from 3.96 in S to 4.84 in SB by biochar amendment. It reduced CO2 emission, had no significant effect (P≤0.05) on N2O emissions but increased CH4 emissions, although the levels of CH4 were very low compared to the other greenhouse gases hence the high CO2/CH4 ratios. In addition, when co-applied with P, it suppressed the stimulating effect of P on production of greenhouse gases. Significant positive correlations were observed between CO2 and N2O as well as N2O and CO2 in SP. pH showed significant positive correlations with CH4 in S and SP, organic–N in S, SP and SPB but negatively correlated to CO2 in SPB as well as TON in SB. S and SP registered a significant negative correlation between ammoniacal–N and CH4. DOC positively correlated to TON and ammoniacal N but negatively correlated to organic-N in SP and SB. Total P positively correlated to DOC in SB, ammoniacal N in SP and SPB and TON in S. It however, negatively correlated to CH4 in SP and N2O in SPB. It is therefore a suitable additive for sustainable agriculture since it enhances soil fertility and minimizes greenhouse gas emissions, especially when combined with P fertilizer.

The economics of public transport electrification: The charging dilemma

The electrification of public transport has the potential to mitigate greenhouse gas emissions and support the electricity system, however this potential depends on how intraday charging interfaces with grid emissions and wholesale prices. Our paper explores these trade-offs by using boundary analysis to evaluate a least-cost tariff-based aggregate bus fleet charging strategy and comparing it to charging strategies that either minimise indirect greenhouse gas emissions or wholesale price exposure, based on the metropolitan Melbourne bus network. We develop a novel two-state fleet charging optimisation model, that allows us to perform a counterfactual analysis across 5 years of historical (2017–2021) and 5 years of modelled (2022–2026) changes to wholesale electricity prices and emission intensities in Australia's National Electricity Market, in response to the three charging strategies. We find that current Australian commercial tariff structures with high daytime volumetric usage and demand charges mute climate and wholesale market signals. By charging to minimise greenhouse gas emissions over bill savings, the fleet can achieve (on average) a further 33.4% reduction in indirect emissions but incurs a 103% increase in annual electricity bills. Alternatively, by charging to minimise wholesale price exposure, the fleet can achieve (on average) a 13.4% reduction in indirect emissions while incurring a 69% increase in electricity bills. Improving the temporal alignment of commercial electricity tariffs with wholesale prices should thus simultaneously reduce indirect emissions and wholesale electricity costs. This necessitates urgent tariff reforms to allow relevant wholesale price signals to pass through to commercial EV operators.

Transition towards sustainable diets: Multi-objective optimization of dietary pattern in China

Global diversity in food cultures has led to a variety of dietary patterns, the evolution of which can significantly impact greenhouse gas (GHG) emissions. To address the dual challenge of climate change and malnutrition, we need to examine feasible pathways towards sustainable diets that balance nutritional adequacy, environmental impact, and cultural acceptability. We propose an optimized dietary transition pathway in China, which aligns with the nation's dietary guidelines (DGs) while aiming to reduce diet-associated GHG emissions and maintain the cultural food preferences. We also examine how the current dietary patterns will change under three different scenarios: 1) meeting nutritional needs (N), minimizing GHG emissions while maintaining nutritional standards (NE), and sustaining cultural acceptance and maintain GHG emissions at a low level while ensuring standard nutrient intake (NEC). We analyze data from 31 provinces in China between 2000 and 2020 to explore sustainable dietary patterns. In this period, plant-based food consumption has experienced a substantial 12 % decrease, in contrast to the 68 % increase in animal-sourced food intake. Meanwhile, diet-associated GHG emissions in China have increased by 17 %. However, dietary nutrition remains below national standards. The diet-associated GHG emission under NEC is 63.43% lower than N and is 7.04% higher than NE, while NEC in cultural unacceptability is 28.39% lower than NE. Our optimized dietary patterns demonstrate the potential to achieve the national nutrition standard, significantly cut GHG emissions, and retain higher cultural acceptability. Acknowledging the regional disparities in sustainable dietary pathways, we propose the implementation of customized provincial directives aimed at fostering compliance with these favorable trends. Our study offers critical policy implications towards achieving a sustainable diet in China, which contributes to managing the complex trade-offs among nutrition, cultural preferences, and environmental impact.

Simulation of a catalytic deoxygenation reaction of fatty acids to produce biohydrocarbons and evaluation of the influence of reaction parameters on the conversion and selectivity of products

Nowadays, there has been an urgency in the search for sustainable energy sources, such as those derived from biomass. This search is not only limited to minimizing greenhouse gas emissions but is also related to the increasing scarcity of fossil fuels. This work aims to investigate and analyze the production of bio-hydrocarbons from fatty acids through the simulation of the deoxygenation reaction by the DWSim software. In this simulation, a continuous catalytic deoxygenation reactor of the stirred tank with fluidized bed type was selected, aiming to understand the influence of operating parameters on this reaction. The parameters evaluated were temperature, pressure, hydrogen feed, and catalyst mass. For this simulation, hydrogen and stearic acid were chosen as feedstocks, focusing on the heptadecane and octadecane production. The rate law was chosen based on an extensive literature review, as well as reaction conditions. The kinetic model used as a reference was based on reactions of stearic acid and hydrogen in the presence of NiMo/Al2O3 as a catalyst. A stearic acid feed of 2.4 x 104 m3 year-1 was used as a base, representing the average feed of fatty raw material practiced in biodiesel plants located in the state of Minas Gerais, Brazil. From the variation of these parameters for a sensitivity analysis, with conversion as the key response, the following optimal operating conditions were defined: temperature of 650 K, pressure of 50 bar, molecular hydrogen feed of 4.1 x 10-3 m³ s-1, and catalyst mass of 8 kg. Under these new circumstances, the conversion of stearic acid reached a value of 99.23 %. The simulation of this reaction allowed us to understand the effect of the main reaction parameters on the reaction products, proved to be an important and robust tool to be used in projects and feasibility analyses of plants and biorefineries.

Circular food system approaches can support current European protein intake levels while reducing land use and greenhouse gas emissions

Protein transition and circular food system transition are two proposed strategies for supporting food system sustainability. Here we model animal-sourced protein to plant-sourced protein ratios within a European circular food system, finding that maintaining the current animal–plant protein share while redesigning the system with circular principles resulted in the largest relative reduction of 44% in land use and 70% in greenhouse gas (GHG) emissions compared with the current food system. Shifting from a 60:40 to a 40:60 ratio of animal-sourced proteins to plant-sourced proteins yielded a 60% reduction in land use and an 81% GHG emission reduction, while supporting nutritionally adequate diets. Differences between current and recommended total protein intake did not substantially impact minimal land use and GHG emissions. Micronutrient inadequacies occurred with less than 18 g animal protein per capita per day. Redesigning the food system varied depending on whether land use or GHG emissions were reduced—highlighting the need for a food system approach when designing policies to enhance human and planetary health.

Conversion of urban lignocellulosic waste into source of bioenergy through hydrothermal carbonization

The hydrothermal carbonization process has attracted the attention of the academic and scientific community as an emerging and ecological technology for the transformation of urban lignocellulosic waste into a source of bioenergy in solid form (hydrochar),liquid, and gases. This review addresses understanding the properties and potential use of these wastes as a source of bioenergy. The physicochemical and energetic properties of these wastes are analyzed, as well as the operating parameters that influencetheir transformation. Furthermore, new perspectives are presented on future challenges related to the potential use of this waste and the carbonization process. The sustainable conversion of these urban wastes into a source of bioenergy will contribute toreducing dependence on fossil fuels, minimizing greenhouse gas emissions, ensuring efficient urban waste management, and developing a circular bioeconomy.

Synergistic redox reactions toward co‐production of H2O2 and value‐added chemicals: Dual‐functional photocatalysis to achieving sustainability

AbstractIntegrating H2O2 evolution with oxidative organic synthesis in a semiconductor‐driven photoredox reaction is highly attractive since H2O2 and high‐value chemicals can be concurrently produced using solar light as the only energy input. The dual‐functional photocatalytic approach, free from sacrificial agents, enables simultaneous production of H2O2 and high‐value organic chemicals. This strategy promises a green and sustainable organic synthesis with minimal greenhouse gas emissions. In this review, we first elucidate the fundamental principles of cooperative photoredox integration of H2O2 synthesis and selective organic oxidation with simultaneous utilization of photoexcited electrons and holes over semiconductor‐based photocatalysts. Afterwards, a thorough review on the recent advancements of cooperative photoredox synthesis of H2O2 and value‐added chemicals is presented. Notably, in‐depth discussions and insights into the techniques for unravelling the photoredox reaction mechanisms are elucidated. Finally, critical challenges and prospects in this thriving field are comprehensively discussed. It is envisioned that this review will serve as a pivotal guidance on the rational design of such dual‐functional photocatalytic system, thereby further stimulating the development of economical and environmentally benign H2O2 and high‐value chemicals production.

A Comparison of Greenhouse Gas Emission Patterns in Different Water Levels in Peatlands

Peatlands store large amounts of carbon in wetland ecosystems. The hydrological conditions within peatlands are important factors that affect the biochemical cycle and patterns of greenhouse gas emissions in these peatlands. This study was carried out in Changbai Mountain Jinchuan peatland to investigate variations in carbon dioxide and methane emissions in peat swamps that have undergone distinct saturation conditions. Three peatland types (high water levels (S1); medium water levels (S2); low water levels (S3)) at different flood depths were selected as specific sampling points. The static box and gas chromatography methods were used at different time periods (6:00; 12:00; and 18:00) from July to September. The discharge flux of CO2 and CH4 slowly increased with the increase in the water level. The results indicate similarity in the fluctuation trends between the fluxes of CO2 and CH4 in S1 and S2 to the fluctuations of water levels. During the entire growth season, the flux range of CO2 and CH4 was −695.329~859.907 mg m2h−1 and 259.981~147.155 mg m2h−1, respectively. Furthermore, there was variation in mutation characteristics between two gases, the CO2 exhibited larger mutation range (−7.08~3.40) than CH4 (−1.79~1.26). In terms of daily flux changes, CO2 showed an upward trend, while CH4 had a downward trend. These results indicate variations in saturation conditions tend to affect discharge of greenhouse gases, with subsequent effects on climate change. This study highlights potential theoretical support to reduce anthropogenic activities on peatlands. This can be achieved by undertaking measures to conserve peatlands and explore mitigation measures to minimize greenhouse gas emissions and hence impacts of climate change.

A COMPREHENSIVE REVIEW ON AI-DRIVEN OPTIMIZATION TECHNIQUES ENHANCING SUSTAINABILITY IN OIL AND GAS PRODUCTION PROCESSES

The oil and gas industry plays a pivotal role in global energy supply but faces increasing pressure to enhance sustainability amidst environmental concerns and economic constraints. This comprehensive review explores the integration of artificial intelligence (AI) in optimizing oil and gas production processes to achieve sustainability goals. The paper examines various AI-driven optimization techniques, including machine learning algorithms, genetic algorithms, and neural networks, and their application in different stages of oil and gas production, such as exploration, drilling, production, and distribution. By leveraging AI, operators can improve efficiency, reduce environmental impact, and maximize resource recovery. Furthermore, the review delves into specific case studies and implementations of AI-driven optimization in real-world oil and gas operations, highlighting their efficacy in minimizing greenhouse gas emissions, optimizing water usage, and mitigating operational risks. Additionally, the paper discusses challenges and limitations associated with AI adoption in the industry, such as data availability, model interpretability, and regulatory compliance. The integration of AI-driven optimization techniques not only enhances sustainability but also contributes to cost reduction and operational excellence in oil and gas production. By optimizing production processes, operators can achieve higher yields with fewer resources, leading to increased profitability and long-term viability in a rapidly evolving energy landscape. Overall, this review provides valuable insights into the transformative potential of AI-driven optimization techniques in fostering sustainability and resilience in oil and gas production processes, paving the way for a more efficient and environmentally responsible industry.
 Keywords: AL, Oil and Gas, Production, Optimization, Sustainability, Review, Process.

CLIMATE CHANGE MITIGATION STRATEGIES IN THE OIL & GAS SECTOR: A REVIEW OF PRACTICES AND IMPACT

Climate change mitigation has become a pressing global challenge, with the oil and gas sector being a significant contributor to greenhouse gas emissions. This review examines climate change mitigation strategies within the oil and gas industry, focusing on practices and their impact. The oil and gas industry faces increasing pressure to reduce its carbon footprint and transition towards cleaner energy sources. Various mitigation strategies have been implemented, including technological innovations, operational improvements, and investments in renewable energy. These strategies aim to minimize greenhouse gas emissions, improve energy efficiency, and transition towards low-carbon alternatives. Technological innovations play a crucial role in mitigating climate change within the oil and gas sector. Advanced drilling techniques, such as horizontal drilling and hydraulic fracturing, have enabled companies to extract oil and gas more efficiently, reducing emissions per unit of production. Additionally, carbon capture, utilization, and storage (CCUS) technologies have been developed to capture and store carbon dioxide emissions from oil and gas operations, preventing them from entering the atmosphere. Operational improvements, such as reducing methane leaks and flaring, are also essential for climate change mitigation in the oil and gas industry. Methane is a potent greenhouse gas, and minimizing its release during extraction, processing, and transportation is critical for reducing the sector's overall emissions. Companies have implemented measures such as leak detection and repair programs, improved equipment maintenance, and enhanced monitoring systems to mitigate methane emissions effectively. Furthermore, investments in renewable energy and low-carbon technologies are increasingly common among oil and gas companies. Many companies are diversifying their portfolios to include solar, wind, and bioenergy projects, aiming to reduce their reliance on fossil fuels and contribute to the transition to a low-carbon economy. In conclusion, climate change mitigation strategies in the oil and gas sector encompass a range of technological, operational, and investment initiatives aimed at reducing greenhouse gas emissions and transitioning towards cleaner energy sources. While progress has been made, ongoing efforts and collaboration between industry stakeholders, governments, and civil society are essential to achieve meaningful impact in mitigating climate change.
 Keywords: Climate Change, Mitigation, Strategies, Oil and Gas Sector, Practices and Impact.

Optimization of a three-stage cascade refrigeration system operating with natural refrigerants to produce low temperatures by applying a bio-inspired method

With the growing global energy consumption, on the first hand, and the need to produce low temperatures on the second hand, therefore the technological challenges consist in minimizing greenhouse gas emissions by reducing the energy consumption. In fact, in refrigeration, this consists of the use of environmentally friendly refrigerants (GWP < 150) with the optimization of cold production processes. In this context, several optimization methods have been tested, and the objective of this work to seek the most reliable method to optimize a three-stage cascade vapor compression refrigeration system. Regarding refrigerants, hydrocarbons can be a solution, the R50/R1150/R290 and R50/R1150/R1270 groups are selected and used here to produce temperatures ranging from −160 °C to −120 °C. After comparing seven (7) nature-inspired optimization methods, the JSOA bio-inspired optimization method is applied. Energetic, exergetic and environmental analyzes are also applied to determine the optimal operating point that allows both the minimization of energy consumption and TEWI (Total Equivalent Warming Impact). A pragmatic comparison between the two considered groups provides insights into the choice of high-temperature sub-cycle fluid, and it is found that R290 shows better results compared to R1270.

Grid connected solar panel with battery energy storage system

A grid-connected battery energy storage system (BESS) is a crucial component in modern electrical grids that enables efficient management of electricity supply and demand. BESS consists of a set of batteries connected to the power grid, allowing for the storage and release of electricity when needed. This paper addresses the challenges associated with intermittent renewable energy sources and enhancing grid stability and reliability. The primary objective of this work is to store surplus electricity during low demand and supply it to the grid during peak demand periods or when renewable energy generation is low. By storing surplus energy, BESS helps balance supply and demand fluctuations, reducing the need for expensive fossil fuel-based power plants and minimizing greenhouse gas emissions. Additionally, BESS provides frequency regulation, voltage support, and grid stabilization. Furthermore, BESS reduces the intermittency of renewable energy sources like solar and wind, allowing for its integration into the grid. It allows the captured energy to be stored and utilized when the renewable sources are not actively generating electricity. Grid-connected BESS are a vital component in the transition towards a more sustainable and resilient energy future. They facilitate the effective utilization of renewable energy, enhance grid flexibility, and contribute to the reduction of carbon emissions, ultimately promoting a cleaner and more reliable electricity supply. The simulation of grid connected solar system with BESS is carried out using MATLAB/Simulink environment.

Promoting sustainable policy in construction: Reducing greenhouse gas emissions through performance-variation based contract clauses

With the growing global concern about construction industry emissions, a potentially useful approach to reduce such emissions could be to obligate the contractual parties to establish emissions-related construction contract provisions. Unfortunately, implementation of such initiatives has been limited in the practice. This study is motivated by the realization from previous studies that it is feasible to use contract clauses to regulate contractor use of equipment in a manner that reduces emissions and that there exist opportunities to establish frameworks that could facilitate implementation. In this paper, a framework is developed for designing and evaluating a green-performance variation clause. The framework involves a model whose objective is to maximize the net present value (NPV) by minimizing greenhouse gas emissions from the contractor's equipment fleet. The model addresses the uncertainties associated with the analysis inputs using Monte Carlo simulation, and the model outcomes are the probabilities of a positive NPV of emissions associated with the Owner and the contractor separately. The framework is demonstrated using a case study in road construction. The case study results yielded the following probabilities that a positive NPV for each of the two parties: 52% and 70% for the Owner and the contractor, respectively. This result suggests that using the developed framework, contract clauses can be designed at the pre-procurement phase to promote mutual and voluntary adoption of emissions-reducing practices at the subsequent phase (construction) of road development. This is shown to yield outcomes that are attractive (thereby, encouraging obligated parties to pursue emissions reduction) and the attractiveness is consistent across the parties. The proposed framework, which is consistent with green contracting, can generally help promote sustainability-related goals and outcomes related to construction projects.

Daily natural gas load prediction method based on APSO optimization and Attention-BiLSTM.

As the economy continues to develop and technology advances, there is an increasing societal need for an environmentally friendly ecosystem. Consequently, natural gas, known for its minimal greenhouse gas emissions, has been widely adopted as a clean energy alternative. The accurate prediction of short-term natural gas demand poses a significant challenge within this context, as precise forecasts have important implications for gas dispatch and pipeline safety. The incorporation of intelligent algorithms into prediction methodologies has resulted in notable progress in recent times. Nevertheless, certain limitations persist. However, there exist certain limitations, including the tendency to easily fall into local optimization and inadequate search capability. To address the challenge of accurately predicting daily natural gas loads, we propose a novel methodology that integrates the adaptive particle swarm optimization algorithm, attention mechanism, and bidirectional long short-term memory (BiLSTM) neural networks. The initial step involves utilizing the BiLSTM network to conduct bidirectional data learning. Following this, the attention mechanism is employed to calculate the weights of the hidden layer in the BiLSTM, with a specific focus on weight distribution. Lastly, the adaptive particle swarm optimization algorithm is utilized to comprehensively optimize and design the network structure, initial learning rate, and learning rounds of the BiLSTM network model, thereby enhancing the accuracy of the model. The findings revealed that the combined model achieved a mean absolute percentage error (MAPE) of 0.90% and a coefficient of determination (R2) of 0.99. These results surpassed those of the other comparative models, demonstrating superior prediction accuracy, as well as exhibiting favorable generalization and prediction stability.

Comparative Analysis between Intelligent Machine Committees and Hybrid Deep Learning with Genetic Algorithms in Energy Sector Forecasting: A Case Study on Electricity Price and Wind Speed in the Brazilian Market

Global environmental impacts such as climate change require behavior from society that aims to minimize greenhouse gas emissions. This includes the substitution of fossil fuels with other energy sources. An important aspect of efficient and sustainable management of the electricity supply in Brazil is the prediction of some variables of the national electric system (NES), such as the price of differences settlement (PLD) and wind speed for wind energy. In this context, the present study investigated two distinct forecasting approaches. The first involved the combination of deep artificial neural network techniques, long short-term memory (LSTM), and multilayer perceptron (MLP), optimized through the canonical genetic algorithm (GA). The second approach focused on machine committees including MLP, decision tree, linear regression, and support vector machine (SVM) in one committee, and MLP, LSTM, SVM, and autoregressive integrated moving average (ARIMA) in another. The results indicate that the hybrid AG + LSTM algorithm demonstrated the best performance for PLD, with a mean squared error (MSE) of 4.68. For wind speed, there is a MSE of 1.26. These solutions aim to contribute to the Brazilian electricity market’s decision making.

Techno-economic optimization for isolated hybrid PV/wind/battery/diesel generator microgrid using improved salp swarm algorithm.

The main objective of this study is to develop a new method for solving the techno-economic optimization problem of an isolated microgrid powered by renewable energy sources like solar panels, wind turbines, batteries, and diesel generators while minimizing greenhouse gas emissions. An Improved Salp Swarm Algorithm (ISSA) with a position adaptation mechanism for the salp leader that involves a leader salp that moves about depending on both food availability and its previous position has been proposed to overcome the convergence problem. In the original SSA, as the approach converges, it can no longer find optimal solutions and becomes trapped in a local minimum. Three Microgrid System (MS) configurations are discussed: PV/WT/BESU/DG, PV/BESU/DG, and WT/BESU/DG. The proposed method seeks to find a middle ground between technical criteria and environmental concerns when deciding on PV, WT, BESU, and DG sizes. The findings indicate that the proposed ISSA approach gives superior results compared to other well-known algorithms like the original SSA, the Ant Lion Optimizer (ALO), the Dragonfly Approach (DA), and the Moth-Flame Optimization Algorithm (MFO), which, after significant investigation, has been proven to help determine the appropriate microgrid size. With PV sizes of 10, 9 WT, 24 BESU, and 3 DG, the PV/WT/BESU/DG configuration offers the highest level of cost-effectiveness with Cost of Energy (COE) of 0.2109 $/kWh, Net Present Cost (NPC) of 376,063.8 $, Loss of Power Supply Probability (LPSP) of 4%, Renewable Energy Fraction (REF) of 96%, and CO2 emission of 12.4457 tons/year. ISSA is brought up as a possible solution to both the problem of rising energy prices and the difficulties inherent in microgrid design.

Studies of C2H2F4 and SF6 alternative gas mixtures for HPL RPC detectors for High Energy Physics applications

This research investigates the viability of replacing greenhouse gases C2H2F4 and SF6 with eco-friendly gas mixtures in Resistive Plate Chamber (RPC) detectors used at CERN LHC experiments. The study focuses on the addition of CO2 as a substitute of C2H2F4 to minimize greenhouse gas emissions and reduce operational costs. Initial performance studies with cosmic muons evaluate key parameters such as efficiency, current, streamer probability, prompt charge, cluster size, and time resolution of the detectors. Subsequent assessments take place at the CERN Gamma Irradiation Facility, emulating the LHC experiment background radiation. Furthermore, the study explores alternatives to SF6, including 3M NOVECTM 4710 and AMOLEATM 1224yd, presenting perforances results about these gas alternatives in High Pressure Laminate RPCs.

An uncertain sustainable supply chain network design for regulating greenhouse gas emission and supply chain cost

The increasing global concern for sustainability in supply chain management is driven by stricter government regulations addressing environmental pollution and social injustice. This has led to a growing emphasis on integrating sustainability into supply chain practices. However, there is limited research on incorporating all three dimensions of sustainability (economic, environmental, and social) into supply chain management. This study presents a mixed-integer linear programming model for designing an uncertain supply chain network design that aims to minimize overall costs (establishment, production, and transportation/routing costs) while considering carbon emissions and a few social factors simultaneously. The study considers sustainable aspects of decision-making process and utilizes chance-constrained programming to address uncertainties. The proposed model attempts to maintain balanced flow levels across all stages of the network, optimizing the utilization of raw materials and production. The proposed optimization model is a cost minimization model that also tries to minimize greenhouse gas emissions throughout the entire network. A greedy based heuristic is provided for dealing with larger instances of the given decision making problem. Additionally, sensitivity analysis has also been carried out to explore the impact of various parameters involved.

Potential-risk and no-regret options for urban energy system design — A sensitivity analysis

This study identifies supply options for sustainable urban energy systems, which are robust to external system changes. A multi-criteria optimization model is used to minimize greenhouse gas (GHG) emissions and financial costs of a reference system. Sensitivity analyses examine the impact of changing boundary conditions related to GHG emissions, energy prices, energy demands, and population density. Options that align with both financial and emission reduction and are robust to system changes are called “no-regret” options. Options sensitive to system changes are labeled as “potential-risk” options.There is a conflict between minimizing GHG emissions and financial costs. In the reference case, the emission-optimized scenario enables a reduction of GHG emissions (−93%), but involves higher costs (+160%) compared to the financially-optimized scenario.No-regret options include photovoltaic systems, decentralized heat pumps, thermal storages, electricity exchange between sub-systems and with higher-level systems, and reducing energy demands through building insulation, behavioral changes, or the decrease of living space per inhabitant. Potential-risk options include solar thermal systems, natural gas technologies, high-capacity battery storages, and hydrogen for building energy supply.When energy prices rise, financially-optimized systems approach the least-emission system design. The maximum profitability of natural gas technologies was already reached before the 2022 European energy crisis.