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Simulation-based digital twins represent an effort to provide high-accuracy real-time insights into operational physical processes. However, the computation time of many multi-physical simulation models is far from real-time. It might even exceed sensible time frames to produce sufficient data for training data-driven reduced-order models. This study presents TwinLab, a framework for data-efficient, yet accurate training of neural-ODE type reduced-order models with only two data sets.

Correlations between test errors of reduced-order models and distinct features of corresponding training data are investigated. Having found the single best data sets for training, a second data set is sought with the help of similarity and error measures to enrich the training process effectively.

Adding a suitable second training data set in the training process reduces the test error by up to 49% compared to the best base reduced-order model trained only with one data set. Such a second training data set should at least yield a good reduced-order model on its own and exhibit higher levels of dissimilarity to the base training data set regarding the respective excitation signal. Moreover, the base reduced-order model should have elevated test errors on the second data set. The relative error of the time series ranges from 0.18% to 0.49%. Prediction speed-ups of up to a factor of 36,000 are observed.

The proposed computational framework facilitates the automated, data-efficient extraction of non-intrusive reduced-order models for digital twins from existing simulation models, independent of the simulation software.

The present study aims to examine the impact of social media marketing activities (SMMAs) on the adoption of food waste reduction behavior among Generation Z consumers within the restaurant service industry in Pakistan. The study focuses on the impact of social media advertisements and investigates the mediating influence of waste reduction intentions on actual behavior. This underscores the significance of contextual and emotional variables in comprehending consumer behaviors.

The study used a cross-sectional research methodology to examine the impact of SMMAs on the behavior of Generation Z consumers in Pakistan’s food service industry with regard to reducing food waste. A study was conducted to investigate the restaurant purchasing behaviors of a sample consisting of 449 individuals belonging to the millennial generation, often known as Generation Z.

The majority of variables related to SMMA, except for interactivity and personalization, were shown to have a positive impact on individuals’ intents to reduce food waste. The study observed a significant relationship between consumers’ intentions to decrease waste and their actual behavior in waste reduction. Furthermore, this relationship was shown to be influenced by the mediating role of waste reduction intention.

Examining how social media affects Pakistani Generation Z’s efforts to reduce food waste is what makes this study distinctive. According to the research, the majority of social media factors positively influence intentions to reduce waste. The relationship between intentions and actual behavior, which highlights the impact of social media campaigns and emotional aspects in promoting waste reduction, is one of the important conclusions.

Prefabricated buildings (PBs) have proven to effectively mitigate carbon emissions in the construction industry. Existing studies have analyzed the environmental performance of PBs considering the shift in construction methods, ignoring the emissions abatement effects of the low-carbon practices adopted by participants in the prefabricated building supply chain (PBSC). Thus, it is challenging to exploit the environmental advantages of PBs. To further reveal the carbon reduction potential of PBs and assist participants in making low-carbon practice strategy decisions, this paper constructs a system dynamics (SD) model to explore the performance of PBSC in low-carbon practices.

This study adopts the SD approach to integrate the complex dynamic relationship between variables and explicitly considers the environmental and economic impacts of PBSC to explore the carbon emission reduction effects of low-carbon practices by enterprises under environmental policies from the supply chain perspective.

Results show that with the advance of prefabrication level, the carbon emissions from production and transportation processes increase, and the total carbon emissions of PBSC show an upward trend. Low-carbon practices of rational transportation route planning and carbon-reduction energy investment can effectively reduce carbon emissions with negative economic impacts on transportation enterprises. The application of sustainable materials in low-carbon practices is both economically and environmentally friendly. In addition, carbon tax does not always promote the implementation of low-carbon practices, and the improvement of enterprises' environmental awareness can further strengthen the effect of low-carbon practices.

This study dynamically assesses the carbon reduction effects of low-carbon practices in PBSC, informing the low-carbon decision-making of participants in building construction projects and guiding the government to formulate environmental policies.

Promoting electric vehicles (EVs) is an effective way to achieve carbon neutrality. If EVs are widely adopted, this will undoubtedly be good for the environment. The purpose of this study is to analyze the impact of network externalities and subsidy on the strategies of manufacturer under a carbon neutrality constraint.

In this paper, the authors propose a game-theoretic framework in an EVs supply chain consisting of a government, a manufacturer and a group of consumers. The authors examine two subsidy options and explain the choice of optimal strategies for government and manufacturer.

First, the authors find that the both network externalities of charging stations and government subsidy can promote the EV market. Second, under a relaxed carbon neutrality constraint, even if the government’s purchase subsidy investment is larger than the carbon emission reduction technology subsidy investment, the purchase subsidy policy is still optimal. Third, under a strict carbon neutrality constraint, when the cost coefficient of carbon emission reduction and the effectiveness of carbon emission reduction technology are larger, social welfare will instead decrease with the increase of the effectiveness of emission reduction technology and then, the manufacturer’s investment in carbon emission reduction technology is lower. In the extended model, the authors find the effectiveness of carbon emission reduction technology can also promote the EV market and social welfare (or consumer surplus) is the same whatever the subsidy strategy.

The network externalities of charging stations and the subsidy effect of the government have a superimposition effect on the promotion of EVs. When the network effect of charging stations is relatively strong, government can withdraw from the subsidized market. When the network effect of charging stations is relatively weak, government can intervene appropriately.

Comparing previous studies, this study reveals the impact of government intervention, network effects and carbon neutrality constraints on the EV supply chain. From a sustainability perspective, these insights are compelling for both EV manufacturers and policymakers.

Reducing construction waste generation and carbon emission in the construction industry is crucial for the “dual carbon” goal. Evaluating the efficiency of reducing construction waste generation and carbon emission in the construction industry at the regional level is an important evaluation basis for the sustainable development of the construction industry. It provides a basis for formulating construction waste and carbon reduction policies tailored to local conditions and comprehensively promote the sustainable development of the construction industry.

A three stage SBM-DEA model based on non-expected outputs is proposed by combining the SBM-DEA model with the SFA method. The proposed model is used to evaluate the efficiency of construction waste and carbon reduction in the construction industry in 30 regions of China from 2010 to 2020. Moreover, the study explores the impact of environmental variables such as urbanization level, proportion of construction industry employees, resident consumption level, and technological progress.

From 2010 to 2020, the efficiency of construction waste and carbon reduction in China’s construction industry has been increasing year by year. Provinces with higher efficiency of construction waste and carbon reduction in the construction industry are mainly concentrated in the eastern coastal areas, showing an overall pattern of “East>West>Northeast>Middle”. There is a clear correlation between the level of urbanization, the proportion of construction industry employees, residents’ consumption level, technological progress, labor input, machinery input, and capital investment. The construction waste and carbon emission efficiency of the construction industry in various provinces is greatly influenced by environmental factors.

The research results provide policy makers and business managers with effective policies for reducing construction waste generation and carbon emission in the construction industry, especially circular economy policies. To provide empirical support for further understanding the connotation of construction waste and carbon reduction in the construction industry, to create innovative models for construction waste and carbon reduction, and to promote the multiple benefits of construction waste and carbon reduction in the construction industry, and to provide empirical support for countries and enterprises with similar development backgrounds in China to formulate relevant policies and decision-making.

The construction industry is a high investment, high energy consumption, and high pollution industry. This study uses the three stage SBM-DEA model to explore the efficiency of construction waste and carbon reduction in the construction industry, providing a new perspective for the evaluation of sustainable development in the construction industry, enriching and improving the theory of sustainable development.

To explore how the blockchain affects the pricing and financing decisions in a low-carbon platform supply chain.

Considering the dual roles of the e-commerce platform as a seller and an initiator, a typical game-theoretical method is applied to analyze the behavior of supply chain decision-makers and the impact of key variables on equilibriums.

When loan interest rates are symmetric, whether blockchain is used or not, the e-commerce platform financing mode will always produce higher wholesale price and unit carbon emission reduction, while the retail price is the opposite. Higher unit additional income brought by the blockchain can bring higher economic and environmental performances, and the e-commerce platform financing mode is superior to bank financing mode. The application of blockchain may cause the manufacturer to change his/her financing choice. For bank financing, with the increase of loan interest rates, the advantages brought by blockchain will gradually disappear, but this situation will not occur under e-commerce platform financing.

Blockchain is known for its information transparency properties and its ability to enhance user trust. However, the impacts of applying blockchain in a low-carbon platform supply chain with different financing options are not clear. The authors examine the manufacturer's strategic choices for platform financing and bank financing, whether to adopt blockchain, and the impact of these decisions on carbon emissions reduction, consumer surplus and social welfare. The research conclusion can provide reference for the operation and financing decisions of platform supply chain under the carbon reduction target in the digital economy era.

This study aims to optimize the levels of factors for a green supply chain (GSC) while concurrently gaining valuable insights into the dynamic interrelationships among several factors, leading to reductions in CO₂ emissions and the maximization of the average service level, thereby enhancing overall supply chain performance.

Response surface methodology (RSM) is employed as a technique for multiple response optimization. This study uses a supply chain simulation model that includes decision variables related to the level of inventory control parameters and vehicle capacity. The desirability approach is adopted to achieve optimization objectives by focusing on minimizing CO₂ emissions and maximizing service levels while simultaneously determining the optimum levels of considered decision variables.

The high R² values of 97.38% for CO₂ and 97.28% for service level, along with adjusted R² values reasonably close to predicted values, affirm the models' capability to predict responses accurately. Key significant model terms for CO₂ encompassed reorder point, order up to quantity, vehicle capacity, and their interaction effects, while service level is notably influenced by reorder point, order up to quantity, and their interaction effects. The study successfully achieved a high level of desirability value of %99.1 and the validated performance levels confirmed that the results fall within the prediction interval.

This study introduces a metamodel framework designed to optimize various design parameters for a GSC combining discrete event simulation (DES) and RSM in the form of a simulation optimization model. In contrast to the literature, the current study offers an exhaustive and in-depth analysis of the structural elements of the supply chain, particularly the inventory control parameters and vehicle capacity, which are crucial for comprehending its performance and environmental impact.

Construction waste reduction (CWR) plays a vital role in achieving sustainability in construction. A good CWR practice can result in optimizing material usage, conserving natural resources, limiting environmental pollution, protecting the environment and enhancing human health. In this regard, the purpose of the current study is to identify the most relevant organizational policies that aid in waste reduction and concurrently explores the congruent measures to be adopted during the construction process in the Indian high-rise building sector.

The research findings were obtained through a mixed- method approach. Content analysis was used to identify waste reduction measures (variables) targeting on the two domains of construction – “waste-efficient execution” and “waste – mitigating organizational policies.” Furthermore, the authors explored and documented the key measures from the identified waste reduction measures using the constraint value of the relative importance index. As the next step, the study listed the theoretical hypothesis based on expert interviews and tested the theory through confirmatory factor analysis.

The results revealed that “waste sensitive construction techniques and strategies” as the most significant category under the domain “Execution” with a path coefficient of 0.85. Concurrently, the study has also determined that “control procedures for budget, quality and resources” as the most effective organizational approach in reducing construction waste in the Indian building industry, with a path coefficient of 0.83.

The current research is context-sensitive to the Indian construction sector. It presents the stakeholder’s perspective on construction waste reduction and the relevant measures to be implemented to reduce construction waste in high-rise building projects in India. It can also act as a concordance for decision-makers to further focus on CWR management and aid in formulating policies suitable for the Indian context.

Traditional grey models are integer order whitening differential models; these models are relatively effective for the prediction of regular raw data, but the prediction error of irregular series or shock series is large, and the prediction effect is not ideal.

The new model realizes the dynamic expansion and optimization of the grey Bernoulli model. Meanwhile, it also enhances the variability and self-adaptability of the model structure. And nonlinear parameters are computed by the particle swarm optimization (PSO) algorithm.

Establishing a prediction model based on the raw data from the last six years, it is verified that the prediction performance of the new model is far superior to other mainstream grey prediction models, especially for irregular sequences and oscillating sequences. Ultimately, forecasting models are constructed to calculate various energy consumption aspects in Chongqing. The findings of this study offer a valuable reference for the government in shaping energy consumption policies and optimizing the energy structure.

It is imperative to recognize its inherent limitations. Firstly, the fractional differential order of the model is restricted to 0 < a < 2, encompassing only a three-parameter model. Future investigations could delve into the development of a multi-parameter model applicable when a = 2. Secondly, this paper exclusively focuses on the model itself, neglecting the consideration of raw data preprocessing, such as smoothing operators, buffer operators and background values. Incorporating these factors could significantly enhance the model’s effectiveness, particularly in the context of medium-term or long-term predictions.

This contribution plays a constructive role in expanding the model repertoire of the grey prediction model. The utilization of the developed model for predicting total energy consumption, coal consumption, natural gas consumption, oil consumption and other energy sources from 2021 to 2022 validates the efficacy and feasibility of the innovative model.

These findings, in turn, provide valuable guidance and decision-making support for both the Chinese Government and the Chongqing Government in optimizing energy structure and formulating effective energy policies.

This research holds significant importance in enriching the theoretical framework of the grey prediction model.

The highlights of the paper are as follows:

1. A novel grey Bernoulli prediction model is proposed to improve the model’s structure.

2. Fractional derivative, fractional accumulating generation operator and Bernoulli equation are added to the new model.

3. The proposed model can achieve full compatibility with the traditional mainstream grey prediction models.

4. Energy consumption in Chongqing verifies that the performance of the new model is much better than that of the traditional grey models.

5. The research provides a reference basis for the government to formulate energy consumption policies and optimize energy structure.

A novel grey Bernoulli prediction model is proposed to improve the model’s structure.

Fractional derivative, fractional accumulating generation operator and Bernoulli equation are added to the new model.

The proposed model can achieve full compatibility with the traditional mainstream grey prediction models.

Energy consumption in Chongqing verifies that the performance of the new model is much better than that of the traditional grey models.

The research provides a reference basis for the government to formulate energy consumption policies and optimize energy structure.

Spurred by the high turnover in the pharmaceutical industry, locating pharmacies inside urban areas along with the high product perishability in this industry, the pharmaceutical supply chain management has recently gained increasing attention. Accordingly, this paper unveils an inventory-routing problem for designing a pharmaceutical supply chain with perishable products and time-dependent travel time in an uncertain environment.

In this study, mathematical programming is employed to formulate a multi-graph network affected by the traffic volume in order to adapt to real-world situations. Likewise, by transforming the travel speed function to the travel time function using a step-by-step algorithm, the first-in-first-out property is warranted. Moreover, the Box–Jenkins forecasting method is employed to diminish the demand uncertainty.

An appealing result is that the delivery horizon constraint in the under-study multi-graph network may eventuate in selecting a longer path. Our analysis also indicates that the customers located in the busy places in the city are not predominantly visited in the initial and last delivery horizon, which are the rush times. Moreover, it is concluded that integrating disruption management, routing planning and inventory management in the studied network leads to a reduction of costs in the long term.

Applying the time-dependent travel time with a heterogeneous fleet of vehicles on the multi-graph network, considering perishability in the products for reducing inventory costs, considering multiple trips of transfer fleet, considering disruption impacts on supply chain components and utilizing the Box–Jenkins method to reduce uncertainty are the contributions of the present study.