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The management issues of this article, and the author is attempting to address these issues, are as follows: What is the optimal decision of each entity in the closed-loop supply chain for the cascading utilization of power batteries under three government measures: no subsidies, subsidies and rewards and punishments? How do different measures affect the process of cascading the utilization of power batteries? Which measures will help incentivize cascading utilization and battery recycling efforts?

The paper uses game analysis methods to study the optimal decisions of various stakeholders in the supply chain under the conditions of subsidies, non-subsidies and reward and punishment policies. The impact of various parameters on the returns of game entities is tested through Matlab numerical simulation.

The analysis discovered that each party in the supply chain will see an increase in earnings if the government boosts trade-in subsidies, which means that the degree of recycling efforts of each entity will also increase; under the condition with subsidies, the recycling efforts and echelon utilization rates of each stakeholder are higher than those under the incentive and punishment measure. In terms of the power battery echelon’s closed-loop supply chain incentive, the subsidy policy exceeds the reward and punishment policy.

The article takes the perspective of differential games and considers the dynamic process of exchanging old for new, providing important value for the practice of using old for new behavior in the closed-loop supply chain of power battery cascading utilization.

Revenue management (RM) is a significant technique to improve revenue with limited resources. With the macro environment of dramatically increasing transit capacity and rapid railway transport development in China, it is necessary to involve the theory of RM into the operation and decision of railway passenger transport.

This paper proposes the theory and framework of generalized RM of railway passenger transport (RMRPT), and the thoughts and methods of the main techniques in RMRPT, involving demand forecasting, line planning, inventory control, pricing strategies and information systems, are all studied and elaborated. The involved methods and techniques provide a sequential process to help with the decision-making for each stage of RMRPT. The corresponding techniques are integrated into the information system to support practical businesses in railway passenger transport.

The combination of the whole techniques devotes to railway benefit improvement and transit resource utilization and has been applied into the practical operation and organization of railway passenger transport.

The development of RMRPT would provide theoretical and technical support for the improvement of service quality as well as railway benefits and efficiency.

Although many countries are focusing on the management of construction and demolition waste (CDW) resource utilization, the effect of risk aversion of the green innovation-led enterprise on the performance of the CDW resource utilization supply chain is unclear when considering different green innovation contexts (green innovation led by the building materials remanufacturer or by the construction waste recycler). This study aims to investigate how the level of risk aversion of the green innovation-led enterprise affects CDW resource utilization under different green innovation contexts based on contingency theory.

Using Stackelberg game theory, this study establishes a decision model consisting of a building materials remanufacturer, construction waste recycler and CDW production unit and investigates how the level of risk aversion of the green innovation-led enterprise under different green innovation contexts influences the performance level of the supply chain.

The conclusions are as follows. (1) For the green innovation-led enterprise, the risk-averse behaviour is always detrimental to his own profits. (2) For the follower, the profits of the construction waste recycler are negatively correlated with the level of risk aversion of the green innovation-led enterprise in the case of a small green innovation investment coefficient. If the green innovation investment coefficient is high, the opposite result is obtained. (3) When the green innovation investment coefficient is low, the total supply chain profits decrease as the level of risk aversion of the green innovation-led enterprise increases. When the green innovation investment coefficient is high, total supply chain profit shows an inverted U-shaped trend with respect to the degree of risk aversion of the green innovation-led enterprise.

(1) This study is the first to construct a green innovation context led by different enterprises in the CDW resource utilization supply chain, which provides a new perspective on green management and operation. (2) This study is the first to explore the operation mechanism of the CDW resource utilization supply chain based on contingency theory, which provides new evidence from the CDW resource utilization supply chain to prove contingency theory. At the same time, this study examines the interactive effects of the green innovation cost coefficient and the degree of risk aversion of green innovation-led enterprises on the performance of supply chain members, expanding the contingency theory research on contingencies affecting enterprise performance. (3) This study will guide members of the CDW resource utilization supply chain to rationally face risks and achieve optimal supply chain performance.

Given the inevitable transition to renewable resource utilization and the urgent need to reduce carbon emissions, this study conducted quasi natural experiments to assess the impact of renewable resource utilization on carbon emissions based on the national “urban mining” demonstration bases (NUMDB).

This study uses panel data from 275 prefecture-level cities in China from 2006 to 2019. The paper selects NUMDB as the proxy variable and conducts a quasi-natural experiment using a multi-period differences-in-differences model. We examine the impact of NUMDB on reducing carbon emissions, and then deeply explore its mechanism and spatial spillover effect.

This study found that: (1) the construction of NUMDB can significantly decrease the carbon emission in the host cities; (2) NUMDB’s construction has more significantly reduced the carbon emission in regions with higher levels of circular economy development, green technology innovation, regional environmental pollution, digital economy development and financial development; (3) by means of green technology innovation, optimized energy structure, and high-quality talent aggregation, NUMDB reduces urban carbon emissions; (4) NUMDB construction positively affects the carbon reduction efficiency of neighboring regions.

We propose corresponding policy suggestions to further promote the carbon emission reduction effect of NUMDB and develop the renewable resources industry in China based on the research findings.

The contributions of this paper are as follows. Our study contributes to expanding the research scope on the environmental impact of the renewable resource industry, as there are few quantitative studies in this area.

We further consider the spatial heterogeneity of policies and analyze the carbon reduction effect of the NUMDB from the city level, which is beneficial to exploring more targeted and operable carbon reduction paths.

This study on identifying the causal relationship between renewable resource utilization and carbon emission reduction helps to explore the sustainable development path of renewable resource more comprehensively. Meanwhile, this paper provides a reference for other countries to improve the utilization of renewable resource and effectively reduce carbon emissions.

The purpose of this study is to examine households’ behavior towards dirty cooking energy utilisation in an environment where relatively higher accessibility to clean energy is noted. Although the low utilisation rate of clean energy can partly be attributed to utility gains anticipated in dirty energy mixes (DEMs) arising out of accessibility constraints, affordances and enablers, it is still unclear on the extend at which each of these contributes towards DEMs manifestation among the seemingly well-to-do households with higher levels of clean energy mixes (CEM) access. This study, therefore, hinges on scrutinising on this lower utilisation patterns despite a seemingly higher accessibility of CEMs, specifically liquified petroleum gases (LPG).

The study is based on a household’s survey that was carried out in 2018, reaching a sample of 393 households using questionnaires in four wards of the Kigamboni district in Tanzania. Subsequent analyses were descriptive as well as inferential based on binary logistic regression analysis where utilisation of DEMs was predicted for both the high and low social economic status (SES) households by incorporating accessibility constraints, affordances and enablers.

The results show, first, if one assumes energy stacking is not an issue, as households become more constrained towards CEMs utilisation, they shift towards DEMs suggesting that the overall effect is a substitution, and second, the complementarity effect ultimately outweighs the substitution effect as households do not shift from DEMs to CEMs rather stack multiple energy. DEMs flourish in this case study area because those with high income are among those in the lowest SES, and some of those with the highest SES are from among the lowest income category, and all of them end up with more DEMs because shifting towards CEMs require income to complement SES.

Policy-wise, removing hurdles in accessing CEMs such as LPG subsidy programme, gas stove provision to the poor, and enhanced LPG awareness will most likely benefits only those who do not stack energy in cooking while strategies targeting those at the lowest SES such as higher education attainment, empower women as a family decision maker, encourage co-occupancy to enlarge the household size and contain urban growth within certain perimeter will have a significant impact only if they raise both incomes and SES.

Despite of the dominance of DEMs for cooking such as charcoal and firewood in Tanzania, CEMs such as LPG, have emerged as complements or alternatives in the household energy basket. The utilisation of such CEMs is, however, still very low despite the accessibility, cost, environmental and health advantages they offer. Accessibility is not the only factor fuelling CEMs; a complementarity must exist between SES and income for the positive transition towards CEMs to be realised.

The primary objective of this study is to investigate the application of blockchain technology (BCT) in the agri-food supply chain, focusing on traceability and food safety.

The study employed a semi-structured interview method with representatives from the cherry supply chain to evaluate their awareness and acceptance of BCT's impact. Additionally, the analytic hierarchy process (AHP) was utilized to determine digital investment priorities in supply chain strategies. By applying the supply chain operations reference (SCOR) model framework to the cherry supply chain, the study aimed to address the question “Which process model is suitable for implementing BCT in the agri-food supply chain?”

The global agri-food supply chains are characterized by significant food losses, escalating prices along the chain, and food safety risks. Concurrently, consumer concerns regarding food safety, quality and transparency are on the rise. BCT, with its ability to ensure data integrity, immutability, and seamless tracking of chain movements, presents immense potential as a secure infrastructure in the agri-food supply chain traceability.

The developed analytic framework and the study's findings can be adapted to different sectors and different sub-sectors within agri-food supply chains.

Data science industry is a multidisciplinary field that deals with a large amount of data and derives useful information for taking routine and strategic business decisions. The purpose of this article is to examine the relationship between pricing models, engagement models, and firm performance (FP). This study also aims at uncovering the most effective pricing model and engagement model for improving FP.

Indian data scientists were the respondents of the study. A total of 213 responses were carefully chosen. The data were analyzed using structural equations on Statistical Package for Social Sciences-Analysis of Moment Structures (SPSS-AMOS) version 25 software.

The findings of the study suggested the positive and significant impact of pricing models and engagement models on FP. Value-based pricing strategies have the maximum impact on FP. On the other hand, managed services have a higher influence on FP.

By developing a multi-faceted framework, this study is a novel contribution to the field of business strategy, especially for the data science industry.

This study aims to examine consumer perception during COVID-19 and identifies cruise industry marketing strategies to fill a gap in crisis management and product pricing literature.

This study developed and validated two-factor measurement scales (vaccine perception and protective behavior), which predicted cruise intents well. This study revealed how geo-regional factors affect consumer psychology through spatial analysis.

This study recommended pricing 7-day cruises at $1,464 (the most preferred length). The results also showed that future price hikes would not affect demand and that coastal marketing would help retain customers.

This study contributed to the business, hospitality and tourism literature by identifying two new and unique factors (vaccine perception and protective behaviors), which were found to affect consumers’ intention to travel by cruise significantly. The result provided a better understanding of cruise tourists’ pricing preferences and the methods utilized could easily be applied to other cruise markets or tourism entities.

Hospitality firms aim to increase their profits by implementing a variety of marketing activities, including using decoy pricing to provide alternative choices for consumers. Decoys are relatively higher-priced offerings that signal lower value than the other offerings in the consideration set. The purpose of this research is to investigate the influence of decoy pricing on consumer choices across various contexts in the foodservice and hotel industries.

Across the pilot and four main studies, the current research employs a sequential exploratory mixed-method design to investigate the influence of decoy pricing in the foodservice and lodging industries. The qualitative part of this research was based on two focus groups, followed by a pilot study and four main study experiments.

The results show that decoy pricing escalates consumers’ choices of more expensive product bundles in both restaurant and hotel cancellation policy contexts. However, decoy pricing does not increase the selection of more expensive hotel product bundles.

While decoy pricing has been utilized as an effective revenue maximization strategy for product placement in retail stores, less is known about how promotional advertisements with decoy offers influence hotel and restaurant customers to choose more costly options. Specifically, this is the first study that explores whether decoy pricing and product/service bundling can encourage customers to select more expensive offers in hotel and restaurant contexts, considering the types of hospitality bundles that may limit this effect.

The study aims to determine the long and short-term causal relationships between the variables associated with the adjustment of monetary policy and the stock market in India in the presence of structural breaks.

The study employed the autoregressive distributed lag (ARDL) bounds test and the Error Correction Model to assess long- and short-term causal relationships. The study also used non-frequentist Bayesian inferences for the validity of estimation robustness. The Bai–Perron test is used to identify breakpoint dates for the Indian stock market index, and the Granger Causality test is employed to ascertain the direction of causality.

The F-bounds test reveals cointegration among the variables throughout the examined period. Specifically, the weighted average call money rate (WACR), inflation (WPI), currency exchange rate (EXE), and broad money supply (M3) exhibit statistical significance with precise signs. Furthermore, the study identifies the negative impact of the COVID-19 outbreak in March 2020 on the Indian stock market.

Although the study provides significant insights, it is not exempt from constraints. A significant limitation is selecting a relatively limited time period, specifically from April 2008 to September 2023. The limited time frame of this study may restrict the applicability of the results to more comprehensive economic settings, as dynamics between the monetary policy and the stock market can be influenced by multiple factors over varying time periods. Furthermore, the utilisation of the Weighted Average Call Money Rate (WACR) rather than policy rates such as the Repo rate presents an additional constraint as it may not comprehensively account for the impacts of particular policy initiatives, thereby disregarding essential complexities in the connection between monetary policy variables and financial markets.

The findings of the study suggest that investors and portfolio managers should consider economic issues while developing long-term investing plans. Reserve Bank of India should exercise prudence to prevent any discretionary measures that may lead to a rise in interest rates since this adversely affects the stock market. To mitigate risk, investors should closely monitor the adjustment of monetary policy variables.

The study has important social implications, especially regarding the lower levels of financial literacy among investors in India. Considering the complex nature of the study’s emphasis on monetary policy adjustments and their impact on the stock market. Investors face the risk of significant losses due to unexpected adjustments in monetary policy. Many individuals may need help understanding how policy changes impact their investments. Therefore, RBI must consider both price and financial stability when formulating monetary policies. Furthermore, market participants should consider the potential impact of fluctuating monetary policy variables when devising their long-term investment strategies. Given that adjustments in interest rates can markedly affect stock market dynamics, investors must carefully assess the implications of monetary policy decisions on their portfolios.

The study uses dummy variables in the ARDL model to represent structural breaks that emerged from the COVID-19 pandemic (as determined by the Bai–Perron multiple breakpoint test). The study also used the Perron unit root test to find out the stationary of the series in the presence of structural breaks. Additionally, the study also employed Bayesian inferences to affirm the robustness of the estimates.