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This paper is concerned with the effects of weather uncertainty on the electricity future curve. Following the approach used by Lucia and Schwartz (2002), the behavior of the underlying spot price is assumed to consist of two components ‘ a totally predictable deterministic component that accounts for regularities in the evolution of prices and a stochastic component that accounts for the behavior of residuals from the deterministic part. The weather uncertainty is modeled consistently with seasonal outlook probabilities from the CPC (Climate Prediction Center) outlook. For a given realization of temperature, the electricity load can be predicted very accurately by a time series model using temperature and other explanatory variables. Furthermore, if temperature and electricity load are known, the spot price can be predicted as well using the regime switching model with time-varying transition probabilities. The electricity future price can be calculated for the given seasonal probabilities from the CPC outlook. Then the electricity future price can be obtained as the arithmetic average of the one-day electricity future price. The future price reflects clearly the response of the spot price to different weather patterns. As the summer gets warmer, the high price regime is more likely to be realized, and as a result, the future price increases.

This paper aims to optimize the charging schedule for battery electric buses (BEBs) to minimize the charging cost considering the time-of-use electricity price.

The BEBs charging schedule optimization problem is formulated as a mixed-integer linear programming model. The objective is to minimize the total charging cost of the BEB fleet. The charge decision of each BEB at the end of each trip is to be determined. Two types of constraints are adopted to ensure that the charging schedule meets the operational requirements of the BEB fleet and that the number of charging piles can meet the demand of the charging schedule.

This paper conducts numerical cases to validate the effect of the proposed model based on the actual timetable and charging data of a bus line. The results show that the total charge cost with the optimized charging schedule is 15.56% lower than the actual total charge cost under given conditions. The results also suggest that increasing the number of charging piles can reduce the charging cost to some extent, which can provide a reference for planning the number of charging piles.

Considering time-of-use electricity price in the BEBs charging schedule will not only reduce the operation cost of electric transit but also make the best use of electricity resources.

The purpose of this paper is to extend the knowledge on business model transformation (BMT) by developing an integrative framework for BMT dilemmas, including strategies for shaping and stabilizing market structures.

The study uses a case-based approach, with data from the Swedish electric utility industry.

The findings uncover practices related to both shaping and stabilizing market structure. The study contributes with insights for firms to overcome the BMT dilemma. Shaping strategies involve disruptive innovations while stabilizing strategies concerns incremental improvements in existing structures; by balancing these efforts, firms can find ways toward successful BMT.

With a focus on incumbent firms and the balancing act of BMT in a network, the study covers areas that have scarcely been addressed in the existing literature. Even though most business model literature has focused on shaping consumer markets, the need to consider BMT as a dual-directional process in an industrial context is emphasized in this study.

This article aims to optimise energy use and consumption by integrating Lean Six Sigma methodology with the ISO 50001 energy management system standard in an Irish dairy plant operation.

This work utilised Lean Six Sigma methodology to identify methods to measure and optimise energy consumption. The authors use a single descriptive case study in an Irish dairy as the methodology to explain how DMAIC was applied to reduce energy consumption.

The replacement of heavy oil with liquid natural gas in combination with the new design of steam boilers led to a CO₂ footprint reduction of almost 50%.

A further longitudinal study would be useful to measure and monitor the energy management system progress and carry out more case studies on LSS integration with energy management systems across the dairy industry.

The novelty of this study is the application of LSS in the dairy sector as an enabler of a greater energy-efficient facility, as well as the testing of the DMAIC approach to meet a key objective for ISO 50001 accreditation.

The purpose of this study is to continue to forecast trends in the hospitality and travel industry with practical implications.

This study is the updated version of our previous list of trends. The new edition updates the previous report on the implications for the hospitality industry of major trends now shaping the future. We focus mainly on energy, environmental and labor force and work trends and discuss sub-trends under each trend. We then implicate how the trends affect the Hospitality and Travel industry.

We shared implications under each sub-trends.

The value of this article is to analyze the impact of the environment on the Hospitality and Travel industry from a macro perspective. For each trend, we implicate an estimate for future trends. We hope this article sheds light on the prediction of the Hospitality and Travel industry.

Agricultural aggregation schemes provide numerous farmer-facing benefits, including reduced transportation costs and improved access to higher-demand urban markets. However, whether aggregation schemes also have positive food security dimensions for consumers dependent on peri-urban and local markets in developing country contexts is currently unknown. This paper aims to narrow this knowledge gap by exploring the actors, governance structures and physical infrastructures of the horticultural value chain of Bihar, India, to identify barriers to using aggregation to improve the distribution of fruits and vegetables to more local market environments.

This study uses mixed methods. Quantitative analysis of market transaction data explores the development of aggregation supply pathways over space and time. In turn, semi-structured interviews with value chain actors uncover the interactions and decision-making processes with implications for equitable fruit and vegetable delivery.

Whilst aggregation successfully generates multiple producer-facing benefits, the supply pathways tend to cluster around urban export-oriented hubs, owing to the presence of high-capacity traders, large consumer bases and traditional power dynamics. Various barriers across the wider enabling environment must be overcome to unlock the potential for aggregation to increase local fruit and vegetable delivery, including informal governance structures, cold storage gaps and underdeveloped transport infrastructures.

To the best of the authors’ knowledge, this study is the first critical analysis of horticultural aggregation through a consumer-sensitive lens. The policy-relevant lessons are pertinent to the equitable and sustainable development of horticultural systems both in Bihar and in similar low- and middle-income settings.

The purpose of this paper is to optimize the design of charging station deployed at the terminal station for electric transit, with explicit consideration of heterogenous charging modes.

The authors proposed a bi-level model to optimize the decision-making at both tactical and operational levels simultaneously. Specifically, at the operational level (i.e. lower level), the service schedule and recharging plan of electric buses are optimized under specific design of charging station. The objective of lower-level model is to minimize total daily operational cost. This model is solved by a tailored column generation-based heuristic algorithm. At the tactical level (i.e. upper level), the design of charging station is optimized based upon the results obtained at the lower level. A tabu search algorithm is proposed subsequently to solve the upper-level model.

This study conducted numerical cases to validate the applicability of the proposed model. Some managerial insights stemmed from numerical case studies are revealed and discussed, which can help transit agencies design charging station scientifically.

The joint consideration of heterogeneous charging modes in charging station would further lower the operational cost of electric transit and speed up the market penetration of battery electric buses.

The purpose of this paper is to examine the impact of structural breaks on the conditional variance of carbon emission allowance prices.

The authors employ the symmetric GARCH model, and two asymmetric models, namely the exponential GARCH and the threshold GARCH.

The authors show that the forecast performance of GARCH models improves after accounting for potential structural changes. Importantly, we observe a significant drop in the volatility persistence of emission prices. In addition, the effects of positive and negative shocks on carbon market volatility increase when breaks are taken into account. Overall, the findings reveal that when structural breaks are ignored in the emission price risk, the volatility persistence is overestimated and the news impact is underestimated.

The authors are the first to examine how the conditional variance of carbon emission allowance prices reacts to structural breaks.