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In an era marked by heightened geopolitical uncertainties, such as international conflicts and economic instability, the dynamics of energy markets assume paramount importance. Our study delves into this complex backdrop, focusing on the intricate interplay the between traditional and emerging energy sectors.

This study analyzes the interconnections among green financial assets, renewable energy markets, the geopolitical risk index and cryptocurrency carbon emissions from December 19, 2017 to February 15, 2023. We investigate these relationships using a novel time-frequency connectedness approach and machine learning methodology.

Our findings reveal that green energy stocks, except the PBW, exhibit the highest net transmission of volatility, followed by COAL. In contrast, CARBON emerges as the primary net recipient of volatility, followed by fuel energy assets. The frequency decomposition results also indicate that the long-term components serve as the primary source of directional volatility spillover, suggesting that volatility transmission among green stocks and energy assets tends to occur over a more extended period. The SHapley additive exPlanations (SHAP) results show that the green and fuel energy markets are negatively connected with geopolitical risks (GPRs). The results obtained through the SHAP analysis confirm the novel time-varying parameter vector autoregressive (TVP-VAR) frequency connectedness findings. The CARBON and PBW markets consistently experience spillover shocks from other markets in short and long-term horizons. The role of crude oil as a receiver or transmitter of shocks varies over time.

Green financial assets and clean energy play significant roles in the financial markets and reduce geopolitical risk. Our study employs a time-frequency connectedness approach to assess the interconnections among four markets' families: fuel, renewable energy, green stocks and carbon markets. We utilize the novel TVP-VAR approach, which allows for flexibility and enables us to measure net pairwise connectedness in both short and long-term horizons.

The maritime industry is the transport mode that contributes most to air pollution. The International Maritime Organization (IMO) identified the reduction of air pollution by ships as a crucial issue. Since 1 January 2020, ships have had to adopt strategies and new technologies to eliminate air pollution. However, ship compliance with nitrate oxide (NOx) emission restrictions is more challenging. This paper aims to identify ship owners' challenges in investing in new technologies.

This paper applied a hybrid methodology combining a survey, a balanced scorecard and fuzzy analytic hierarchy process (F-AHP) to identify and evaluate constraints and weights in investment decision-making for NOx technologies. A survey was carried out to validate constraints.

A survey was carried out, representing 5.1% of Greek-owned ships by deadweight capacity. The findings provide a weighted list of seven crucial technical and economic constraints faced by ship operators. The constraints vary from ship retrofit expenditure to crew training and waste management. Additionally, NOx emission technologies were compared. It was found that liquefied natural gas is the preferred investment option for the survey participants compared with selective catalytic reduction, exhaust gas recirculation and batteries.

Several studies have dealt with the individual technical feasibility of NOx reduction technologies. However, apart from technical feasibility for a shipowner, the selection of a NOx technology has several managerial and safety risks. Therefore, the originality of this paper is to reveal those constraints that have a higher weight on ship owners. With this cost-benefit approach, investment challenges for ship operators are revealed. Policymakers can benefit from the results of the employed methodology.

Agriculture commodity price forecasts have long been important for a variety of market players. The study we conducted aims to address this difficulty by examining the weekly wholesale price index of green grams in the Chinese market. The index covers a ten-year period, from January 1, 2010, to January 3, 2020, and has significant economic implications.

In order to address the nonlinear patterns present in the price time series, we investigate the nonlinear auto-regressive neural network as the forecast model. This modeling technique is able to combine a variety of basic nonlinear functions to approximate more complex nonlinear characteristics. Specifically, we examine prediction performance that corresponds to several configurations across data splitting ratios, hidden neuron and delay counts, and model estimation approaches.

Our model turns out to be rather simple and yields forecasts with good stability and accuracy. Relative root mean square errors throughout training, validation and testing are specifically 4.34, 4.71 and 3.98%, respectively. The results of benchmark research show that the neural network produces statistically considerably better performance when compared to other machine learning models and classic time-series econometric methods.

Utilizing our findings as independent technical price forecasts would be one use. Alternatively, policy research and fresh insights into price patterns might be achieved by combining them with other (basic) prediction outputs.

The main purpose of this paper is to analyse the influence of capital efficiency on the economic growth of Bangladesh using the Harrod-Domar (H-D) model.

We use annual data from 1980 to 2019 for this paper. Three steps are taken in the data analysis. First, to check the existence of a unit root, we use the augmented Dickey-Fuller (ADF) test and to determine co-integration among the variables, we use the Johansen-Juselius co-integration test. Next, for long-run estimation, we use the dynamic ordinary least square (DOLS) estimator. The sensitivity of the long-run estimations is further checked by the fully modified OLS (FMOLS) and autoregressive distributed lag (ARDL) estimators. Lastly, we use the Granger causality test to determine the long-run causality among the variables.

The long-run co-integration test validates the co-integrating relationship among the variables. DOLS estimations reveal that the economic growth of Bangladesh is negatively associated with the incremental capital output ratio (ICOR), validating the notion that capital efficiency matters for achieving higher economic growth. On average, an increase in ICOR by a unit tends to reduce economic growth in the long term by 0.75 percent. Our results also reveal no significant relationship between savings and economic growth when the model is extended. Finally, causality results indicate unidirectional causality between ICOR and economic growth.

Based on the results obtained, we argue that the enhancement of capital productivity could bring efficiency because ICOR is an inverse of capital productivity. Since Bangladesh’s capital productivity is considerably low compared with other neighbouring countries, it is suggested that firms should gradually move towards technological advancement and enhance economies of scale, etc. in the long run. Moreover, policies in favour of continuous skill development programmes could be highly effective in increasing capital productivity given that capital follows a vintage structure.

This is the first paper to analyse the economic growth pattern of Bangladesh using the traditional H-D model by incorporating variables such as savings and ICOR and also by relaxing the assumption of time-invariant (i.e. fixed) data of the variables. Moreover, this paper extends the traditional H-D empirical model by introducing key indicators and time breaks for Bangladesh’s economy through a stepwise regression process.

Radical changes in consumer habits induced by the coronavirus disease (COVID-19) pandemic suggest that the usual demand forecasting techniques based on historical series are questionable. This is particularly true for hospitality demand, which has been dramatically affected by the pandemic. Accordingly, we investigate the suitability of tourists’ activity on Twitter as a predictor of hospitality demand in the Way of Saint James – an important pilgrimage tourism destination.

This study compares the predictive performance of the seasonal autoregressive integrated moving average (SARIMA) time-series model with that of the SARIMA with an exogenous variables (SARIMAX) model to forecast hotel tourism demand. For this, 110,456 tweets posted on Twitter between January 2018 and September 2022 are used as exogenous variables.

The results confirm that the predictions of traditional time-series models for tourist demand can be significantly improved by including tourist activity on Twitter. Twitter data could be an effective tool for improving the forecasting accuracy of tourism demand in real-time, which has relevant implications for tourism management. This study also provides a better understanding of tourists’ digital footprints in pilgrimage tourism.

This study contributes to the scarce literature on the digitalisation of pilgrimage tourism and forecasting hotel demand using a new methodological framework based on Twitter user-generated content. This can enable hospitality industry practitioners to convert social media data into relevant information for hospitality management.

2019冠狀病毒病引致消費者習慣有根本的改變; 這些改變顯示，根據歷史序列而運作的慣常需求預測技巧未必是正確的。這不確性尤以受到大流行極大影響的酒店服務需求為甚。因此，我們擬探討、若把在推特網站上的旅遊活動視為聖雅各之路 (一個重要的朝聖旅遊聖地) 酒店服務需求的預測器，這會否是合適的呢?

本研究比較 SARIMA 時間序列模型與附有外生變數 (SARIMAX)模型兩者在預測旅遊及酒店服務需求方面的表現。為此，研究人員收集在推特網站上發佈的資訊，作為外生變數進行研究。這個樣本涵蓋於2018年1月至2022年9月期間110,456個發佈資訊。

研究結果確認了傳統的時間序列模型，若涵蓋推特網站上的旅遊活動，則其對旅遊需求方面的預測會得到顯著的改善。推特網站的數據，就改善預測實時旅遊需求的準確度，或許可成為有效的工具; 而這發現對旅遊管理會有一定的意義。本研究亦讓我們進一步瞭解朝聖旅遊方面旅客的數碼足跡。

現存文獻甚少探討朝聖旅遊的數字化，而本研究不但在這方面充實了有關的文獻，還使用了一個根據推特網站上使用者原創內容嶄新的方法框架，進行分析和探討。這會幫助酒店從業人員把社交媒體數據轉變為可供酒店管理之用的合宜資訊。

This study aims to increase knowledge of the ability of nutrition labels to guide consumer choices in real-life environments.

Food consumption and plate waste data were collected from two self-service restaurants (SSR) with different customer groups over six observation days: three control and three intervention (with nutrition labelling) periods. Study Group 1 consisted of vocational school students, mostly late adolescents (N = 1,710), and Group 2 consisted of spa hotel customers, mostly elderly (N = 1,807). In the experimental restaurants, the same food was served to the buffets during the control and intervention periods.

The nutrition label in the lunch buffet guides customers to eat fewer main foods and salads and to select healthier choices. Increased consumption of taste enhancers (salt and ketchup) was observed in the study restaurants after nutritional labelling. Nutrition labelling was associated with a reduction in plate waste among the elderly, whereas the opposite was observed among adolescents.

The results provide public policymakers and marketers with a better understanding of the effects of nutrition labelling on consumer behaviour. Future studies should further evaluate the effects of nutrition labelling on the overall quality of customer diets and the complex environmental, social, and psychological factors affecting food choices and plate waste accumulation in various study groups.

Improving productivity and efficiency has always been crucial for industrial companies to remain competitive. In recent years, the topic of environmental impact has become increasingly important. Published research indicates that environmental and economic goals can enforce or rival each other. However, few papers have been published that address the interaction and integration of these two goals.

In this paper, we identify both, synergies and trade-offs based on a systematic review incorporating 66 publications issued between 1992 and 2021. We analyze, quantify and cluster examples of conjunctions of ecological and economic measures and thereby develop a framework for the combined improvement of performance and environmental compatibility.

Our findings indicate an increased significance of a combined consideration of these two dimensions of sustainability. We found that cases where enforcing synergies between economic and ecological effects were identified are by far more frequent than reports on trade-offs. For the individual categories, cost savings are uniformly considered as the most important economic aspect while, energy savings appear to be marginally more relevant than waste reduction in terms of environmental aspects.

No previous literature review provides a comparable graphical treatment of synergies and trade-offs between cost savings and ecological effects. For the first time, identified measures were classified in a 3 × 3 table considering type and principle.

Energy-efficiency measures have always been important when renovating aging building stock. For property owners, window intervention is a recurring issue. Replacement is common to reduce operational heating energy (OHE) use, something many previous building renovation studies have considered. Maintaining rather than replacing windows has received less attention, especially for multi-residential buildings in a subarctic climate where there is great potential for OHE savings. The objective was to assess the life cycle (LC) climate impact and costs of three window maintenance and replacement options for a 1980s multi-residential building in subarctic Sweden.

The options’ embodied and operational impacts from material production, transportation and space heating were assessed using a life cycle assessment (LCA) focusing on global warming potential (LCA-GWP) and life cycle costing (LCC) with a 60-year reference study period. A sensitivity analysis was used to explore the impact of uncertain parameters on LCA-GWP and LCC outcomes.

Maintaining instead of replacing windows minimized LC climate impact and costs, except under a few specific conditions. The reduced OHE use from window replacement had a larger compensating effect on embodied global warming potential (E-GWP) than investment costs, i.e. replacement was primarily motivated from a LC climate perspective. The LCA-GWP results were more sensitive to changes in some uncertain parameters, while the LCC results were more robust.

The findings highlight the benefits of maintenance over replacement to reduce costs and decarbonize window interventions, challenging property owners’ preference to replace windows and emphasizing the significance of including maintenance activities in future renovation research.

In response to the problem of insufficient traction/braking adhesion force caused by the existence of the third-body medium on the rail surface, this study aims to analyze the utilization of wheel-rail adhesion coefficient under different medium conditions and propose relevant measures for reasonable and optimized utilization of adhesion to ensure the traction/braking performance and operation safety of trains.

Based on the PLS-160 wheel-rail adhesion simulation test rig, the study investigates the variation patterns of maximum utilized adhesion characteristics on the rail surface under different conditions of small creepage and large slip. Through statistical analysis of multiple sets of experimental data, the statistical distribution patterns of maximum utilized adhesion on the rail surface are obtained, and a method for analyzing wheel-rail adhesion redundancy based on normal distribution is proposed. The study analyzes the utilization of traction/braking adhesion, as well as adhesion redundancy, for different medium under small creepage and large slip conditions. Based on these findings, relevant measures for the reasonable and optimized utilization of adhesion are derived.

When the third-body medium exists on the rail surface, the train should adopt the low-level service braking to avoid the braking skidding by extending the braking distance. Compared with the current adhesion control strategy of small creepage, adopting appropriate strategies to control the train’s adhesion coefficient near the second peak point of the adhesion coefficient-slip ratio curve in large slip can effectively improve the traction/braking adhesion redundancy and the upper limit of adhesion utilization, thereby ensuring the traction/braking performance and operation safety of the train.

Most existing studies focus on the wheel-rail adhesion coefficient values and variation patterns under different medium conditions, without considering whether the rail surface with different medium can provide sufficient traction/braking utilized adhesion coefficient for the train. Therefore, there is a risk of traction overspeeding/braking skidding. This study analyzes whether the rail surface with different medium can provide sufficient traction/braking utilized adhesion coefficient for the train and whether there is redundancy. Based on these findings, relevant measures for the reasonable and optimized utilization of adhesion are derived to further ensure operation safety of the train.