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Currently, there is a conflict in developing countries between the requirements for the self-development of forestry and the insufficient investment in the forestry sector, and the forest ticket system is an innovative forestry management method to solve this contradiction. In the research on the forest ticket system, the study of its price formation mechanism is relatively important. The key issues of the forest ticket system are how to form the forest ticket price and whether the forest ticket pricing methods are reasonable. Solving these problems is the purpose of this study.

This study will use three methods, namely the forest ecosystem service value evaluation index method, the ecosystem service value based on per unit area evaluation method and the contingent valuation method, to study the forest ticket price formation mechanism, filling the gap in the current research on forest ticket pricing methods. It will analyze how these three pricing methods specifically price the forest ticket and evaluate whether these pricing methods are reasonable. This study will then summarize and comprehensively study the forest ticket price formation mechanism and provide policy recommendations for decision-making departments.

The contingent valuation method and the forest ecosystem service value evaluation index method should be mainly used and given priority in the forest ticket pricing process. When the forest ticket is mainly issued for local residents' willingness to compensate for the forestry ecological value, the contingent valuation method should be mainly considered; when the forest ticket is mainly issued for compensating for the ecological value of local used forest land, the forest ecosystem service value evaluation index method should be mainly considered. The ecosystem service value based on per unit area evaluation method does not need to be the focus.

Compared with existing research studies, which focus more on the forest ticket system itself and the definition of forest ticket, this study mainly focuses on the forest ticket price formation mechanism, emphasizing how to form the forest ticket price and whether the forest ticket pricing methods are reasonable, which has a certain degree of innovation and research value and can partially fill the gap in related fields. At the same time, this study has certain help for the enrichment of the forest ticket system and the extension of related research studies.

This research aims to determine the ideal fare for various aircraft itineraries by modeling prices using a neural network method. Dynamic pricing has been studied from the airline’s point of view, with a focus on demand forecasting and price differentiation. Early demand forecasting on a specific route can assist an airline in strategically planning flights and determining optimal pricing strategies.

A feedforward neural network was employed in the current study. Two hidden layers, consisting of 18 and 12 neurons, were incorporated to enhance the network’s capabilities. The activation function employed for these layers was tanh. Additionally, it was considered that the output layer’s functions were linear. The neural network inputs considered in this study were flight path, month of flight, flight date (week/day), flight time, aircraft type (Boeing, Airbus, other), and flight class (economy, business). The neural network output, on the other hand, was the ticket price. The dataset comprises 16,585 records, specifically flight data for Iranian airlines for 2022.

The findings indicate that the model achieved a high level of accuracy in approximating the actual data. Additionally, it demonstrated the ability to predict the optimal ticket price for various flight routes with minimal error.

Based on the significant alignment observed between the actual data and the tested data utilizing the algorithmic model, airlines can proactively anticipate ticket prices across all routes, optimizing the revenue generated by each flight. The neural network algorithm utilized in this study offers a valuable opportunity for companies to enhance their decision-making processes. By leveraging the algorithm’s features, companies can analyze past data effectively and predict future prices. This enables them to make informed and timely decisions based on reliable information.

The present study represents a pioneering research endeavor that investigates using a neural network algorithm to predict the most suitable pricing for various flight routes. This study aims to provide valuable insights into dynamic pricing for marketing researchers and practitioners.

Marketing management, pricing strategies, zoo management, non-profit organizations.

Difficult. Recommended for courses: marketing, strategy, pricing, customer behaviour, management of non-profit organizations, emerging markets.

The case study deals with the pricing dilemma of the Birch House Zoo located in an Eastern European country. The zoo has implemented capital-intensive developments in the recent years its main attraction the Tropic World included. The organization is managed and subsidized by the city where it is situated, yet the City Council lately expressed that they wanted the zoo to be self-financing by the end of 2011 by finding new source of revenue. In 2009, the operational expenses of the zoo exceeded EUR five million; however, the revenues were far bellow this level. The tariff structure did not change in the last 30 years as pricing always had to be adjusted to the local purchasing power; recent developments and new attractions are only partly priced in at the moment. In the light of the special environment in which Birch House Zoo operates, the director has to initiate key actions that could bring the zoo to the level of breakeven in its operations and make it financially independent.

Ability to create pricing and revenue generating strategies; understanding idiosyncrasies of the management of non-profit organizations regarding this matter; understanding price elasticity issues.

Teaching note.

Aim of the present monograph is the economic analysis of the role of MNEs regarding globalisation and digital economy and in parallel there is a reference and examination of some legal aspects concerning MNEs, cyberspace and e‐commerce as the means of expression of the digital economy. The whole effort of the author is focused on the examination of various aspects of MNEs and their impact upon globalisation and vice versa and how and if we are moving towards a global digital economy.

A distinction must be drawn between a dismissal on the one hand, and on the other a repudiation of a contract of employment as a result of a breach of a fundamental term of that contract. When such a repudiation has been accepted by the innocent party then a termination of employment takes place. Such termination does not constitute dismissal (see London v. James Laidlaw & Sons Ltd (1974) IRLR 136 and Gannon v. J. C. Firth (1976) IRLR 415 EAT).

As businesses become more sophisticated and welcome new technologies, artificial intelligence (AI)-based methods are increasingly being used for firms' pricing decisions. In this review article, we provide a survey of research in the area of AI and pricing. On the upside, research has shown that algorithms allow companies to achieve unprecedented advantages, including real-time response to demand and supply shocks, personalized pricing, and demand learning. However, recent research has uncovered unforeseen downsides to algorithmic pricing that are important for managers and policy-makers to consider.

As a typical nature-based solution to climate change, forestry carbon sinks are vital to achieving carbon neutrality in China. However, regulations in China are insufficient to promote the development of carbon offset projects in forestry. This study aims to identify the regulatory obstacles impeding the development of forestry offsets under China’s certified emission reduction (CCER) and explore ways to improve the regulatory system.

This study conducts a qualitative analysis using a normative legal research method. This study conducted a synthetic review of national and local regulatory documents to gain insights into the regulatory landscape of forestry offsets in China. The main contents and characteristics of these documents are illustrated. Furthermore, related secondary literature was reviewed to gain further insight into forestry offset regulations and to identify significant gaps in China’s CCER regulation.

Forestry offset regulations under the CCER are characterized by fragmentation and a relatively lower legally binding force. There is no systematic institutional arrangement for forestry offset development, impeding market expectations and increasing transaction costs. The main challenges in China’s regulation of forestry carbon sinks include entitlement ambiguity, complicated rules for registration and verification, a lack of mechanisms for incentives, risk prevention and biodiversity protection.

Forestry carbon sinks’ multiple environmental and social values necessitate their effective development and utilization. This study assessed forestry offset regulations in China and proposed corresponding institutional arrangements to improve forestry carbon sink regulations under the CCER.

A tax based on land value is in many ways ideal, but many economists dismiss it by assuming it could not raise enough revenue. Standard sources of data omit much of the potential tax base, and undervalue what they do measure. The purpose of this paper is to present more comprehensive and accurate measures of land rents and values, and several modes of raising revenues from them besides the conventional property tax.

The paper identifies 16 elements of land's taxable capacity that received authorities either trivialize or omit. These 16 elements come in four groups.

In Group A, Elements 1‐4 correct for the downward bias in standard sources. In Group B, Elements 5‐10 broaden the concepts of land and rent beyond the conventional narrow perception, while Elements 11‐12 estimate rents to be gained by abating other kinds of taxes. In Group C, Elements 13‐14 explain how using the land tax, since it has no excess burden, uncaps feasible tax rates. In Group D, Elements 15‐16 define some moot possibilities that may warrant further exploration.

This paper shows how previous estimates of rent and land values have been narrowly limited to a fraction of the whole, thus giving a false impression that the tax capacity is low. The paper adds 14 elements to the traditional narrow “single tax” base, plus two moot elements advanced for future consideration. Any one of these 16 elements indicates a much higher land tax base than economists commonly recognize today. Taken together they are overwhelming, and cast an entirely new light on this subject.

This research constructs the critical predictors of visitation that shall allow the practitioners to foresee the visitation in the years to come through secondary data. For this study, tourist arrival data associated with the most popular forest park (i.e., Xiton Forest Park) in Taiwan along with relevant socio-economic data are utilized. This research adopts a group of analytical procedures involving correlation analysis, regression, and curve estimation analyses. The results show that the number of holiday per month and the average monthly rainfall have positive and negative correlations, respectively, with the visitation. Meanwhile, average monthly temperature and monthly gross domestic product per capita show a positive correlation in all three analytical methods and therefore are regarded as the primary predictors of tourist arrival. Consequently, this study provides managerial implications to increase the tourist arrivals to the forest park.

This study aims to identify an automated machine learning algorithm with high accuracy that sport practitioners can use to identify the specific factors for predicting Major League Baseball (MLB) attendance. Furthermore, by predicting spectators for each league (American League and National League) and division in MLB, the authors will identify the specific factors that increase accuracy, discuss them and provide implications for marketing strategies for academics and practitioners in sport.

This study used six years of daily MLB game data (2014–2019). All data were collected as predictors, such as game performance, weather and unemployment rate. Also, the attendance rate was obtained as an observation variable. The Random Forest, Lasso regression models and XGBoost were used to build the prediction model, and the analysis was conducted using Python 3.7.

The RMSE value was 0.14, and the R2 was 0.62 as a consequence of fine-tuning the tuning parameters of the XGBoost model, which had the best performance in forecasting the attendance rate. The most influential variables in the model are “Rank” of 0.247 and “Day of the week”, “Home team” and “Day/Night game” were shown as influential variables in order. The result was shown that the “Unemployment rate”, as a macroeconomic factor, has a value of 0.06 and weather factors were a total value of 0.147.

This research highlights unemployment rate as a determinant affecting MLB game attendance rates. Beyond contextual elements such as climate, the findings of this study underscore the significance of economic factors, particularly unemployment rates, necessitating further investigation into these factors to gain a more comprehensive understanding of game attendance.