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• This study develops an econometric model of a multiple integer demand. • We show that the proposed model has a closed-form probability. • An integer programing approach for demand prediction and welfare analysis is developed. • The empirical results show that the proposed model provides a better fit for the data. We developed an integer programing extreme value (IPEV) model that accounts the integer property of trip data and has the same advantages as the multiple discrete–continuous extreme value choice (MDCEV) model. The proposed model is consistent with utility theory and provides a single structural framework for simultaneously modeling the choice of alternatives and quantity decisions with the constraint of the integer value of consumption. We demonstrate that the proposed model has a closed-form probability expression. Finally, we apply the proposed model to the recreation demand for national parks in Japan. The empirical results suggest that the proposed model provides a better fit for the data than the previous model and that ignoring the integer property of demand might cause an underestimation of the welfare loss. [ABSTRACT FROM AUTHOR]

Studies dealing with obesity have confronted it either from the fiscal policy or from economic cost perspective. There is the need to target fiscal policy goals at the economic cost that obesity imposes on society instead of simulating arbitrary taxes schemes. This paper analyzes the effectiveness of imposing a revenue-neutral fat tax in Spain addressed to internalize the public health expenditure associated to obesity. Results suggest that this tax policy led to an improvement in the diet quality, and decreased the consumption of saturated fatty acid, sodium, and cholesterol. From the welfare perspective, the tax is regressive across all household segments. [ABSTRACT FROM AUTHOR]

This study aims to analyze Saudi demand for natural honey imported from Yemen, Pakistan, Australia, Argentina, Mexico, and Germany, which contribute around 73.7% of the value of natural honey imports during the period of study (1991–2017). Marshallian elasticities estimation using AIDS model shows all own-price elasticities were negative and significant except for own-price elasticity associated with demand for natural honey imported from Pakistan. Natural honey imported from Yemen has own-price inelastic demand. In addition, the results of cross-price elasticities show a relationship between different products of imported natural honey and the estimated values of expenditure elasticities. According to these study results, it is important to encourage the domestic production of honey to meet the increase in the demand of the natural honey. The future studies could examine whether the domestic natural honey might be close substitute of any of these imported natural honey. In addition, we recommend increasing sources of import natural honey, especially if a country might be closer substitute for others. [ABSTRACT FROM AUTHOR]

In Turkey, agricultural manufacturing plays a big role in industry as a sector and can produce almost all categories of agricultural equipment. When considered agricultural machinery sector, it is ranked as the 5th biggest machinery sector in Turkey, covering 7% of the total exports and in domestic market, agricultural machinery sector is ranked as 4th sector. From a regional point of view, it can be stated that İstanbul, Ankara, İzmir, Konya, Aydın, Manisa and Balıkesir regions are prominent in the sector in terms of both manufacturing volume and number of employments. However, when it comes to contribute and implement new technological developments, Turkey cannot perform adequately considering export-import balance. Most of the imports are especially the equipment possessing high technology. The lack of following trend technologies becomes a potential threat on the sector. A demand analysis questionnaire was conducted to understand the demands of SME's in agricultural machinery sector, which is the target group of 'Smart Technologies, Development, Design and Prototyping Center' that is planned to establish within our university. In this study, results of the survey that was implemented to capture current state of the overall approach to R&D applications and demand analysis in agricultural machinery sector in Konya, Turkey. In addition to the current picture, main impediments in process of technological transformation are confirmed by the results of demand analysis survey for the region. The demand analysis clearly revealed that almost 60% of the companies participating in the survey believe that there are problems in the current state of the sector; whereas another additional 10% considers the future of the sector being in jeopardy. Main problems are defined as Lack of skilled labour, Insufficient R&D activities by considering low awareness of R&D and SMEs' inability to develop and conduct R&D projects and Lack of effective University-Industry Collaboration. [ABSTRACT FROM AUTHOR]

Urban policy makers are increasingly interested in cycling as part of a sustainable and reliable transport network. One of the ways through which cities promote cycling is by providing a public bicycle system (PBS) with electronic docking stations. This paper studies demand for public rental bicycles following local and temporary metro interruptions in Paris. I construct a unique data set by linking metro interruptions announced in Twitter communication by the Parisian metro operator to usage data on the Vélib' PBS. I find that, as a direct consequence of a metro interruption, the consumption of bicycles within 100 m of metro stations increases by 0.72 bicycles per hour per docking station on average, and with 1.54 bicycles per hour per docking station during the first 20 min; an increase of approximately 11% and 22% respectively. Due to their effects on demand, metro interruptions increase the probability of empty stocks at docking stations with 15%. The findings highlight that cycling is a local net substitute for metro service, and that public rental bicycles can alleviate time losses stemming from interruptions in public transport. [ABSTRACT FROM AUTHOR]

Product-Service Systems (PSS) provide a new way of value creation, but recent studies of PSS seldom consider the relationship between customers' demand on PSS and the service capacity of enterprises. Also, they lack discussions on the relationship between customers' dynamic demand on PSS and the integrated value of PSS. Thus we get inspired from the operation policies of traditional production-inventory systems and traditional service systems, to promote an integrated operation policy analysis of PSS offering. We formulate customer's perceived value of PSS as the summation of product value and service value. Then the perceived value is compared to a benchmark value to figure out the customers' purchasing behavior, so as obtaining the steady statement of the demands. Subsequently, we defined an optimization problem to determine the dynamic service policy, namely service capacity allocation, to maximize the enterprise's revenue. We adopt a numerical study, which shows that the revenue is a convex function to demand and service capacity and we can obtain the optimal service policy when demands are given. Our study shed light on the integrated analysis of balancing PSS demand and service capacity, as well as promote a new way to analyze the value format of PSS. [ABSTRACT FROM AUTHOR]

The sales of new energy vehicles (NEVs) and the construction of charging infrastructure promote and constrain each other. It is crucial for the development of the new energy vehicle industry to understand the gap clearly and accurately between the supply and demand of NEV charging infrastructure. In this paper, a neural network combined model based on convolutional neural network (CNN) and long and short-term memory (LSTM) is introduced for accurate prediction of NEVS sales and charging infrastructure ownership. Compared with other traditional and combined models, the CNN-LSTM combined model performs best in multiple evaluation metrics while using less computing power. The RMSE, MAE, MAPE, and R2 of the CNN-LSTM combined model were 52.80, 42.67, 17 %, and 0.78, respectively. Accordingly, it is sufficient to demonstrate the excellent prediction performance of the CNN-LSTM combined model constructed in this paper. The forecast results show that in 2025, the ratio of NEVs to public charging piles will rise to 10.2:1 and the ratio to private charging piles will fall to 2.5:1. The overall ratio shows a downward trend and is expected to reach 2:1. There is a gap in the demand for NEV charging infrastructure. Finally, this paper makes suggestions for narrowing the gap between the supply and demand of NEV charging infrastructure and the sustainable development of the NEV industry. [ABSTRACT FROM AUTHOR]

Abstract In tribute to Jules Dupuit, this study analyzes how the innovative financing tax for development has influenced the competition among airline companies and to what extent it affected their sales. To do so, we specify an econometric model where the representative consumer chooses the utility maximizing alternative among the differentiated products offered by the airlines which compete in terms of prices. It is a fairly reasonable and well accepted representation of the competition in the airline industry, assuming that the structure of their networks is given in the short run. The present solidarity tax has increased the average price for the European airlines by only 0.08 % and could increase average prices by 1 % if it were applied to all European airlines. Implemented by all countries it would lead to a 0.17 % increase in Air France prices while the price increase would be 1.05 % for the other airlines in our sample. These numbers compared to the growth rate of the air traffic show that the impact of the solidarity tax would be very small. Overall the air ticket levy applied by all European airlines would not affect significantly the degree of competition among airlines. Highlights • Analysis of how the innovative financing tax for development has influenced the competition among airline companies. • Estimation of a differentiated product oligopoly model of the European Airlines Industry. • Small impact of the solidarity tax on the degree of competition among airlines. [ABSTRACT FROM AUTHOR]

Agricultural production is among the industries with the highest impact on the environment in terms of greenhouse gas (GHG) emissions, especially in the production of ruminant meats. Households can change their food consumption habits so as to consume less polluting products such as white meat or vegetable-based food. We analyze whether or not a CO 2 equivalent (CO 2 -eq) tax policy in France can change household habits with respect to animal product purchases, and their environmental impact. Using two levels of a CO 2 -eq tax (€56 and €200 per tonne of CO 2 -eq) applied to the consumption of all animal products, only ruminant meats or only beef, we show that a high level of tax does not allow meeting the 20% objective threshold of GHG emissions reduction for 2020 since it would lead to a 6% decrease in GHG emissions only. Despite the weak effect of such a tax, the most efficient scenario would be to tax the consumption of beef only at a high level. Indeed, this tax policy would allow reaching a 3.2% decrease in GHG emissions, that represents 53% of the variation in GHG emissions when all products are taxed whereas it would only generate 12% of the household welfare damages. [ABSTRACT FROM AUTHOR]

The analysis of increasingly large and diverse data for meaningful interpretation and question answering is handicapped by human cognitive limitations. Consequently, semi-automatic abstraction of complex data within structured information spaces becomes increasingly important, if its knowledge content is to support intuitive, exploratory discovery. Exploration of skill demand is an area where regularly updated, multi-dimensional data may be exploited to assess capability within the workforce to manage the demands of the modern, technology- and data-driven economy. The knowledge derived may be employed by skilled practitioners in defining career pathways, to identify where, when and how to update their skillsets in line with advancing technology and changing work demands. This same knowledge may also be used to identify the combination of skills essential in recruiting for new roles. To address the challenges inherent in exploring the complex, heterogeneous, dynamic data that feeds into such applications, we investigate the use of an ontology to guide structuring of the information space, to allow individuals and institutions to interactively explore and interpret the dynamic skill demand landscape for their specific needs. As a test case we consider the relatively new and highly dynamic field of Data Science , where insightful, exploratory data analysis and knowledge discovery are critical. We employ context-driven and task-centred scenarios to explore our research questions and guide iterative design, development and formative evaluation of our ontology-driven, visual exploratory discovery and analysis approach, to measure where it adds value to users’ analytical activity. Our findings reinforce the potential in our approach, and point us to future paths to build on. [ABSTRACT FROM AUTHOR]

Urban air mobility (UAM) is a novel concept that is revolutionizing urban transportation. This transformation is largely due to the development of electric vertical take-off and landing (eVTOL) vehicles, which have made UAM a reality in the urban region. The success of this emerging mode of transportation is largely dependent on market demand. However, there is a lack of systematic reviews on demand analysis for UAM. To address this gap, we conducted a comprehensive review of the recently published literature on demand analysis for UAM. We firstly identified the demand for UAM in various types of on-demand applications, including passenger services, cargo services, and other services. Secondly, we discussed and identified the factors that influence the market demand in UAM, such as time, cost, distance, congestion, safety, privacy, and noise. Additionally, we examined the existing qualitative and quantitative methods for demand analysis in UAM. We found that the most common techniques include stated-preference surveys, discrete choice models, and clustering algorithms. We further discussed the role of market demand in the UAM life cycle, highlighting the potential impacts of demand on the development, implementation, and regulation of UAM systems. Finally, we concluded our review by highlighting several opportunities for future research related to demand analysis for UAM, and these include feasibility of air shuttle services, potential cargo applications, public acceptance, infrastructure placement, integration with existing transportation, and novel demand estimation methods, while addressing various aspects of UAM life cycle, including vehicle technology, infrastructure, airspace, and operation management. • The success of Urban air mobility (UAM) is largely dependent on market demand. • First study to review research on UAM from a market demand perspective. • We examine the qualitative and quantitative methods for demand analysis in UAM. • We discuss the role of market demand in the UAM life cycle. • Opportunities for future research related to UAM demand analysis are identified. [ABSTRACT FROM AUTHOR]

• DiD LA/QUAIDS model assesses the impact of euro on consumers in the Eurozone. • The model is estimated using EU consumer expenditure panel data from 1996 to 2022. • Consumer's welfare dropped approximately 1.13% ∼ 2.16% annually after Euro adoption. This research uses a Quadratic Almost Ideal Demand System, combined with a Difference-in-Differences model with time-varying staggered treatments, to assess the impact of the European common currency on consumers' welfare. Based on a panel of 29 European Union member countries from 1996 to 2022, results indicate that consumers in the Eurozone lost between 1.13% and 2.26% in annual per capita consumer welfare after adopting the euro. [ABSTRACT FROM AUTHOR]

Summary National rice development strategies in Africa are often supply-focused and implicitly assume that consumers will readily substitute imported for domestic rice. However, due to increasing import dependency, urban consumer preferences for rice have become biased toward Asian export quality standards, against which African rice has difficulties to compete. Anecdotal evidence suggests that this import bias is higher in cities close to the port and remote from the geographical centers of cultural heritage where African rice was domesticated more than 3,000 years ago. We purposely select a sample of five West African urban markets which are supplied by both foreign and domestic rice value chains and which are located at varying distances from the nearest ports and centers of cultural heritage, and conduct framed field experiments based on auctions to analyze the drivers of urban demand for domestic rice with upgraded quality characteristics. We find that West African rice has increasing difficulties competing against imported rice on urban markets the more consumers appreciate characteristics of imported Asian rice, the closer to the port, and the further the geographical and genealogical distance from rice cultural heritage. These challenges provide crucial insights into value chain upgrading in policy makers’ struggle to achieve rice self-sufficiency in West Africa. Our findings suggest that the optimal portfolio of investment in value chain upgrading is a function of the targeted end-market and its distance from the port and rice cultural heritage. The closer the end-market is located toward the port, the more investment is needed in lifting demand of domestic rice through quality upgrading, branding, and promotion to enable it to compete against imported rice. Proximity to centers of cultural heritage, on the other hand, endows rice value chains with a “comparative advantage in demand,” requiring less investment in demand-lifting and leaving more room for supply-shifting investments. [ABSTRACT FROM AUTHOR]

Abalone (Haliotis spp.) is a highly demanded product due to its quality. Although abalone production volume is not as high relative to other seafood products, it is highly valued; it amounted to nearly 2.6 billion dollars in 2017. Previously, global abalone production depended primarily on fisheries, but now days it relies mostly on aquaculture; today, over 95% of abalone production comes from aquaculture. Several factors govern the price of abalone in the international market, including production and export volumes, among others. Therefore, it is of interest to elucidate the factors that drive abalone supply and demand. We used exports and imports data of the major abalone producers to investigate the market behavior using non-parametric statistical analyses and historical and current abalone production data worldwide. Our findings confirm the existence of significant correlations between the various mechanisms governing the abalone demand, supply, and price. It was found that the supply behavior (catch-price) of Mexican and Australian abalone was adjusted to the bioeconomic model of the backward-bending supply curve for fisheries, due to overexploitation or the establishment of catch quotas. • The catch of abalone has an inversely proportional effect on price, conforming to the backward-bending supply model. • In general, exports (demand) of abalone have an inversely proportional effect on price, showing the classical demand curve. • Farmed abalone production has a directly proportional effect in the international price. • For China, the coefficient of price elasticity of demand for abalone is greater than one, showing that it is a luxury item. [ABSTRACT FROM AUTHOR]

The potential of smart-card transactions within bike-sharing systems (BSS) is still to be explored. This research proposes an original offline data mining procedure that takes advantage of the quality of these data to analyze the bike usage casuistry within a sharing scheme. A difference is made between usage and travel behavior: the usage is described by the actual trip-chaining gathered with every smart-card transaction and is directly influenced by the limitations of the BSS as a public renting service, while the travel behavior relates to the spatio-temporal distribution, the travel time and trip purpose. The proposed approach is based on the hypothesis that there are systematic usage types which can be described through a set of conditions that permit to classify the rentals and reduce the heterogeneity in travel patterns. Hence, the proposed algorithm is a powerful tool to characterize the actual demand for bike-sharing systems. Furthermore, the results show that its potential goes well beyond that since service deficiencies rapidly arise and their impacts can be measured in terms of demand. Consequently, this research contributes to the state of knowledge on cycling behavior within public systems and it is also a key instrument beneficial to both decision makers and operators assisting the demand analysis, the service redesign and its optimization. [ABSTRACT FROM AUTHOR]

Projected population growth and urbanization will become a challenge for finite natural resources, their distribution and local availability. At the same time, 2.5 billion people do not have access to sanitation facilities. Indonesia is one of these rapidly growing countries with a poorly developed municipal wastewater and solid waste sector. Without an integrating concept to recover and reuse resources, “waste flows” are discarded and their potential value is ignored. Therefore, the Indonesian backlog may be an opportunity, since it allows for direct introduction of a circular resource approach. To foster a sustainable municipal wastewater and solid waste management, the 20 years’ demand forecast of recoverable resources (phosphorus, compost, duckweed, plastic and paper) was analyzed. Phosphorus, compost and duckweed analysis was based on nutritional demand and not on market demand. Demand for recoverable plastic and paper related to the potential substitution of conventionally manufactured products. Phosphorus and compost demand analysis was based on (1) fertilizer requirements of 68 crops (staple food, horticulture and plantation), and (2) anticipated increase in production area of these crops. Duckweed demand as a protein-rich fish feed was analyzed based on the forecasted demand from aquaculture (tilapia and carp). The potentially recoverable (waste) plastic and paper to substitute conventional manufactured products were based on extrapolation of past trends in plastic and paper production in Indonesia. The potential contribution of recoverable products to the forecasted demand for 2035 was assessed for phosphorus (15%), compost (35%), duckweed (7%), plastic (66%) and paper (18%). A geographical discrepancy between potential recovery and demand location for phosphorus and compost was found. Therefore, the locations of potential markets should be considered in the planning and selection of wastewater and solid waste facilities. The presented methodology to assess the potential demand for recoverable resources from wastewater and solid waste may be applied in other countries as well. [ABSTRACT FROM AUTHOR]

Liquefied Natural Gas, LNG, is known to be an innovative alternative fuel technology. However, there are severe barriers which impede the application of LNG in the landlocked areas of Europe. Above all, the necessary infrastructure which could stimulate the spacious application of LNG is still lacking there. The objective of the present work is therefore to build a basement for launching LNG in this region. This will be carried out in two stages: First by deliberately examining the potential demand for LNG to see if the construction of LNG infrastructure is viable in this region and second by identifying the stakeholders which have to be taken into account to successfully accomplish this process. Therefore, a demand analysis has been carried out by interviewing 15 potential Austrian pioneer users with capacious vehicle fleets. Their requirements towards LNG application have been categorized and prioritized using Maslow's hierarchy of needs. Finally, a stakeholder matrix evaluates crucial stakeholders and gives an indication of how to deal with them appropriately. The results reveal that interest for LNG exists and the potential for introducing LNG in landlocked Europe is given. [ABSTRACT FROM AUTHOR]

First and last mile connectivity of public transport hubs is a key component in promoting multi-modal travel. The Dutch train station operator (NS Stations) promotes the combination of bike and train by offering a train station-based round-trip bikesharing (SBRT) scheme, known as 'OV-fiets', located at train stations throughout the country. This scheme allows users to rent a bike to travel between train stations and their destination and vice versa. The round-trip nature of the SBRT makes it unique in comparison to widely applied one-way bikesharing schemes. Little is known about the determinants of demand for round-trip bikesharing, especially when being integrated into an existing PT scheme. This paper aims to fill this gap by identifying potential temporal and weather-related determinants for SBRT-rentals of the Dutch SBRT-system using multiple linear regression (MLR) and an in-depth analysis for selected stations. The results are compared with the findings of one-way bikesharing schemes. The results show that for hourly rentals in an SBRT-system, the highest explanatory power is attributed to the number of train travelers leaving the corresponding train station, followed by temporal and weather-related determinants. Furthermore, the magnitude of the correlation between the determinants and the hourly demand varies considerably across stations, depending on the underlying demand patterns. [ABSTRACT FROM AUTHOR]

Carsharing has become an important addition to existing mobility services over the last years. Today, several different systems are operating in many big cities. For an efficient and economic operation of any carsharing system, the identification of customer demand is essential. This demand is investigated within the presented research by analyzing booking data of a German free-floating carsharing system. The objectives of this paper are to describe carsharing usage and to identify factors that have an influence on the demand for carsharing. Therefore, the booking data are analyzed for temporal aspects, showing recurring patterns of varying lengths. The spatial distribution of bookings is investigated using a geographic information system and indicates a relationship between city structure and areas with high demand for carsharing. The temporal and spatial facets are then combined by applying a cluster analysis to identify groups of days with similar spatial booking patterns and show asymmetries in the spatiotemporal distribution of vehicle supply and demand. Influences on demand can be either short-term or long-term. The paper shows that changes in the weather conditions are a short-term influence as users of free-floating carsharing react to those. Furthermore, the application of a linear regression analysis reveals that socio-demographic data are suitable for making long-term demand predictions since booking numbers show quite a strong correlation with socio-demography, even in a simple model. [ABSTRACT FROM AUTHOR]

I analyse whether the introduction of the unhealthy food tax lead to significant improvements in the dietary habits of the Hungarian population. I focus on the consumption of processed and unprocessed food before and after the tax was levied on a range of food high in salt and sugar. Using data from a large scale household panel data set, I estimate the consumed quantities of processed food to decrease by 3.4% due to the unhealthy food tax, while the consumed quantities of unprocessed food increased by 1.1%. The lowest income groups were the most responsive to the introduction of the tax. Despite data limitations, the results indicate moderate improvement in population diet that is likely to be attributable to the tax. [ABSTRACT FROM AUTHOR]

The concept of ecosystem services (ES) is broadly established in research and in communities of interest. The European Commission (EU) has embraced these conceptual approaches in order to provide policy makers with decision-supportive information concerning the supply of and demand for ES. It is, however, not yet clear how ES information should be represented to fulfill decision-supportive functions or even to process the data in such a manner that it is understandable. Knowledge about the ideal representation and communication of ES information integrated into decision support systems (DSSs) is particularly key for guiding users through such systems. In order to determine the correct representation type for a given situation and intended use, we developed a demand analysis, distributed through an online survey, to identify user demands for ES information. A principal component analysis depicts that requirements were highly heterogeneous among respondents of this study. Five components describing the representation type can, however, be identified, depending on the situation of application and the intended use of the ES information by the respondents: (1) 3D landscape visualizations are preferred for analyzing and exploring ES-related information; (2) texts and abstracts are preferred for communication and discussion support; (3) thematic 2D map representations are preferred to support scenario development in public applications; (4) abstract 3D landscape visualizations facilitate estimations in group applications; and (5) charts and tables, in combination with thematic 2D map representations, support analyses. However, while certain representation types are function- and/or situation-specific, no representation type can be used as a panacea. A demand analysis, as presented in this paper, can contribute to the definition of how ES information is to be integrated into DSSs and how it needs to be designed to be (decision-) supportive. [ABSTRACT FROM AUTHOR]

Highlights: [•] Techno-economical and social analysis of energy storage is conducted for commercial buildings. [•] Methodologies for demand analysis, technical, economical and social evaluations are developed. [•] An illustrative example is analyzed for three kinds of energy storage systems. [Copyright &y& Elsevier]

Abstract: The consumption of meat from wild animals (or bushmeat) occurs throughout Africa and highlights the conflict between two distinct development goals: food security and biodiversity conservation. Growing human populations throughout the greater Serengeti ecosystem rely heavily on bushmeat as a source of protein, which places pressure on migratory wildlife populations. This paper uses unique data from protein consumption surveys from 131 households over 34months in a generalizable empirical framework to estimate price, cross-price, and expenditure elasticities of protein sources, and analyze the potential economic effects of policies to mitigate bushmeat hunting and consumption. Results suggest that: (1) directly increasing the price of bushmeat through enforcement or other policies to reduce supply will have the most direct and largest effect of bushmeat consumption; (2) increasing income increases bushmeat consumption as well as consumption of other meat sources; (3) if surrounding fisheries experience a negative shock, or collapse, this will lead to a dramatic increase in bushmeat consumption. Overall, these results strongly indicate that policies to reduce bushmeat hunting while maintaining food security must be considered in a broad and comprehensive framework. [Copyright &y& Elsevier]

Abstract: Tourism demand has been extensively studied by tourism economists under the economic framework. On the other hand travel motivation has also been widely analyzed by the researchers using socio-psychological framework. The focus of this study is to discuss both of these frameworks and to propose a combined framework which illustrates that the root of tourism market demand could be found in both economic and socio-psychological theories and that market demand could be derived from factors underpinning both theories—theories of destination choice and theory of market demand. Climate as an important socio-psychological variable is incorporated into tourism demand analysis through error correction model. The results shed new lights to the potential of the proposed framework in revealing tourism demand. [Copyright &y& Elsevier]

Abstract: In this paper we present a non-linear demand system for households’ joint choice of number of trips and days to spend at a destination. The approach, which facilitates welfare analysis of exogenous policy and price changes, is used empirically to study the effects of an increased CO2 tax. In particular, we focus on the effect of including substitution between households choice of the number of trips and days to spend at a destination in the welfare analysis. The analysis reveals that the equivalent variation (EV) measure, for the count data demand system, can be seen as an upper bound for the households welfare loss. Approximating the welfare loss by the change in consumer surplus, accounting for the positive effect from longer stays, imposes a lower bound on the households welfare loss. The difference in the estimated loss measures, from the considered CO2 tax reform, is about 20%. This emphasizes the importance of accounting for substitutions toward longer stays in travel demand policy evaluations. [Copyright &y& Elsevier]

Abstract: Transportation problems are important complex systems because of the increased number of vehicles in cities. In this paper, we study time series of vehicle demand by using the ferry-boat system between Salvador city and Itaparica island, in Bahia, Brazil. We compare the traditional demand analysis (ARIMA method) with the self-affine ones (the scaling exponent and the density of crossing points ). In addition, taking into account the inherent self-affine behavior we study the stationary states of this dynamic process by using a nonlinear Fokker–Planck equation. The present findings indicate that the scaling exponent describes some properties of flux of vehicles using the ferry-boat system. The behavior of gives an alternative explanation about demand analysis, and the nonlinear Fokker–Planck equation presents a solution close to the stationary behavior of this complex dynamical analysis. [Copyright &y& Elsevier]

Abstract: As an innovative tool, weather index agricultural insurance (WIAI) was introduced into China in 2008. Before product designing and selling to farmers, it is necessary to understand farmers’ real desire so that the product can be tailored to fit their needs. This paper focuses on the demand analysis for WIAI on household level. With this goal, 660 households located in 22 administrative villages in Changfeng and Huaiyuan county, Anhui province, China were surveyed. Survey questions covered risks faced by households, their coping mechanisms and willingness to pay for WIAI. Survey data were assessed and findings are shown as below: drought and flood were major weather risks for crops; the key coping strategies to crop loss were seeking employment elsewhere, or borrowing money from relatives or friends; most of investigated farmers showed interest in WIAI; those who were more familiar with insurance, or whose losses were higher due to deficient or excessive rainfall, or who had more trust in the accuracy of local weather forecasts, expressed more interest in WIAI with the quite low correlation coefficient, while statistically significant. It hints that there are potential demands for WIAI among farmers. However, methodologies deserve further development so as to provide more reliable assessment of the relationships between the willingness to pay and the abovementioned three factors. [ABSTRACT FROM AUTHOR]

Abstract: Abstract Cultural transformation is needed to allow the world’s population to make the right decisions for dealing with greenhouse gases. The authors have conducted an explorative study into the social representations and cultural models that could facilitate or hinder the necessary decisions for the implementation of Carbon Capture and Storage (CCS). Some key cultural factors have been identified with regard to the role of scientists, the relationship with the authorities, and the connection among the different social institutions. In addition it is highlighted how the research approach used can interact with and foster a new cultural representation of environmental problems and solutions. [Copyright &y& Elsevier]

After the rapid expansion in the early stage, many enterprises have closed down or withdrawn from the Free-floating bike sharing (FFBS) market, the remaining few giants are also generally at a loss at present. One main reason causing these is the serious FFBS imbalance between supply and demand. As there is no fixed docking station, the individual-based identification method such as trip-chain, which is used for identifying travel demand and behaviour in traditional station-based bike sharing (SBBS) cannot be used in FFBS. Therefore, the lack of methods to obtain in-depth demand makes it unable to achieve reasonable rebalancing. This study constructs an individual-based spatio-temporal travel demand mining methodology, which is the first disaggregate travel demand mining model that suitable for FFBS. The proposed methodology consists of three steps. A spatio-temporal trajectory clustering algorithm is first developed to obtain an individual's frequent trajectory clusters, and then a sequential pattern mining algorithm is applied to users who have multiple spatio-temporal trajectory clusters to extract travel patterns and trajectory sequential relations in patterns. A point clustering method is used finally to identify spatial relationships among different trajectory clusters. Besides, a zone aggregating method is proposed that aggregated granularity could be flexible adjusted for zone demand imbalance analysis. Based on these, how to utilize identified frequent pattern trajectories to improve rebalancing is studied. The proposed methodology is applied to Beijing Mobike dataset, six frequent travel patterns are mined out and analyzed in detail. On this basis, imbalance and rebalancing analysis are carried out with the case study at last. Consequently, this research contributes a powerful tool to achieve accurate FFBS demand analysis and rebalancing. [ABSTRACT FROM AUTHOR]

• The analysis splits customers into clusters based on the consumption behavior. • The disaggregated analysis shows the behavior and resilience of various sectors. • The impact of the COVID-19 measures is evaluated at short and mid terms. • The proposed approach is able to show which sub-sectors have been the vulnerable. Since the emergence of the virus that causes COVID-19 (the SARS-CoV-2) in Wuhan in December 2019, societies all around the world have had to change their normal life patterns due to the restrictions and lockdowns imposed by governments. These changes in life patterns have a direct reflection on energy consumption. Thanks to Smart Grid technologies, specifically to the Advance Metering Infrastructure at secondary distribution network, this impact can be evaluated even at the customer level. Thus, this paper analyzes the consumption behavior and the impact that this crisis has had using Smart Meter data. The proposed approach includes the selection and normalization of features, automatic clustering, the obtaining of the estimated consumption without considering the crisis (at short and mid-terms) and the impact evaluation. The proposed approach has been tested on a case with a real Smart Meter infrastructure from Manzanilla (Huelva, Spain). The results of this use case showed that residential customers have increased their consumption around 15% during full lockdown and 7.5% during the reopening period. In contrast, globally, non-residential customers have decreased their consumption 38% during full lockdown and 14.5% during the reopening period. However, referring to non-residential customers, five different consumption profiles were found with different short-term and mid-term behaviors during the COVID crisis. The different behavior found shows customers who have maintained their normal consumption during the lockdown, others who have reduced it (to a greater or lesser extent) and have not recovered it after the removal of the restrictions, and others who have reduced the consumption but then they recovered it when the restrictions were removed. The metadata of the customers in each behavior cluster found are highly correlated to the restrictions imposed to control the spread of the virus. This study shows evidence about the proposed approach usefulness to analyze the behavior and the impact at customer level during the COVID-19 crisis. [ABSTRACT FROM AUTHOR]