Search

Using a monthly data between 2008 and 2019, this study investigated how the development of the Internet technologies has shaped the consumer consumption pattern of different types of paper products in China during this period. In detail, this study covers four major types of paper products in the Chinese market: newsprint, printing paper, household paper, and packaging paper. The developments of Internet technologies were decomposed into two aspects: the communication technology (CT) and the software platform technology (PT). Granger causality analysis was employed to determine whether the consumption pattern of paper products was affected by the development of the Internet. Furthermore, impulse-response analyses were utilized to measure how the consumption pattern of each paper product was shaped by the development of each aspect of Internet technologies, i.e., CT or PT. The empirical results suggested that the Internet developments are causing the consumption patterns shifts of all four paper products. Specifically, the consumptions of newsprint and printing paper were driven by both CT and PT. However, the consumptions of packaging paper and household paper were driven by the PT only. Compared to the development of CT, we found that the quick evolution of PT plays a more significant role in shifting people's consumption of paper products in China. The empirical results revealed by this study could provide valuable economics and policy implications to both academia and relevant industries. • Internet development has shifted the paper products consumption pattern in China. • Development of internet boosted the demand for printing paper and household paper. • Development of internet suppressed the demand for newsprint. • The effects of internet technological development are separated into two aspects. • Emerging Internet platforms shifted the consumption of paper products efficiently. [ABSTRACT FROM AUTHOR]

[Display omitted] • Furfural and HMF are genotoxic and carcinogenic by-products of thermal degradation. • Developed a method to detect furfural and HMF in beverages and fruit juices. • Translated the method into chromogenic test-strips for point-of-use applications. • The method sensitivity is 0.006 and 0.005% (v/v) for furfural and HMF, respectively. Improper storage and transportation of non-alcoholic beverages can, over longer periods, induce Maillard reaction, degrading nutritional components and generating genotoxic and carcinogenic by-products such as furfural and 5-hydroxymethylfurfural (HMF), rendering products unsafe for human consumption. Here, we describe a rapid quantitative solution-based method and test-strips for detection of furfural and HMF. The standard spectroscopic method indicated an LOD of 0.006 ± 0.003% (v/v) and 0.005 ± 0.002% (v/v) for furfural and HMF, respectively in fruit juice samples. The novel chromogenic test-strip has sensitivity of 0.008% (v/v) and 0.004% (v/v) for furfural and HMF, respectively in the same samples of fruit juice. Thus, the developed method and test-strips were specific for furfural and HMF and can be used to help ensure food safety and quality in various industrial applications [ABSTRACT FROM AUTHOR]

Optimizing domestic energy use and increasing the efficiency of residential power supply chains depend much on home energy management (HEM) systems. This paper examines the utilization of advanced meta-heuristics, namely the Siberian Tiger Optimization (STO) and Sand Cat Swarm Optimization (SCSO) algorithms, for developing HEM systems. The paper presents an integrated STSC algorithm, enhanced by artificial intelligence, that monitors and optimizes household energy usage. To optimize electricity distribution, this algorithm seeks a compromise between reducing costs and lowering the peak-to-average proportion of power. After extensive simulations, the STSC algorithm outperforms previous meta-heuristics in Peak Average Ratio. It demonstrates the possibility of substantial cost reductions in residential settings, reaching up to 8.5%. This improves the overall efficiency of households' power supply chains. In addition to reducing costs, the STSC algorithm contributes to sustainability objectives by utilizing AI to minimize carbon emissions, including renewable energy sources, and facilitate adaptable demand solutions. This highlights its role in promoting sustainable supply chain practices in energy efficiency. The combined use of STO and SCSO algorithms in the STSC method is a new and innovative development in HEM systems. This study highlights the capacity of AI-driven technologies to effectively and environmentally optimize household energy consumption. [ABSTRACT FROM AUTHOR]

Poverty measurement using durable ownership data is an attempt to infer income constraints by observing consumption choices. But what drives household spending choices on durable goods? How do these choices relate to poverty and class? What does it mean to be 'relatively' poor and why should we care to measure it? In this paper, we propose an economic theory of household decision-making that links these questions using a novel wealth-begets-wealth mechanism. We show that the steady state distribution of total (accumulated) household durable expenditures in this model exhibits natural clusters (classes). Furthermore, certain households may be vulnerable to a long run 'poverty of opportunities', being unable to access any of the channels of income generation available in society. Our model shows that relative poverty can be understood as the endogenous outcome of an intergenerational process that perpetuates unequal access to opportunities. This finding has novel implications for the measurement of poverty, which has traditionally hinged on definitions that assume exogenous (often arbitrary) cutoffs. The contribution of this paper also lies in its novel methodology, viz., formulating a theoretical model as the foundation of a data-generating process for synthetic observations, using patterns observed therein to inform the process of poverty measurement. The methodology delivers a framework for generating testable hypotheses around the long-run effect of policy changes (such as income transfers or education subsidies) on relative poverty – an approach that can be applied generally to understand the observed behaviour of economic agents in complex dynamic settings. [ABSTRACT FROM AUTHOR]

• The paper analyzes long-term impacts of electoral violence on health and education. • Areas exposed to violence in Kenya suffer inflation and fall in annual consumption. • Children and adolescents exposed to violence have stunted height as adults. • The sons of those exposed to violence during growing age also have stunted growth. • No long-term impact on education is found as violence peaks during school holidays. This paper examines whether prenatal, childhood, and teenage exposure to electoral violence has any long-term effects on height and human capital in Kenya. First, using the difference-in-difference estimator, we show that areas impacted by electoral violence see increases in local food prices and a decrease in annual nightlight, suggesting important local consumption shocks. We then identify people exposed to such violence using a nationally representative survey and take advantage of the temporal and spatial variation of electoral violence in Kenya between 1992 and 2013. Using coarsened exact matching, we find that exposure to electoral violence between prenatal and the age of sixteen reduces adult height. Previous research has demonstrated that protracted, large-scale armed conflicts can cause significant nutritional shocks and pass down stunting effects to descendants. In line with these studies, we find that the low-scale but recurrent electoral violence in Kenya has impacted the height-for-age of children whose parents were exposed to such violence while they were growing up. Only boys exhibit this intergenerational effect, possibly due to their increased susceptibility to malnutrition and stunting in Sub-Saharan Africa. The fact that most electoral violence in Kenya occurs over the school holidays may help explain why, in contrast to previous studies on large-scale conflicts, childhood exposure to electoral violence has no lasting impact on educational attainment. [ABSTRACT FROM AUTHOR]

Energy consumption forecasting plays a pivotal role in modern resource management and sustainable development. This paper presents a concise overview of state-of-the-art techniques and methodologies employed in the field of energy consumption forecasting, with a particular emphasis on the application of Machine Learning (ML) models. The paper surveys recent advancements, addresses key challenges, and identifies promising directions for future research in this critical domain. By examining the current landscape of energy consumption forecasting through the lens of machine learning, this review aims to offer researchers and practitioners valuable insights and guidance for enhancing the accuracy and efficiency of energy consumption pattern prediction. [ABSTRACT FROM AUTHOR]

This paper examines the impact of digital financial inclusion (DFI) on household energy poverty. We develop a simple theoretical model to explain the energy poverty alleviation effect of DFI. Using multidimensional household energy consumption survey data, we find that DFI reduces the incidence of household energy poverty by about 33.5 % and the intensity of energy poverty by 24.6 %, driven mainly by improvements in energy efficiency and an increase in household off-farm work, resulting in a stronger role in rural and low-income groups. However, the threshold model demonstrates that the Internet and digital tools are a prerequisite for this pro-poor energy effect to be realized. Notably, DFI, while exhibiting spatial spillover effects, exacerbate energy poverty in neighboring areas due to the siphoning effect. [ABSTRACT FROM AUTHOR]

Nowadays, the increasing consumption of conventional fuels has led to rising pollutant emissions, among which soot has received much attention due to its harmful effects. In this paper, previous typical soot models are reviewed. Based on empirical, semi-empirical and detailed models, phenomenological models, the most popular models of recent years, are also summarized. This work also summarizes the application of existing soot models in combination with other models and methods in turbulent flame and diesel engine development based on existing models. Besides, as the development of clean alternative fuels continues, new fuels have received significant attention as a method to reduce soot formation. For different fuels, the reasons for reducing soot formation have also attracted the interest of researchers, and thus this paper reviews the current research about relevant carbon soot modeling for biodiesel, natural gas, and other new fuels. A detailed understanding of soot generation and soot modelling can help further research into methods to reduce soot emissions. [Display omitted] [ABSTRACT FROM AUTHOR]

• Unveiling insights into the Chinese low consumption puzzle by emphasizing the impact of monopolistic and strategic land supply on the final consumption rate. • Monopolistic land supply suppresses the final consumption rate by expanding government income share and widening urban-rural income gaps. • Strategic land supply suppresses the final consumption rate by driving up the share of capital income and housing prices. • Reducing land transaction monopolies and optimizing supply structures can yield substantial benefits for economic rebalancing. The challenge of low consumption presents a complex development issue in China, one that has been insufficiently analyzed through the lens of land supply. This paper introduces a fresh perspective to the Chinese low consumption puzzle by shedding light on the constraints imposed by monopolistic/strategic land supply on the final consumption rate. Our findings reveal that China's distinctive land supply patterns systematically restrict consumption, as monopolistic land supply hinders total consumption by widening the share of government sector income and the urban-rural income gap. Additionally, strategic land supply curtails consumption by increasing the share of capital income and driving up housing prices. This study underscores the significance of land supply-side reforms in bolstering consumption, offering valuable policy insights for China and other transitioning economies. [ABSTRACT FROM AUTHOR]

The hydrogen/electric vehicle charging station (HEVCS) is widely regarded as a highly attractive system for facilitating the popularity of hydrogen and electric vehicles in the future. However, conventional optimal dispatch of HEVCS could lead to poor performance due to the lack of adequate consideration of vehicle charging decision behaviours and neglection of the impacts of different information sources on it. This paper investigates a charging demand prediction method that considers multi-source information and proposes a multi-objective optimal dispatching strategy of HEVCS. First, an information interaction framework of integrated road network, vehicles and HEVCS is introduced. Road network model and HEVCS model are established based on the proposed framework. To improve the flexibility of dispatch, two charging modes are designed, which are intended to guide drivers to adjust their consumption behaviour by electricity price incentives. Furthermore, psychologically based hybrid utility-regret decision model and Weber-Fechner (W–F) stimulus model are developed to reasonably predict drivers' choice of charging stations and charging modes. The daily revenue of HEVCS and the total queuing time of drivers are the objective functions considered in this paper simultaneously. The above multi-objective optimization results that the proposed strategy can effectively improve the benefits of HEVCS and reduce energy waste. Additionally, this paper discusses the results of a sensitivity analysis conducted by varying incentive discount, which reveals the combined benefits of the HEVCS and the vehicles are effectively increased by setting reasonable incentive discounts. • A framework of multi-source information interaction is established. • Two charging modes are designed by means of price incentive. • The models of charging decision prediction based on psychology are presented. • Both the energy units and EVs are optimally dispatched in HEVCS. [ABSTRACT FROM AUTHOR]

The purpose of the paper is to examine the factor that influence the productivity in the EU Member States. In the paper a multi-level analysis was applied. The research showed that the systematic transformation of the economy towards modern branches in favor of the traditional ones caused an increase in productivity. On the other hand, it is impossible to completely replace the former. Hence, the most appropriate direction seems to be a gradual increase in efficiency in underdeveloped sectors of the economy without abandoning them entirely. What is especially interesting, it was found that positive changes in final consumption expenditure of general government negatively affect productivity. Thus, there is a dilemma related to the scope of the impact of fiscal policy and regulations in the economy. [ABSTRACT FROM AUTHOR]

In this paper, a real-time chemi-thermomechanical pulping (CTMP) process was investigated, where opportunities were explored to increase energy consumption efficiency with a guarantee of expected pulp properties as a priority. Due to the process instrumentation limitations, such as the lack of advanced regulatory controllers and optimization units, the advanced process controllers and real-time optimization strategies can hardly be implemented in the existing CTMP processes. Therefore, a novel visualization-based process evaluation methodology is proposed to aid the mill operators in making further decisions to improve the process operations. Additionally, to determine the desirable pulp properties for production, an inferential sensor model is developed that estimates the handsheet properties from pulp properties. Also, to assess the model prediction accuracy in real-time, a reliability index model is designed. The proposed methodologies have been validated on a practical CTMP process, and the results show the satisfactory performance of the system. • Developed a strategy to evaluate the CTMP performance using visualization methodology. • A novel inferential sensor model is implemented to determine the desirable pulp properties. • A reliability index model classifying reliability levels and assessing model accuracy is developed. • The proposed model adapts to model updates, uncertainties, and computation resource limitations. [ABSTRACT FROM AUTHOR]

The main contradictions in Chinese society have changed as China enters a new era, and the people's pursuit of the better life has become more intense. This study explores how energy poverty can be addressed from a digital perspective in the context of the booming digital economy. This study analyzed the impact of mobile phone use on energy poverty by constructing an OLS model using micro data from Tibetan farming and pastoral areas. We also conduct tests for endogeneity, robustness, heterogeneity, and influence mechanisms. The results indicate that mobile phone use has a significant negative impact on energy poverty, and the results remain robust after instrumental-variable regression. In terms of economic utility, mobile phone use reduces energy poverty by increasing household consumption or off-farm income. In terms of social utility, mobile phone use can also alleviate energy poverty by raising social capital. Heterogeneity analysis shows that mobile phone use contributes to energy poverty alleviation in a way that benefits the poor and the less educated. The authors investigate the impact of mobile phone use on Energy Poverty, a topic that has previously been little addressed in academia. The findings of this paper enrich the literature on the digital technology and energy poverty and provide a new perspective for alleviating energy poverty in rural areas and improving farmers' well-being. Finally, this paper offers some policy recommendations. • The use of mobile phones by farmers and herdsmen can alleviate energy poverty. • mobile phone use contributes to energy poverty alleviation in ways that benefit the poor and the less educated. • The social and economic utility brought by the use of mobile phones can help farmers and herdsmen alleviate energy poverty. [ABSTRACT FROM AUTHOR]

Due to the mobile and random nature of services in cyber–physical–social systems (CPSSs), developing service composition approaches that ensure high availability, minimal energy consumption, and high quality of service (QoS) remains a complex challenge. Over the last two decades, several service composition approaches have been proposed in the literature to deal with this challenge. Nevertheless, the existing approaches have certain limitations, particularly in situations where services may move from one location to another, become unavailable due to intensive battery usage, encounter failures, or undergo a decline in quality. These limitations often arise because these approaches do not simultaneously integrate mobility, energy, and QoS constraints while defining the user's movement in a random manner. In this paper, the learning-based swarm optimization-aware service composition algorithm (LS-SCA) is proposed to overcome the aforementioned shortcoming. This approach surpasses existing ones by accounting simultaneously for the user's mobility, energy, and QoS criteria during the service composition process. First, the Small World in Motion (SWIM) mobility model is employed in this study to determine the user's mobility traces, avoiding the random generation of users' traces. Second, an energy consumption model is proposed to increase the energy efficiency by avoiding the overuse of the devices' batteries that can reduce the availability of services and lead to the composition failure. Third, the two-phase learning-based swarm optimizer (TPLSO) method is used in the composition process to find the sub-optimal composition that satisfies the global QoS constraints with the highest utility in terms of mobility, energy, and QoS. Unlike the most existing metaheuristic-based service composition approaches where the overall composition population is improved over a given number of iterations, the TPLSO method is exploited in this paper to improve only a subset of compositions, which reduces the composition time and increases the QoS utility of the composition. The simulation scenarios using two real datasets demonstrate that the LS-SCA approach outperforms six baselines in terms of energy consumption, QoS utility, and availability of composition. This notable performance makes the proposed approach more suitable for real-world applications where energy efficiency, QoS, and availability are crucial factors to consider. [ABSTRACT FROM AUTHOR]

China's carbon emissions from households have increased with the growth of the consumption scale. To accomplish the ambitious goals of reducing emissions, carbon emission reduction on the household side should not be neglected. Based on the 2012–2018 CFPS database, This paper examines the relationship between household carbon emissions(HCEs) and the digital economy by analyzing data on household consumption expenditure and carbon emission coefficients. Utilize instrumental variables and the "Broadband China" natural experiment to conduct robustness tests, and further clarify how the digital economy influences direct and indirect household carbon emissions. The study finds that (1) Direct HCEs witness an increase while indirect HCEs experience a decrease due to the digital economy. (2) The digital economy increases direct HCEs by expanding the consumption scale, but surprisingly, the digital economy has not made a significant impact on enhancing residents' environmental awareness. (3) When analyzing indirect HCEs, we introduced a macro perspective, and it is found that the digital economy results in a decline in indirect HCEs primarily due to a reduction in the energy intensity of the whole industry. This paper provides new evidence on the digital economy and its impact on carbon emissions at the household level, which is instructive for understanding the interaction between the production and consumption sides in achieving carbon neutrality and how to promote emission reduction on the consumption side. • Household carbon emissions are measured from direct and indirect perspectives. • The impact of digital economy on household carbon emissions is heterogeneous. • Digital economy increases household carbon emissions through consumption scale. • Digital economy has no significant impact on environmental awareness. • Digital economy reduces indirect household carbon emissions through energy intensity. [ABSTRACT FROM AUTHOR]

• This paper studies the new problem, EVs' provision of flexible ramping products (FRPs). • The dispatching capability of EV fleets is disclosed through the feasible operating region in variable spaces. • A cloud-edge collaborated market structure for the electricity market with FRPs is proposed to facilitate a large amount of EVAs integrated with protected privacy. • A distributed optimal model combining the EVA's bidding subproblem and the market clearing subproblem is established. • The distributed optimal model is solved based on the modified analytical target cascading (ATC), in which the convergence performance is improved through heuristic constraints. • Time-coupled multi-period (TCMP) operation mode is implemented in the market to maximize the dispatching potential of EV fleets. More flexible ramping service is required due to the increase of renewable power generation in power systems. Electric vehicles (EVs) could provide such flexible ramping products (FRPs) at low cost while participating in the electricity market through aggregation. However, EVs' dispatching capability cannot be fully utilized without the right incentives. This paper addresses a distributed optimal model developed between EV aggregators (EVAs) and the independent system operator (ISO). To make such concept, a cloud-edge collaborated market structure is adopted. At the edge level, EVAs assess the dispatching capability and solve the market bidding subproblem. At the cloud level, ISO solves the market clearing subproblem considering system economy and security. The overall problem is solved by the analytical target cascading (ATC) method. Heuristic constraints are also introduced into the model to improve convergence performance. The model is tested on a modified IEEE 30-bus system. Results demonstrate that the proposed method can incentivize EVAs with different owners to shift load and provide FRPs accurately, meanwhile reducing the cost and increasing the consumption of renewable energy effectively. [ABSTRACT FROM AUTHOR]

This paper addresses the conundrum of optimizing electricity consumption patterns in response to fluctuations in demand and price, a task managed by both load aggregators and the distribution system operator (DSO). The conventional approaches in the literature to integrate demand response (DR) into optimal power flow (OPF) problems typically overlook the price responsiveness of consumers or simplify power flow equations to account for price-elastic demand. In this paper, we strive to close this gap by introducing a bi-level primal-dual optimization framework that incorporates aggregators' objectives into the market-clearing process while preserving the precision of the OPF equations. At the upper level, the model seeks to minimize both the total payment and peak load. The lower level, structured as a second-order conic (SOC) problem, aims to reduce overall generation costs subject to constraints of the second-order conic (SOC) branch flow model (BFM). The two levels interact through the optimal demand response and distribution locational marginal prices (DLMPs). The principles of convex duality principles together with the strong duality constraint are leveraged to transform the market-clearing problem into a single primal-dual problem. We also circumvent the non-linearity that stems from the DR payment term by incorporating discretizing loads and the big- M method, thus converting the problem into a mixed-integer SOC (MI-SOC) formulation. The merits of the proposed MI-SOC framework are validated through case studies conducted on the IEEE 33-bus test system, showcasing its potential to enforce price-elasticity demand response in distribution system's electricity markets. [ABSTRACT FROM AUTHOR]

Steel demand forecasting is crucial for steel industries, enabling effective production planning, inventory management, resource optimization, and strategic decision-making. This also holds true for steel plant manufacturers, who require insights into future demand trends to assess market potential, evaluate project feasibility, and identify growth regions. This paper addresses the predictability of steel demand across countries and regions worldwide, employing a comparative analysis of linear and non-linear modeling approaches to forecast steel demand per capita. A global model is trained in order to integrate information from diverse countries at varying stages of development, allowing the model to learn from comparable conditions and historical patterns. For each modeling approach hyperparameter tuning and selection of lagged input variables was performed. The performance of different model configurations was evaluated and compared for individual countries. This evaluation reveals that the predictability varies across countries and regions and shows for which countries a simple regression model based on past data is sufficient and for which countries more sophisticated models are needed. The developed models are useful in providing insights into consumption patterns, comparative risk assessment, and development of steel consumption. [ABSTRACT FROM AUTHOR]

Improving energy efficiency in public passenger transport is key to achieving sustainability goals. In many developing countries, public passenger transport relies heavily on fossil fuels, which necessitates a strong focus on reducing emissions from energy consumption. Surprisingly, few studies have addressed this issue in the context of daily operations, with most research focusing on operational and infrastructural changes. This paper aims to fill this gap by introducing detailed energy consumption variables into a public transport simulation model using SUMO, a tool known for its seamless integration with Python. The development of two Python scripts underpins this endeavour. The first script facilitates the automatic generation of scenarios, allowing the manipulation of parameters such as station spacing, line length, passenger capacity and waiting times, among others. The second script is dedicated to estimating energy consumption for each scenario. Together, these tools allow us to calculate average energy consumption for different scenarios and modes of public transport. As a result, they provide valuable insights for the formulation of public policies aimed at saving energy in different public transport environments and lay the foundation for the integration and development of intelligent transport systems and simulation-based digital twins. [ABSTRACT FROM AUTHOR]

To balance the hydrogen consumption of fuel cell vehicle (FCV), the durability of the fuel cell (FC), and the life of the power battery (PB) to further reduce the whole lifecycle costs of FCV. A multi-objective reinforcement learning-based (MORL-based) energy management strategy (EMS) is proposed in this research. First, the composition mechanism of the FCV lifecycle costs is analyzed, and the equivalent hydrogen consumption model, FC durability degradation model, and PB life decay model are established; Then, a three-dimensional reward function is constructed by integrating the objectives of equivalent hydrogen consumption, FC durability degradation, and PB life decay. And the penalty terms coupled with the decay factors are introduced into the reward function to satisfy the mutual constraint characteristics between the PB and the FC system to ensure the stability of the MORL-based EMS; In addition, the prioritized experience replay technology is introduced into the MORL-based EMS to improve the learning efficiency and convergence of traditional deep Q network (DQN) algorithm; After that, the evaluation and target network of the embedded dueling network are introduced to solve the multi-objective overestimation problem encountered in the training process by generalizing the behavior learning in the presence of similar value behaviors; Finally, the performance of MORL-based EMS and DQN-based EMS is compared by numerical simulation under various driving cycles. The results show that the MORL-based EMS proposed in this paper has better convergence ability, adaptability, and lower lifecycle costs than the DQN-based EMS. In addition, the lifecycle costs of the MORL-based EMS can achieve a 99.2% control effect of the dynamic programming-based EMS. • The prioritized dueling double deep Q network-based EMS is proposed for an FCV. • Three objectives are considered in the PD3QN-based EMS. • Considering the battery life decay, EMS can better reduce the life cycle costs. • The PD3QN-based EMS can achieve a 99.2% control effect of DP-based EMS. • The PD3QN-based EMS can achieve good convergence ability and adaptability. [ABSTRACT FROM AUTHOR]

Understanding consumers' purchase behaviors is fundamental to the success of the dairy industry. With its economic importance, the Chinese market is critical to dairy producers in most countries around the world. However, understanding consumers in this market is particularly challenging, as these consumers often have a different relationship with dairy products than consumers elsewhere in the world, given the country's historical dairy-related scandals. This special relationship can be characterized by what consumer behavior researchers call "high involvement," indicating that Chinese dairy consumers often attempt to reduce the level of risk associated with buying dairy products. Surprisingly, although this relationship affects several important aspects of purchase behavior, examination of the concept of involvement in the dairy sector has not been widely considered. Of note, there is no understanding of how Chinese consumers vary in their involvement levels and their implications on their dairy purchase behaviors. Nor have there been involvement-based insights provided on how dairy companies can position their marketing strategy to suit the needs of these consumers better. Thus, this study proposes a new approach to understanding Chinese consumer dairy decisions by introducing "consumer involvement" as a segmentation tool through which individuals' behaviors can be predicated according to their involvement profile. Based on an online survey administered in Shanghai using 1,073 dairy consumers, principal component analysis confirmed involvement with dairy is a multidimensional construct with the following 4 factors: pleasure value, symbolic value, risk importance, and risk probability. A 2-step cluster analysis identified 4 consumer clusters based on their involvement profile: face-concerned dairy lover, carefree dairy consumer, cautious dairy lover, and confused dairy consumer. According to a one-way ANOVA test and cross-tabulation with χ2 test, these consumer segments behave differently in relation to the extensiveness of decision making, cue utilization, trust of information sources, and consumption behavior. The outcomes in this paper further explain why efforts to restore consumer trust for dairy products do not work among some consumers, as individuals may exhibit diverse attitudes toward such information due to their heterogeneous involvement levels. The study also provides suggestions for market practitioners and organizations to develop effective target market strategies and policies according to different consumer clusters. [ABSTRACT FROM AUTHOR]

• We present a methodology for relating traded products to aggregate country indicators. • We identify imported and exported products that correlate with growth and income per capita. • Catching-up economies characterized by frugality in consumer demand and the prominence of mass-production industries. • Mature economies distinguished by the importance of consumer demand and the export of knowledge-intensive goods. Using a novel methodology to relate traded products to aggregate country indicators, this paper identifies imported and exported goods that correlate with growth and income per capita, and based upon that, determines the productive and demand structure of mature and catching-up economies. The methodology in question first uses Principal Component Analysis on the products that countries specialize in importing and exporting, to reduce the number of variables that will, later, be linearly related to income and growth using Canonical Correlation Analysis. It is shown that fast growing or catching-up economies are characterized by the export of mass-produced manufactured goods, and the import of capital and intermediates – but not consumer goods. Frugality in consumption demand and congruence between exports and imports explain the high growth rates of catching-up or transitioning economies. In contrast, high income and slow growth mature economies are characterized by the export of goods whose production requires advanced technological capabilities. Their demand structure is characterized by the prominence of consumption rather than means of production. [ABSTRACT FROM AUTHOR]

Frequent power outages constrain households' electricity consumption and lead to additional backup expenditures on top of their electric utility bills in many developing countries. This study analyzes how residential electricity consumers of different income levels would change their electricity consumption and backup decisions in response to electricity reliability improvements. Using a nationally-representative sample of Nepalese households, the findings point out that improved reliability is positively correlated with the probability of electric appliance ownership. The interaction of income and reliability-constraint indicators suggests that the unreliable electricity supply constrains households equally at all income levels. However, the results from an ordered probit model with three off-grid backup decision alternatives indicate that the improved reliability matters more for poorer households, for whom the combined ownership cost of both appliances and backup equipment may be prohibitive. Putting this paper's findings into an energy-policy perspective, a connection to the grid by itself does not necessarily translate to realized benefits from electricity consumption. The reliability of the service plays a critical role, especially for poorer households. • This paper investigates how households respond to reductions in power outages. • The probability of electric appliance ownership increases at all income levels. • Household income constrains both appliance ownership and backup decisions. • Therefore, poorer households benefit relatively more from a reliable grid supply. [ABSTRACT FROM AUTHOR]

With advances in AI (Artificial Intelligence) and the surge of Big Data, Deep learning (DL) has a remarkable impact on businesses in various domains including finance, marketing, healthcare, manufacturing, and agriculture. From sales prediction to detection and monitoring customer behaviors, its use in business analytics is proven to help grow and improve business functions. However, execution of DL can instigate high cost during both model training and deployment. Time and energy are two essential cost factors of DL computation as they imply not only monetary but also environmental consequences. Moreover, different DL architecture incurs different costs. Selecting the best model to train and deploy is hence important to the business. Most existing work focuses on building a DL model that obtains the highest accuracy or similar measures to evaluate quality of data analytics. In this paper, we argue that in practice, such performance criteria may not be adequate, for example, the most accurate model may not be the most cost-effective. This paper studies a practical approach that includes energy consumption during training and deploying the trained DL in deciding what DL model should be deployed. The paper describes how energy consumption can be estimated and illustrates the approach for cost analysis of DL application on the smart agriculture business where plant disease classification model is trained and deployed. Lessons learned from the study is included. In summary, our contributions are: (1) an approach to analyze cost of using DL model in terms of time, accuracy and energy consumption to support model selection, and (2) application of the approach on plant disease classification in smart agriculture business. [ABSTRACT FROM AUTHOR]

Recently, the widespread adoption of artificial intelligence, particularly generative AI technology, has surged across various industries. However, a notable drawback of this technology is its significant energy consumption during model training and operation, which poses challenges to sustainability goals and the environment. Consequently, various initiatives have emerged to promote what is termed "green artificial intelligence," aiming to mitigate these environmental impacts. Nevertheless, research discussing these initiatives remains scarce. Hence, this study aims to identify green artificial intelligence initiatives that contribute to environmental friendliness. This paper has comprehensively reviewed the existing literature, professional websites, and expert blogs to identify and analyze available green AI initiatives. This paper has identified 55 such initiatives, broadly categorized into six themes: cloud optimization, model efficiency, carbon footprinting, sustainability-focused AI development, open-source initiatives, and green AI research and community. This study discusses the strengths and limitations of each initiative to offer a comprehensive overview. The findings provide valuable insights, particularly for industries interested in green artificial intelligence and green technology in general. While some tools have been recognized and studied, comprehensive research and analysis are still required to empirically evaluate the majority of other tools due to their early stages of development in this field. • Study explores the growing awareness and concern about environmental impact of AI. • It investigates development of tools and approaches to address Green AI. • Significance of collaboration, open-source initiatives, in Green AI development. • Examine importance of ethical considerations in green AI development. • Provide insights for industries in green AI and green technology in general. [ABSTRACT FROM AUTHOR]

Deep learning is widely used in Bearing Fault Diagnosis (BFD). Nonetheless, practical industrial production often generates a large amount of industrial noise. These noises exhibit randomness and complexity, which puts forward higher requirements for diagnosis algorithms. Certain studies have tackled the issue of anti-interference in high-noise environments (S N R ≤ 0 dB) by increasing the complexity of the model. However, due to the excessive number of parameters and computation, such models cannot be deployed on low-end edge devices. Balancing resource consumption and accuracy has become a major challenge in BFD modeling research. To solve the above problems, this paper proposes a new Transformer architecture model called LTFAFormer. The LTFAFormer is capable of achieving high-precision diagnostics on low-end edge devices and shows greater noise resistance. In terms of processing sequence information, Transformer has proven to be superior to other solutions. However, when dealing with longer sensor signal data containing complex noise, the traditional self-attention mechanism not only cannot effectively extract fault features, but also generates more computational complexity than CNN. To address this issue, we propose a novel time–frequency dual-channel parallel attention mechanism. Our approach enhances the feature extraction capability of the model by expanding the attention computation scale and reduces the computational resource consumption of the model by optimizing the model structure. To validate the effectiveness of LTFAFormer, we present two cases to demonstrate that LTFAFormer has higher prediction accuracy while satisfying lightweight. Especially in high-noise environments, LTFAFormer has stronger robustness. In this paper provides a new set of feasible strategies for the practical deployment of BFD models in practical industrial environments. • The LTFAFormer, a lightweight and noise-robust model, is ideal for low-end devices. • Added a Dual-Channel Attention Module to expand traditional self-attention scale. • Developed a Cross-fusion mechanism for attention to enhance feature fusion. • Optimized the model's embedding module based on sensor signal traits. • Our model outperforms others in bearing fault diagnosis and is robust in noise. [Display omitted] [ABSTRACT FROM AUTHOR]

Effective coverage path planning for USVs is essential to optimize the performance of bathymetric surveys. Traditionally, it is well recognized that the best sweep direction for coverage path planning is the parallel direction that finds the minimum span of the region of interest (ROI). However, the results in this paper first reveals that it is not always the case when the water current is involved. In this paper, we first formulate an energy consumption estimation model for USV and introduce a novel coverage path planning strategy. Then, we investigate the relationship between sweep direction and USV energy consumption under the influence of water currents, referred to as the E - β curve. We then systematically analyze the variations of the E - β curve under different shapes of ROIs, various water current attacking angles, and different coverage strategies. Finally, the model is extended to more realistic scenarios involving non-convex ROIs. The practicality of the model is validated USV semi-physical model. The results indicate that, under water currents, our proposed model allows for an optimal sweep direction that minimizes the energy consumption of USVs. This contrasts with traditional methods, which fail to achieve the optimal energy cost. The results hold significant implications for the design of USV mapping strategies in inland river. • This paper is the first to unveil the relationship between sweep direction and USV energy consumption under water currents, referred to as E - β curve. • Systematical analysis has been conducted on the factors that may impact the E - β curve. • An energy consumption estimation model for USV is constructed. • The model is extended to realistic scenarios involving non-convex polygonal ROIs and semi-physical simulation. [ABSTRACT FROM AUTHOR]

The widespread use of digital technology has driven a huge change in the way payments are made. Based on the micro-data of the China Family Panel Studies (CFPS), this paper explores the relationship between mobile payment and carbon emissions from the consumption side. With the exogenous impact of Alipay's "Collecting Five Blessings" campaign, this paper finds that mobile payment significantly increases household consumption carbon emissions, but the marginal effect is gradually decreasing. Mechanism analysis shows that mobile payment mainly realizes the above impact through promoting the increase of consumption expenditure and upgrading of consumption structure. Further analysis indicates that carbon reduction policies introduced by the Chinese government in the personal consumption sector can mitigate the carbon emissions growth associated with mobile payments. Still, there is a need to strengthen policy measures further to effectively address the issue of rising carbon emissions from household consumption. [ABSTRACT FROM AUTHOR]

• Estimates of global poverty as reported in the Sustainable Development Goals assume that a household of six people needs three times as many resources as a household of two people (i.e., there are no economies of scale in household consumption). • This paper assesses the sensitivity of global poverty estimates to assuming that there are economies of scale in household consumption. • The findings indicate that regional poverty rates do not change substantially when incorporating household-level economies of scale. • While the regional profile does not change much, the identification of who is poor changes substantially with the poverty status of more than 260 million people changing when allowing for economies of scale in household consumption. • The findings offer evidence that dividing total household resources by the square-root of household size performs reasonably well in identifying poor households. Estimates of the number of people living in extreme poverty, as reported by the World Bank, figure prominently in international development dialogue and policy. An assumption underpinning these poverty counts is that there are no economies of scale in household size – a family of six needs three times as much as a family of two. This paper examines the sensitivity of global estimates of extreme poverty to changing this assumption. The analysis rests on nationally representative household surveys from 162 countries covering 97.5 percent of the population estimated to be in extreme poverty in 2019. We compare current-method estimates with a constant-elasticity scale adjustment that divides total household consumption or income not by household size but by the square-root of household size. While the regional profile of extreme poverty is robust to this change, the determination of who is poor changes substantially – the poverty status of 264 million people changes. We then discuss evidence suggesting that the assumption of economies of scale more closely aligns with non-consumption measures of poverty. Specifically, we draw from existing literature of subjective assessments of poverty and wellbeing, along with new empirical evidence from examining the partial correlation (conditional on household size) between the two measures of poverty and a set of presumed poverty covariates (i.e., years of schooling, literacy, asset index, working in agriculture, access to electricity, piped drinking water, improved sanitation). [ABSTRACT FROM AUTHOR]

Urban energy consumption is a critical aspect of sustainable urban development, with technological innovations playing a pivotal role in shaping energy usage patterns. This paper presents a comprehensive analysis of urban energy consumption dynamics in relation to the adoption of technology across various sectors, including transportation, residential, industrial, and commercial domains. Through extensive data collection and advanced statistical analyses, we investigate the correlation between technology adoption rates and energy consumption behaviors in urban areas. Our findings reveal a strong positive correlation between technology adoption rates and reduced energy consumption across sectors. We employ spatial analyses to identify localized successes, shedding light on regions where technology-driven initiatives have significantly lowered energy demands. These insights offer valuable guidance for urban planners and policymakers aiming to implement targeted interventions and replicate effective strategies. Furthermore, the paper emphasizes the transformative potential of innovative solutions, ranging from smart infrastructure to industrial automation, in promoting energy efficiency and environmental sustainability. By fostering the integration of such technologies, cities can mitigate their carbon footprint, enhance energy resilience, and create more livable environments for residents. This research contributes to the discourse on urban energy transitions and provides actionable insights for urban policymakers and stakeholders striving for a greener, more sustainable urban future. • Urban energy study integrates tech for sustainable transitions. • Optimized urban planning with technology-driven energy solutions. • Innovative model promotes green, resilient cities through tech adoption. [ABSTRACT FROM AUTHOR]

• This paper proposes a comprehensive optimization program to increase economic efficiency and improve the resiliency. • A demand response program integrated with home energy storage systems is presented to optimize energy consumption. • To modify the consumption pattern of household consumers, a real-time pricing algorithm is proposed. • A self-healing system reconfiguration program integrated with distributed energy resources is presented. • Real data of California households are considered to model the home appliances and home energy storage systems. This paper proposes a comprehensive optimization program to increase economic efficiency and improve the resiliency of the Distribution Network (DN). A Demand Response Program (DRP) integrated with Home Energy Storage Systems (HESSs) is presented to optimize the energy consumption of household consumers. Each consumer implements a Smart Home Energy Management System (SHEMS) to optimize their energy consumption according to their desired comfort and preferences. To modify the consumption pattern of household consumers, a Real-Time Pricing (RTP) algorithm is proposed to reflect the energy price of the wholesale market to the retail market and consumers. In addition, a Self-Healing System Reconfiguration (SHSR) program integrated with Distributed Energy Resources (DER), reactive power compensation equipment, and Energy Storage Systems (ESSs) is presented to manage the DN energy and restore the network loads in disruptive events. The reconfiguration operation is performed by converting the isolated part of the DN from the upstream network to several self-sufficient networked virtual microgrids without executing any switching process. Real data of California households are considered to model the home appliances and HESSs. The proposed comprehensive program is validated on the modified IEEE 123-bus feeder in normal and emergency operating conditions. [ABSTRACT FROM AUTHOR]

Electricity theft has emerged as a notable concern for smart grids, as fraudulent users illicitly access electricity from utilities without a contractual agreement or manipulate meter readings to evade bill payments. Consequently, the importance of electricity theft detection (ETD) in preserving the cost-effectiveness of smart grids has increased. In recent years, the utilization of deep learning models in electricity theft detection has surged in popularity, owing to their capacity to capture the periodic patterns within time-series data on electricity consumption. Nonetheless, deep learning techniques for electricity theft detection encounter several challenges, such as data imbalance, higher false positive rates, and the determination of periodic cycle timings. In this paper, we present an innovative approach to tackle these challenges: the Multi-Objective Evolutionary Hybrid Deep Learning (MOE-HDL) architecture, specifically designed to identify electricity theft. In MOE-HDL techniques, first, a novel multi-objective evolutionary algorithm based on ranking scheme (RMOE) is proposed to optimize architectural model parameters and the detection window size, with precision and recall as conflicting objectives. Next, an optimized dynamic detection window is generated to effectively capture the periodic patterns in consumption data and convert the one-dimensional data into a grid structure. Finally, a hybrid deep learning model is designed with the help of optimized model parameters to identify abnormal patterns in electricity consumption data. We conducted a comprehensive set of experiments, comparing our approach with eight baseline methods for electricity theft detection. The experimental results demonstrate that the proposed MOE-HDL approach outperforms these alternatives in terms of performance. • In this paper, a novel multi-objective evolutionary hybrid deep learning approach was developed for theft detection in smart grids. • Precision and recall are two conflicting yet effective objective functions designed to optimize the model parameters and detection window. • A dynamic detection window is obtained through optimization to transform electricity consumption data. • The hybrid deep learning model is designed according to the model parameters obtained through evolutionary multi-objective optimization. [ABSTRACT FROM AUTHOR]

There has been an increase in the use of renewable energy sources over recent years, which has led to a strand of literature examining the determinants of renewable energy consumption. However, most of the research used linear estimation models while reviewing the determinants of renewable energy consumption and ignored the indirect effect of democratic institutions on renewable energy consumption. With the use of a panel threshold model, this paper demonstrates that democratic institutions play a significant role in renewable energy consumption. In countries in which the democratic rights of people are preserved better, higher economic growth leads to increased use of renewable energy consumption; however, there is a negative association between economic growth and renewable energy consumption in less democratic countries. Increased trade openness leads to lower growth rates of renewable energy consumption in less democratic countries, while increases in real oil prices lead to increased renewable energy consumption in less democratic countries but play no significant role in more democratic countries. The findings of this paper suggest that democratic institutions are vital in channelling economic resources (economic growth) to renewable energy, and increased trade openness is associated with lower rates of renewable energy deployment in less democratic countries. [ABSTRACT FROM AUTHOR]

Models of optimal retirement should reflect market incompleteness in reality caused by disability risk. In this paper, we develop an analytic approach for optimal retirement models with disability risk. More precisely, we provide an analytically tractable characterization of total wealth that is the sum of financial wealth and the present value of future income. We then provide analytic properties of the retirement wealth threshold. Finally, we derive the analytical optimal consumption and portfolio choice with retirement and disability risk. • We develop an analytic incomplete-market approach for optimal retirement models with disability risk. • Analytically tractable optimal strategies are provided. • Various mathematical and economic properties of optimal strategies are obtained. [ABSTRACT FROM AUTHOR]

This paper revisits the relation between economic growth and life insurance consumption for a panel of 130 countries in the period between 2003 and 2016. To address endogeneity concerns, we instrument economic growth by the quality of governments' regulations and show that economic growth positively affects different measures of insurance consumption. Additionally, we find that the share of income households spend on life insurance increases as economies become wealthier. Our findings thus support the demand-following and feedback hypotheses, which state that economic growth contributes to insurance sector development. Finally, we provide evidence that these effects are stronger for middle- compared to high-income economies. Consistent with the idea that the marginal propensity to consume is especially large in low-income economies, we cannot find any evidence that economic growth affects insurance consumption in these countries. [ABSTRACT FROM AUTHOR]

This paper aims to explore the effectiveness of macroeconomic recovery policies implemented by fiscal and monetary authorities against Covid-19 pandemic in Turkey. A dynamic stochastic general equilibrium (DSGE) model, in which Turkey is defined as an open and small developing country, is built. The model contains eight groups of agents, which are two heterogeneous households, the firms producing intermediate, final and capital goods, the commercial bank, the central bank and the government. Stochastic simulations of the model reveal the propagation of Covid-19 shock, the impacts of fiscal and monetary tools on the selected economic variables. The simulations demonstrate that direct fiscal measures are more effective in mitigating negative economic impacts of Covid-19. This paper broadens existing literature on the macroeconomic impacts of Covid-19 by exerting the functioning of different fiscal and monetary measures in a comprehensive framework within a developing country context. • A simulated dynamic stochastic general equilibrium (DSGE) model is constructed. • Covid-19 is introduced as an autoregressive process that directly affects labor supply, aggregate demand as well as households' consumption and external block such as export, global interest rates and inflation. • Direct fiscal measures are much more effective in alleviating negative impacts of the pandemic in Turkey. • A more targeted and harmonious policy mix that includes more direct fiscal tools and prudently accommodative monetary policy would be more appropriate in order to support output and consumption. [ABSTRACT FROM AUTHOR]

New technologies that are applied in many areas of social and economic life, make the processes of creating, distributing and communicating value to customers continuously transform. It is also connected with redefining the roles performed by market entities, including producers, service providers, distributors and consumers. New technologies expand the possibilities of engaging the consumer in processes that have been conducted before only by companies. In this context, technology management is important. It translates into creating a spiral of values, in which what is possible in the field of new technologies will inspire what is indicated in the area of value management for the customer and vice versa. This is reflected in the concept of technological determinism, as well as in the ongoing changes in consumer behaviour and consumption virtualization. The aim of the paper is to present the scope of Internet applications in the processes of managing value for customers in Poland. The paper is based on literature studies and the results of a quantitative empirical study, which was performed on a sample of 156 companies operating on the Polish market. The research included people responsible for the use of the Internet in marketing or for maintaining contacts with customers with the use of new technologies. The analysis of the research results shows that the Internet is used to a greater extent to communicate and distribute value rather than to involve customers in its creation. [ABSTRACT FROM AUTHOR]

In the last decade, with the introduction of smart meters to smart grids, demand response programs (DRPs) have been widely adopted to establish a generation and consumption balance. DRPs provide many benefits for efficient grid management. However, these programs are conducive to higher levels of dissatisfaction by changing grid customers' consumption patterns. This paper aims to investigate the effects of DRPs on social welfare (SW). To this end, the paper presents a mathematical model for SW during the implementation of DRPs. In the proposed model, the level of customer satisfaction is assumed the main factor contributing to SW. This mathematical model considers different types of DRPs in terms of their impacts on SW. The paper also seeks to obtain linear and nonlinear models of DRPs and the coefficient of participation (CoP). CoP as an indicator shows the percentage of customers who actively participate in each DRP and plays a significant role in the assessment of the SW level. Moreover, owing to the sparsity and variety of distribution network customers, load patterns are classified into different clusters to take the load types into account. As a matter of fact, this process aims to identify similar patterns and thus, the same level of satisfaction for each separate cluster. The classification process is performed by using a machine learning-based clustering method known as the Affinity Propagation (AP) algorithm. Then, the model calculates the level of SW for the clusters based on the usage of electrical equipment and the time of day when they are turned on. The obtained levels of SW help operators select the best programs for every cluster in terms of customer satisfaction, and achieve the highest performance of DRPs. Lastly, the model is evaluated using real data of a distribution network to ensure the effectiveness and accuracy of the model. [Display omitted] • Development of a mathematical model for assessing Social Welfare during DRPs execution, considering customer satisfaction. • Integration of Coefficient of Participation (CoP) as an important factor in evaluating SW during DRP implementation. • Development of Affinity Propagation algorithm to cluster load patterns, enabling tailored DRPs to maximize effectiveness. [ABSTRACT FROM AUTHOR]

• Derivatives have applications in biodiversity conservation, both direct and indirect. • Derivatives can help hedge the risk of biodiversity loss. • By addressing biodiversity conservation, derivatives can also contribute to sustainable development goals. Increase in human activities owing to the rise in consumption demand is increasing the risk of biodiversity loss. To address this concern, several conservation activities are being undertaken, including those that find their basis in finance. This paper highlights the role of finance towards biodiversity conservation, which is illustrated by taking into consideration one of the tools commonly used in financial risk management, namely, derivatives. The paper infers that derivatives can be directly applied to biodiversity conservation. In addition, it also highlights the possibility of specific derivatives such as energy and weather derivatives to indirectly contribute to conservation. Despite the challenges involved, such as pricing of biodiversity elements and assessment of loss, the possibility of applying derivatives to minimize the risk of biodiversity loss exists. [ABSTRACT FROM AUTHOR]

Demand Side Management (DSM) plays a significant role in the smart grid to minimize Electricity Cost (EC). Home Energy Management Systems (HEMSs) have recently been studied and proposed explicitly for HEM. In this paper, we propose a novel nature-inspired hybrid Genetic Flower Pollination Algorithm (GFPA) to minimize cost with an affordable delay in appliance scheduling. Our proposed GFPA algorithm combines elements of the Genetic Algorithm (GA) and Flower Pollination Algorithm (FPA) to create a hybrid approach. To assess the effectiveness of the proposed algorithm, we consider a scalable town consisting of 1, 10, 30, and 50 homes, respectively. The proposed solution finds an optimal scheduling pattern that simultaneously minimizes EC and Peak to Average Ratio (PAR) while maximizing User Comfort (UC). We assume that all homes are homogeneous in terms of appliances and power consumption patterns. Simulation results show that our proposed scheme GFPA performs better when applying Critical Peak Pricing (CPP) signal using different Operational Time Intervals (OTIs) and compared with unscheduled, GA, and FPA-based solutions in terms of reducing cost since they achieve on average 98%, 36%, 23%, and 22%, respectively. Similarly, PAR averages 98%, 36%, 59%, and 55%, respectively. While, UC comparing to GA and FPA, are around 88%, 48%, and 63%, respectively. Our proposed scheme achieves better results by applying Real Time Pricing (RTP) signals and different OTIs. As these schemes, i.e., unscheduled, GA, FPA, and GFPA, achieve cost on average 92%, 50%, 29%, and 28%, respectively. While PAR on average 94%, 39%, 62%, and 56%, and UC for GA, FPA, and GFPA on average 98%, 52%, and 49%, respectively. Overall, our proposed GFPA algorithm offers a more effective solution for minimizing EC with an affordable delay in appliance scheduling while considering PAR and UC. [ABSTRACT FROM AUTHOR]

When customers purchase goods provided by public utilities, such as telecommunications, water, gas or electricity, the predominant payment system is pay-later billing. This paper tests the effectiveness of a pay-as-you-go system and the importance of payment timing in reducing consumption. I conduct a lab experiment which mimics an energy consumption choice and randomizes the timing of when consumption costs are paid: Either immediately ('pay-as-you-go') or one-week after consumption ('pay-later'). Results show that pay-as-you-go billing significantly decreases energy consumption. Yet the decrease in consumption comes at the cost of needing more work time to complete a fixed number of real-effort tasks. As the design controls for a number of contaminating effects, present-focused discounting of the future energy bill under the pay-later system is the likely explanation for the change in behavior. [ABSTRACT FROM AUTHOR]

This paper models the real-time adaptation of utility when individuals have a limited capacity to make fine distinctions. A simple adaptive mechanism is proposed that adapts utility to an arbitrary distribution of rewards in a way that is ultimately optimal, in terms of minimizing the probability of errors. A modified simple mechanism is approximately optimal in terms of maximizing expected utility. The model generates the so-called "hedonic treadmill" and induces a preference for rising consumption. [ABSTRACT FROM AUTHOR]

To incentivize competitive conduct, the US Department of Justice restricted codesharing between Alaska Airlines and American Airlines in several route scenarios as a condition for allowing the former to acquire Virgin Airlines. Have these restrictions incentivized competitive conduct? This paper investigates their effects on output, price, and revenue in these route scenarios. Estimation results suggest that output and price have significantly decreased, particularly for routes between the airlines' hubs or focus cities, indicating that the restrictions have not incentivized competitive conduct. Rather, the decrease in both output and price suggests that consumers' demand has decreased. Welfare for consumers and airlines has decreased. • This study investigates merger impacts when the merging carriers are prohibited to codeshare in select markets. • We find sizable decreases in output and revenue, and a mild fall in airfare on restricted routes. • The merger effects are persistent and appear to be driven by restrictions on routes between the airlines' hubs/focus cities. • We conclude that codeshare carve-outs may not be an effective policy tool for merger approval. [ABSTRACT FROM AUTHOR]

It is challenging to handle the non-linear power consumption model, complex workflow structures, and diverse user-defined deadlines for energy-efficient workflow scheduling in sustainable cloud computing. Although metaheuristics are very attractive to solve this problem, most of the existing work regards the problem as a black-box and ignores the use of domain knowledge. To make up for their shortcomings, this paper tailors an energy-aware intelligent scheduling algorithm (EIS) with three new mechanisms. First, we derive the optimal execution time that minimizes energy consumption for each task on a given resource. Second, based on the optimal execution time of each workflow task, the EIS distributes the workflow slack time (difference between its completion time and deadline) to reduce the voltages and frequencies of task executions for energy saving. Third, the EIS mines the idle time gaps caused by task precedence constraints to further reduce dynamic energy consumption whilst satisfying workflows' deadline constraints. To measure the performance of the EIS, we conduct extensive comparison experiments based on actual workflow applications. The results demonstrate that the energy consumption of the EIS is much lower than that of the competitors under different deadlines, and has a faster descend rate with the evolution process. [ABSTRACT FROM AUTHOR]

In this paper, we investigate the effects of information and communication technology (ICT) on economic growth and energy consumption for a panel of 27 countries ranked top of the ICT Development Index, from 1990 to 2019. By employing the panel nonlinear autoregressive distributed lag (NARDL) model approach, we confirm the presence of a long-run asymmetric effect of ICT on economic growth and energy consumption. Specifically, negative changes (reduction) in the adoption and usage of ICT can have severe consequences on economic growth in these 27 countries in the long-run, erasing all previous gains from positive ICT changes. Additionally, positive or negative shocks to ICT in these countries increase energy consumption, indicating the rebound effect exists in these countries. Finally, the results also reveal that, while the economic growth in these countries is dependent on energy consumption in the short-run, it is energy independent in the long-run. Several important policy implications are drawn. [ABSTRACT FROM AUTHOR]

Consumer behavior, by definition, is concerning with various actions adopted by a person to acquire, use and dispose of consumer goods and services, including the decision-making process that precedes and determines these actions. In real practice, such behavior can be altered with the introduction of various internet-based financing services. Especially for college students, who have relatively weak knowledge of personal finance and insufficient vigilance of excessive consumption, their consumption behavior can be easily manipulated by internet-based financing service providing firms. In this paper, the objective is to determine the dominant factors adopted by internet-based financing service providers to modify the consumption behavior of college students. From 571 college students' questionnaires, the collected data are subjected to factor and logistic regression analysis to verify what factors are likely to stimulate the willingness of college students to actively employing the internet-based financing services to fulfill their advanced consumption demands. The obtained result indicates the service and convenience factors are likely to influence and alter their consumption behavior, and the corresponding recommendations on how to refine and regulate such market are proposed at the end of this paper. [ABSTRACT FROM AUTHOR]

This paper explores income and consumption smoothing patterns among ECOWAS countries during the period 1980–2016. It is relevant given the willingness of heads of state in the region to create a single currency. Indeed, in a monetary union, asymmetric shocks are not problematic if risk-sharing mechanisms, other than the exchange rate, are in place to allow countries to adjust to specific shocks. The paper provides more information on the accounting decomposition of national aggregates allowing a better understanding of risk-sharing channels. Unlike previous studies in this area, we show that official development assistance and gross saving smooth out asymmetric shocks between ECOWAS countries. Besides, net taxes on products and production, respectively, social contributions and social benefits limit net primary incomes' effectiveness, respectively, net secondary incomes to smooth asymmetric shocks. Moreover, we show that even if the degree of risk-sharing increases over time, it has remained limited. Also, market- based risk-sharing mechanisms are ineffective when they are most needed. That's why ECOWAS countries need a supranational fiscal that could provide an additional tool to smooth asymmetric shocks in the region. [ABSTRACT FROM AUTHOR]

• A distributed real-time pricing based energy management framework is presented. • A behavior welfare model is developed to exploit the reward fairness strategy. • The interactions among energy users have been investigated by a noncooperative game. • A distributed algorithm with sufficient convergence condition is proposed. Real-time pricing (RTP) is the pivotal component of demand response (DR) that promotes the power utilization of users in a cost-efficient way. This paper presents a reward fairness-based distributed RTP (RFbDRTP) framework for electric users to reduce the electricity consumption cost as well as experience the fairness from participating in DR. In RFbDRTP, a behavior welfare model (BWM) is developed to exploit the reward fairness strategy that returns the cost savings as a reward to the users in proportion to their load increase or decrease in DR. The model can motivate users to react to the price signal through adjusting their electricity consumption patterns, to in turn enable optimal distributed RTP for matching supply–demand. In this paper, users are assumed to interact with each other because of pricing based on the imbalance between load demand and electricity supply. We formulate the interactions among the users into a noncooperative game and give a sufficient condition to ensure the unique equilibrium in the game. After that, we develop a distributed algorithm and give a sufficient convergence condition of the algorithm. The simulation results show that the proposed RFbDRTP is effective in motivating users to participate in DR and matching demand with supply, and the distributed algorithm can converge to the equilibrium with a significant convergence rate. [ABSTRACT FROM AUTHOR]

The reform of collective land ownership in post-socialist contexts offers a useful window into how changes in property rights shape and structure the dynamics of territorial transformation. Focusing on China's rural revitalization campaign, this paper demonstrates how the state, as creator and regulator of land rights and property titles, facilitates landscape change by relaxing regulations over the lease of rural land and creating market institutions that favour land transfers to organized capital, in this case tourism companies and property developers. Far from being a spontaneous process of pure market forces and consumer demand, the state is actively harnessing market forces to achieve land use change and extend planning control over rural land. On the ground, the expanding presence of recreational capital has brought about the remaking of Chinese villages into leisure farms, holiday guesthouses and ecological parks geared towards touristic consumption. The paper calls for further attention to processes of exclusion and displacement that this transformation might engender. By exploring the dynamics of rural gentrification in a post-socialist context, this paper makes the case for the need to pay more attention to the comparative study of property rights and ownership regimes in rural gentrification research, and highlights how the intersection of property rights and state power reveals nuances that underscore the importance of going beyond Western conceptualizations of gentrification. • Comparative study of property rights is important for gentrification research. • Examines nexus of state power and property rights transformation in gentrification. • Marketization of rural land in China facilitates gentrification in the countryside. • Tourism and recreational capital contributing to revalorization of rural landscapes. • Shows comparative nuances of a gentrification theory for the rural Global South. [ABSTRACT FROM AUTHOR]

A shared economy is a model of collaborative consumption by obtaining, giving, or sharing access to goods and services through online platforms. It is a marketplace that provides users with access to product benefits with temporary ownership. Digitalization has introduced new possibilities and conditions for sharing and exchanging goods and services, allowing users to compare offers and connect dynamically with the seller in real time. The paper presents the results of the survey conducted on a group of 1,620 prosumers and the practical applications of the analysis of the results. The research aimed to identify the main components of the co-sharing decision-making process and their importance in this process. A diagnostic survey was used in the study, and the collected data were statistically analyzed. The main findings are that there are common criteria for evaluating the model of sharing goods and services for the research population. They are: sharing preference, trust, realized benefits, propensity to propagate, risk awareness, aversion to sharing, willingness to share, and the limits of anonymity. Moreover, subjective satisfaction with the achieved income influences the perception of factors that are important in the decision-making process of sharing goods and services. [ABSTRACT FROM AUTHOR]