Becoming a tree when I will be dead? Why not! Generation X,Y and Z,and innovative green death practices

Within the theoretical field of ethical consumption, the study focuses on consumer behavior and innovative green death practices. While scarcely investigated in the marketing and consumer behavior research domains, at least four main reasons spur to consider the funeral industry as an interesting subject of enquiry: i) the sector boasts of a significant economic relevance; ii) it is extremely impactful from an environmental and social point of view; iii) it is witnessing interesting innovative processes in the direction of social and environmental sustainability; iv) consumer movements are rising all over the world asking for more sustainable death practices. Thus, adopting an augmented version of the theory of planned behavior (TPB), which includes individuals’ environmentally responsible consumption, and egoistic and altruistic environmental concern, the study investigates behavioral intention to adopt innovative green death practices (a green funeral) of people belonging to three different generations: X, Y, and Z. A purposive sample of 627 Italian people participated in an online the survey. Collected data were analyzed adopting an ordinal logit model. The results confirm the TPB predictive power also in the field of green funeral behavioral intention. Furthermore, environmentally responsible consumption and environmental concern – even though only in its altruistic component – positively influence behavioral intention, and generation exerts a moderating effect among the examined constructs. Overall, the research attempts to enrich the literature on ethical consumption by exploring the underinvestigated phenomenon of buying behavior of unsought and end-of-life products, that of green funerals, and provide managerial recommendations to funeral service sellers.     

Green consumption behavior prediction based on fan-shaped search mechanism fruit fly algorithm optimized neural network

Predicting consumption behavior is very important for adjusting supplier production plans and enterprise marketing activities. Conventional statistical methods are unable to accurately predict green consumption behavior because it is characterized by multivariate nonlinear interactions. The paper proposes an optimized fruit fly algorithm (FOA) and extreme learning machine (ELM) model for consumption behavior prediction. First, to address the problem of uneven search direction of FOA leading to insufficient search ability and low efficiency, the paper proposes a sector search mechanism instead of a random search mechanism to improve the global search ability and convergence speed of FOA. Second, to address the issue that the initial weights and hidden layer bias values of the ELM are randomly generated, which affects the learning efficiency and generalization of the ELM, the paper uses an improved FOA to optimize the weights and bias values of ELM for improving the prediction accuracy. Taking the green vegetable consumption behavior of Beijing residents as an example, the results show the optimization of the initial weight and threshold of ELM by the GA, PSO, FOA, and SFOA, the prediction accuracy of the GA-ELM, PSO-ELM, FOA-ELM, and SFOA-ELM models all surpass those of ELM. Compared with BPNN, GRNN, ELM, GA-ELM, PSO-ELM, and FOA-ELM models, the RMSE value of SFOA-ELM was decreased by 9.45%, 8.40%, 11.89%, 5.84%, 2.22%, and 2.69%, respectively. These findings demonstrate the effectiveness of the SFOA-ELM model in green consumption behavior prediction and provide new ideas for the accurate prediction of consumption behaviors of other green products with similar characteristics.     

实现2035年发展目标的潜在增长率*

党的十九届五中全会提出了到2035年人均GDP达到中等发达国家水平的远景目标，因此测算和回答能否和如何如期实现该目标，对于我国实现第二个百年奋斗目标和坚持“四个自信”具有重要的意义。为此，本文根据跨越和陷入“中等收入陷阱”经济体的发展经验，对2021—2035年我国潜在增长率变化进行了测算。一是参照跨越和陷入“中等收入陷阱”经济体在我国相同发展阶段时各主要生产要素的变化，模拟设定我国未来各主要生产要素的增长率；二是通过运用附加人力资本的增长核算模型测算基准、乐观和悲观三种不同情境下未来我国经济的潜在增长率，验证我国2035年发展目标实现的可能性；三是依据主要要素对潜在增长率的贡献度，提出我国如期实现2035年发展目标的相应政策建议。     

Deep learning for modeling the collection rate for third-party buyers

This study evaluates a wide range of machine learning techniques such as deep learning, boosting, and support vector regression to predict the collection rate of more than 65,000 defaulted consumer credits from the telecommunications sector that were bought by a German third-party company. Weighted performance measures were defined based on the value of exposure at default for comparing collection rate models. The approach proposed in this paper is useful for a third-party company in managing the risk of a portfolio of defaulted credit that it purchases. The main finding is that one of the machine learning models we investigate, the deep learning model, performs significantly better out-of-sample than all other methods that can be used by an acquirer of defaulted credits based on weighted-performance measures. By using unweighted performance measures, deep learning and boosting perform similarly. Moreover, we find that using a training set with a larger proportion of the dataset does not improve prediction accuracy significantly when deep learning is used. The general conclusion is that deep learning is a potentially performance-enhancing tool for credit risk management.     

Addressing plastic pollution through green consumption: Predicting intentions to use menstrual cups in the Philippines

Plastic pollution is a global environmental crisis that poses a huge threat to the health of people and marine ecosystems worldwide. A significant source of plastic pollution is menstrual hygiene management, and an approach that can help address this crisis is the usage of washable and reusable menstrual cups. Using an extended theory of planned behavior model that includes self-identity and perceived quality, the study predicted intentions to use menstrual cups in the Philippines. Structural equation modeling results showed that perceived quality predicted attitudes towards menstrual cup usage. Moreover, attitudes, perceived behavioral control, and self-identity predicted intentions to use menstrual cups. There were also some differences in the factors and predictors of intentions between non-users and regular users of menstrual cups. Among regular users, perceived behavioral control did not predict intentions. These findings provide empirical support for the extended model, and provides insights for governments, non-government organizations, and corporations in promoting the usage of menstrual cups to address the global plastic pollution crisis.     

How power distance belief,self-construal,and relationship norms impact conspicuous consumption

Building on the idea that luxury products can signal the owner's status to others, this research elucidates how fundamental and pervasive social factors—power distance belief (PDB), self-construal, and relationship norms—dynamically impact consumers' preferences for conspicuous consumption. Through three experiments, we found that (1) high-PDB consumers are more likely to prefer conspicuous consumption compared to low-PDB consumers, and this effect is mediated by their need for status; (2) the relationship between PDB and conspicuous consumption is enhanced under interdependent self-construal, attenuated under independent self-construal, and mediated by consumers' need for status; (3) these effects are moderated by relationship norms, such that the positive effect of PDB on conspicuous consumption under interdependent self-construal is attenuated when communal relationships (vs. exchange relationships) are salient. Our conceptualization and findings contribute to the literature on the dynamics of individuals' status-related behavior in social psychology and related fields, and provide important practical applications for luxury businesses.     

Determinants of China's bilateral trade balance in global value chains

This paper identifies the determinants of China's bilateral trade balance using a new measure based on international input–output data, the so-called ‘trade in value-added’ (TiVA), which can prevent double counting in the estimation of bilateral trade balance. Our results show that using a measure based on gross exports, rather than TiVA, causes relatively large overestimation of the impact of the RMB exchange rate on China's bilateral trade balance. This overestimation is mainly because that the increasing production of exports may require increasing intermediate imports as a consequence of international fragmentation of production in global value chains. In addition, our results also show that the impact of FDI inflows on China's bilateral trade balances depends on the position and role of China and its trading partners in GVCs.     

Debt Overhang and Deleveraging in the US Household Sector: Gauging the Impact on Consumption

Using a novel data set for the U.S. states, this paper examines whether household debt and the protracted debt deleveraging help explain the dismal performance of U.S. consumption since 2007 in the aftermath of the housing bubble. By separating the concepts of deleveraging and debt overhang—a flow and a stock effect—we find that excessive indebtedness exerted a meaningful drag on consumption over and beyond wealth and income effects. The overall effect, however, is modest—‐around one sixth of the slowdown in consumption between 2000–06 and 2007–12—and mostly driven by states with particularly large imbalances in their household sector. This might be indicative of non‐linearities, whereby indebtedness begins to bite only when misalignments from sustainable debt dynamics become excessive.     

Viewpoint: Regulating meat consumption to improve health,the environment and animal welfare

Meat consumption has increased significantly in the last 50 years. This trend raises various health and environmental issues, as well as moral concerns regarding farm animal welfare. In this paper, we discuss the regulation of meat consumption in developed countries. Specifically, we discuss possible justifications for this regulation in terms of environmental, health and animal welfare considerations, as well as the effect of fiscal, informational and behavioral regulatory instruments. Finally, we present a list of challenges that policy makers and food scholars may need to confront in the future.