How do crazy rich Asians perceive sustainable luxury? Investigating the determinants of consumers’ willingness to pay a premium price

Motivated by the increasing media coverage of environmental disasters and growing evidence of humans’ detrimental impacts on the natural environment, the key aim of this study was to examine consumer interest in buying sustainable luxury products. Rooted in the Value-Belief-Norm (VBN) theory, a research framework incorporating pro-environmental self-identity (PSI), consumer pro-environmental values (CPV), engagement, and willingness to pay a premium price (WPP) was proposed. To better understand the sustainable behavior of consumers, this study modelled myopia as a personality trait that moderates the aforementioned relationships. Partial least squares path modelling (PLSPM) was employed to analyze data collected from 296 Chinese consumers who had purchased luxury fashion products. The results highlighted the influential role of CPV in shaping both consumer engagement and WPP, surpassing the impact of PSI. Also, the study established the positive effect of engagement on WPP and confirmed its mediating role in the relationship between CPV and WPP. The incorporation of myopia as a moderator further enhanced the explanatory power of the VBN theory in understanding sustainable consumption evaluations. The findings revealed that the positive links between PSI, CPV, engagement, and WPP were particularly pronounced among non-myopic consumers, suggesting that a clear vision of long-term consequences strengthens the connections between these constructs. These findings offer valuable insights to both academics and practitioners, particularly in the realm of luxury fashion brands within Chinese culture. They provide a foundation for designing targeted marketing communication strategies that effectively leverage and cultivate consumers' pro-environmental self-identities and values. By aligning brand messaging with these values, luxury fashion brands can enhance consumer engagement and foster a willingness to invest in sustainable products. Ultimately, this study contributes to the growing body of knowledge on sustainable consumption and offers practical implications for promoting environmentally responsible choices in the luxury fashion industry.     

Developing disaster resilience: A processual and reflective approach

This study takes a processual view of resilience to investigate how tourism organisations utilise dynamic capabilities to develop resilience in a disaster context. A longitudinal qualitative research design was used to gain insights into the process. The study uses on-site observation, secondary documents, and in-depth interviews with representatives from 25 tourism organisations and industry stakeholders across two years to provide a longitudinal understanding of disaster resilience. A dynamic resilience framework is developed based on three dynamic capability steps (sensing, seizing, transforming) and helps to explain how tourism organisations develop resilience elements at each disaster management stage. Two notable contributions include (i) a new resilience element added to the existing list of resilience development, which illustrates disruptive changes of tourism facilitated by transforming dynamic capabilities, and (ii) a trial-and-error process used by tourism organisations to improve decision-making for future disaster events. These help to explain the cyclical nature of resilience building.     

Including deprivation costs in facility location models for humanitarian relief logistics

Humanitarian assistance is meant to save lives and alleviate human suffering during and in the aftermath of man-made and natural disasters. To prevent and strengthen preparedness for the occurrence of such situations, having available relief supplies in the short-term becomes crucial. The lack of access to life-sustaining items implies a loss in people's welfare, treated as an externality called deprivation costs which must be incorporated into decision-making processes. Since typical humanitarian applications are extensions of commercial logistic models, they usually do not account for externalities, leading to high social costs and likely to unfeasible or suboptimal solutions.This paper develops a facility location model for prepositioning supplies in preparation for disasters; the key feature of this formulation being the fact that it explicitly considers deprivation costs in the objective function. The model attempts to minimize the global social costs, as the sum of both private costs (i.e. costs of transportation, inventory costs and fixed costs of facilities) and deprivation costs, determining the amount per type of product to be prepositioned for serving the areas affected by a disaster during the initial response. The model focuses on those assistance interventions that should be carried out immediately, i.e. within the first 24 h of a humanitarian crisis. We applied the model, using real information, to the Colombian Caribbean region, which was affected by floods in 2010 and 2011. Results demonstrate that deprivation costs represent more than 50% of the total social cost.     

基于景观生态安全格局的泥石流多发城镇防灾、减灾体系构建--以昆明市东川区为例

通过对泥石流灾害形成原因及对城镇造成危害的调查分析,提出泥石流多发城镇的灾害防御应首先从土地资源的保护和利用以及景观的战略布局着手,在城市避难疏散系统的构建中统筹兼顾各类城市灾害的危害性.采取灾害测报系统及布局、生命线保障系统规划,救灾设施及布局、抗灾防灾工程建设和疏散及避难通道规划等五项工程性措施,通过防灾救灾组织指挥体系建设、救灾队伍建设、灾害应急预案的制定、防灾救灾的宣传教育和灾害政策法规建设等五项非工程性措施的支撑,从根本上达到防灾减灾的目标,构建生态安全的城市.     

农业保险、粮食产量与灾害适应

农业保险保障水平的不断提高对维护粮食安全具有重要作用.本文在分析农业保险对粮食产量的理论影响基础上,构建了"适应性预期-局部调整"模型并利用省级面板数据和系统GMM估计方法进行实证分析.结果表明:农业保险对粮食单产和总产均具有显著正向影响,但主要通过影响粮食单产影响粮食总产出,对播种面积的影响不显著.其中,农业保险对灾...     

Stochastic optimization model for distribution and evacuation planning (A case study of Tehran earthquake)

In this study, a stochastic multi-objective mixed-integer mathematical programming is proposed for logistic distribution and evacuation planning during an earthquake. Decisions about the pre- and post-phases of the disaster are considered seamless. The decisions of the pre-disaster phase relate to the location of permanent relief distribution centers and the number of the commodities to be stored. The decisions of the second phase are to determine the optimal location for the establishment of temporary care centers to increase the speed of treating the injured people and the distribution of the commodities at the affected areas. Humanitarian and cost issues are considered in the proposed models through three objective functions. Several sets of constraints are also considered in the proposed model to make it flexible to handle real issues. Demands for food, blood, water, blanket, and tent are assumed to be probabilistic which are related to several complicated factors and modeled using a complicated network in this study. A simulation is setup to generate the probabilistic distribution of demands through several scenarios. The stochastic demands are assumed as inputs for the proposed stochastic multi-objective mixed integer mathematical programming model.The model is transformed to its deterministic equivalent using chance constraint programming approach. The equivalent deterministic model is solved using an efficient epsilon-constraint approach and an evolutionary algorithm, called non-dominated sorting genetic algorithm (NSGA-II). First several illustrative numerical examples are solved using both solution procedures. The performance of solution procedures is compared and the most efficient solution procedure, i.e., NSGA-II, is used to handle the case study of Tehran earthquake. The results are promising and show that the proposed model and the solution approach can handle the real case study in an efficient way.     

REGIONAL AND SECTORAL DISAGGREGATION OF MULTI-REGIONAL INPUT–OUTPUT TABLES – A FLEXIBLE ALGORITHM

A common shortcoming of available multi-regional input–output (MRIO) data sets is their lack of regional and sectoral detail required for many research questions (e.g. in the field of disaster impact analysis). We present a simple algorithm to refine MRIO tables regionally and/or sectorally. By the use of proxy data, each MRIO flow in question is disaggregated into the corresponding sub-flows. This downscaling procedure is complemented by an adjustment rule ensuring that the sub-flows match the superordinate flow in sum. The approximation improves along several iteration steps. The algorithm unfolds its strength through the flexible combination of multiple, possibly incomplete proxy data sources. It is also flexible in a sense that any target sector and region resolution can be chosen. As an exemplary case we apply the algorithm to a regional and sectoral refinement of the Eora MRIO database.     

Government intervention on cooperation,competition, and coopetition of humanitarian supply chains

We considered a humanitarian environment composed of donors and non-governmental organizations (NGOs) that the non-profits may adopt competitive or coopetitive inter-organizational interaction for managing the disasters. We also assumed that the government intervenes in the relief operations by applying one of its two policies; social welfare maximization (SWM) or budget consumption minimization (BCM). Using game theory (GT) approach, we develop 4 scenarios and, as a result, 4 mathematical programming models for examining the effect of the NGOs interactions and the government policies on the performance of donors, NGOs and government. We find that coopetition of NGOs facilitates the achievement of the government's objectives, and it also helps the non-profits to become more successful in providing relief. The government prefers to provide the indirect relief to the nonprofits to manage emergency operations successfully, and the financial aids are given only in the condition of reducing the level of NGOs cooperation. We also conclude that the cooperation of NGOs increases the donors' utility. A numerical example is conducted to test the findings of the models.     

Airports’ role as logistics centers in humanitarian supply chains: A surge capacity management perspective

The purpose of this study is to investigate the key factors of airport logistics surge capacity and the relationships between these elements. The findings show that successful airport surge capacity management is closely related to prioritizing the flights and operational activities; considering the influx of local people to airports and local infrastructure capacity in the planning phase; developing suitable methods to encourage people to take part in surge operations; providing uninterrupted communication in and out of airport. In view of the above-stated findings this study suggests that airports can meet the surge demand through systematic capacity planning of the existing and additional human, supply, system and space elements by considering experienced or anticipated capacity shortages in disaster conditions.     

REFLECTIONS ON THE INOPERABILITY INPUT–OUTPUT MODEL

We argue that the inoperability input–output model is a straightforward – albeit potentially very relevant – application of the standard input–output model. In addition, we propose two less standard input–output approaches as alternatives to take into consideration when analyzing the effects of disasters or disruptions.