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Redistributive policies aim to reduce income disparities and improve social equity. This study investigates whether redistributive effects that successfully diminish objective income inequality also effectively alter people’s perceptions of inequality.

Utilizing data from the 2018 China Household Income Survey (CHIP), comprising 56,167 individuals, this study applies ordered probability regression (Oprobit) and ordinary least squares (OLS) for analysis. To address potential biases in estimates, we employed the generalized propensity score matching (GPSM) method to estimate the treatment effect of transfer income on perceptions of inequality.

The results indicate that while China’s redistribution policies effectively reduce income disparities, they do not improve perceptions of inequality. Individuals exhibit biased attitudes toward redistributive policies. Specifically, perceptions of inequality are insensitive to the overall redistributive effect; the relationship is negative among the poor but positive among the rich. This contradictory pattern may be attributed to perceived income losses among the rich and gains among the poor.

The findings have important implications for policy development. Redistribution policies should not only aim to mitigate income disparities but also address and improve people’s perceptions of inequality.

Existing literature has largely overlooked the impact of redistribution on perceived income inequality. This study represents an early effort to explore whether redistributive policies that reduce income inequality also influence people’s perceptions of inequality.

Citizen engagement can promote value creation in urban development projects. This potential stems from the granting of decision-making authority to citizens, labeled citizen enfranchisement in this study. Citizens are focal stakeholders of urban development projects and enfranchisement grants them an explicit say on such projects. Despite this potential for enhanced value creation, there remains limited understanding about how project organizations enfranchise stakeholders in the front end of urban development projects.

In this research, we designed a multiple-case study to analyze two novel citizen engagement processes in Northern-European cities. In these processes, citizens were enfranchised in ideating, designing, and making selections on urban development projects. We followed a multimethod approach to data collection. The collected datasets include document data, interview data and observation data.

Our findings demonstrated a distribution and redistribution of decision-making authority throughout the phases of the citizen engagement processes. Citizens’ voices were amplified throughout the project front end, although episodes of decision-making authority held by the cities took place periodically as well. By granting explicit decision-making authority to citizens, citizen enfranchisement facilitated a more democratic urban development process, promoting value creation.

In contrast to the earlier research, the findings of our study illustrate citizen engagement taking place at so-called higher levels of stakeholder engagement. In particular, our study reveals a granting of de facto decision-making authority to citizens, also known as citizen enfranchisement. These findings contribute to the earlier research on stakeholder engagement in projects, where the influence of stakeholder engagement has often been considered symbolic or limited.

The purpose of this paper is to demonstrate the efficacy of machine learning (ML) in managing natural language processing tasks, specifically by developing two ML models to systematically classify a substantial number of food waste interventions.

A literature review was undertaken to gather global food waste interventions. Subsequently, two ML models were designed and trained to classify these interventions into predefined supply chain-related groups and intervention types. To demonstrate the use of the models, a meta-analysis was performed to uncover patterns amongst the interventions.

The performance of the two classification models underscores the capabilities of ML in natural language processing, significantly enhancing the efficiency of text classification. This facilitated the rapid and effective classification of a large dataset consisting of 2,469 food waste interventions into six distinct types and assigning them to seven involved supply chain stakeholder groups. The meta-analysis reveals the most dominant intervention types and the strategies most widely adopted: 672 interventions are related to “Process and Operations Optimisation”, 457 to “Awareness and Behaviour Interventions” and 403 to “Technological and Engineering Solutions”. Prominent stakeholder groups, including “Processing and Manufacturing”, “Retail” “Government and Local Authorities” and “NGOs, Charitable Organisations and Research and Advocacy Groups”, are actively involved in over a thousand interventions each.

This study bridges a notable gap in food waste intervention research, a domain previously characterised by fragmentation and incomprehensive classification of the full range of interventions along the whole food supply chain. To the best of the authors’ knowledge, this is the first study to systematically classify a broad spectrum of food waste interventions while demonstrating ML capabilities. The study provides a clear, systematic framework for interventions to reduce food waste, offering valuable insight for practitioners in the food system, policymakers and consumers. Additionally, it lays the foundation for future in-depth research in the food waste reduction domain.

Climate change has been universally recognized as a major threat to human well-being, necessitating a comprehensive transformation of people's activities. Various measures have been proposed to contain climate change among which the green transformation grabs special attention, thanks to its desirable properties. Within the green transformation process, green tourism comes to prominence with huge potential. As one of the largest carbon emitters, the transition towards green tourism may offer substantial benefits not only for tourism companies but also for the whole economy. Yet, most studies tend to focus on the adverse effects of tourism on climate change while overlooking the potential impact of climate change on tourism. This chapter clarifies the feedback relationship between climate change and tourism and makes some recommendations.

This study aims to apply the appreciative inquiry approach (AI) to develop a tourism strategy for poverty alleviation in marginalised communities. The focus is to provide practical insights for leveraging tourism to drive positive socio-economic change for the impoverished, using Rosetta, a port city in Egypt with cultural and historical significance, as a case study.

This qualitative applied study uses the four-D phases of AI and thematic analysis to strategise tourism development in Rosetta. Through interviews, focus groups and field visits, the study identifies tourism potential, stakeholder aspirations and actionable strategies for sustainable development. The approach prioritises a bottom-up, community-centric and stakeholder-involved process, aiming for inclusive and equitable growth.

The study revealed Rosetta’s underutilised tourism potential, emphasising heritage tourism. Although tourism offers some economic benefits, its impact on alleviating poverty in Rosetta remains limited. A holistic strategy for tourism development in Rosetta is proposed for economic growth and poverty reduction, focusing on sustainable management, local empowerment, enhanced marketing, improved infrastructure and diversified tourism offerings.

While AI is not new in qualitative studies, the novelty of this study lies in its application to tourism planning for poverty alleviation in a marginalised community like Rosetta, introducing a comprehensive tourism strategy with an original framework applicable to comparable destinations. The study’s significance is emphasised by providing actionable strategies for policymakers, valuable insights for practitioners and enriching the discourse and methodology on pro-poor tourism for academics, representing a step towards filling the gap between theoretical concepts and practical strategies.

Historically, governments have favoured the economic benefits associated with tourism development resulting in many tourism destinations being confronted with overdevelopment, crowding, environmental degradation as well as damage to the social and cultural fabric, especially pronounced in high attractivity destinations. The devastating consequences of the SARS-CoV-2 pandemic for tourism have led to a realisation that actors participating in tourism are especially susceptible to major health and security crises or natural disasters, mainly because their services are location bound and cannot be sold elsewhere. The involuntary ‘pause’ in travel worldwide has led many governments to realise that tourism policies must be placed in a broader context and that stakeholders, including residents and the environment where the brunt of the negative consequences are most deeply felt, must be an intrinsic part in determining the outcomes to be achieved. To Snowclone John F. Kennedy: ‘Ask not what your destination can do for tourism, ask what tourism can do for your destination’. Indeed, the visitation process involves the demand-driven co-creation or co-production between visitors (resident, day and overnight) and hosts, mostly based on the use of public goods. The complexity of this visitation system with its myriad stakeholders means that there cannot be a single tourism or visitation policy, but that there must be different policies that intervene at different points in the system and create an impact. Thus, policy formulation must be context-specific, individualised and take into account the interdependence among policies to achieve the desired outcomes.

The purpose of microcredit is to offer small loans to people who are not covered by traditional financial channels. It can facilitate entrepreneurship, boosting local socio-economic development and improving environmental and political factors.

This paper aims to analyse microcredit in Italy, focusing on a project based in Rione Sanità, Naples. Rione Sanità is one of the poorest areas of Southern Italy, displaying high rates of criminality and unemployment, especially among youth, women, migrants and the vulnerable. The district is renowned for its fine and ancient handicrafts, food, trade and historical heritage – potential drivers for boosting tourism in the area. Qualitative methodologies were used to collect primary data through field visits and interviews with project bankers, local businesses, artisans, associations and religious representatives, project volunteers, as well as participation at local meetings. These data were corroborated by budget analysis based on the project's accounting.

The study shows encouraging results for the project and policy prospects. Despite the tiny starting numbers, there emerges a significant potential for microcredit to spread in the district, as in Southern Italy, providing an effective strategy to combat unemployment, usury and criminality, yielding community development and favoring broad societal challenges.

With this evidence, the paper attempts to shed some light and verify the potential of microfinance projects as a driver of sustainable development and ethical finance in poor areas of developed countries.

In the process of making agricultural production decisions in rural households, severe weather conditions, either extreme cold or heat, may squeeze the labor input in the agricultural sector, leading to a reallocation of labor between the agricultural and non-agricultural sectors. By applying a dataset with a wide latitude range, this study empirically confirms the influence of extreme temperatures on the agricultural labor reallocation, reveal the mechanism of farmers’ adaptive behavioral decision and therefore enriches the research on the impact of climate change on rural labor markets and livelihood strategies.

This study utilizes data from Chinese meteorological stations and two waves of China Household Income Project to examine the impact and behavioral mechanism of extreme temperatures on rural labor reallocation.

(1) Extremely high and low temperatures had led to a reallocation of labor force from agricultural activities to non-farm employment, with a more pronounced effect from extreme high temperature events. (2) Extreme temperatures influence famers’ decision in abandoning farmland and reducing investment in agricultural machinery, thus creating an interconnected impact on labor mobility. (3) The reallocation effect of rural labor induced by extreme temperatures is particularly evident for males, persons that perceives economic hardship or labor in economically active areas.

By applying a dataset with a wide latitude range, this study empirically confirms the influence of extreme temperatures on the agricultural labor reallocation, and reveals the mechanism of farmers’ adaptive behavioral decision and therefore enriches the research on the impact of climate change on rural labor markets and livelihood strategies.

This contribution responds to the call of various researchers for a shift in Responsible Management Education (RME) to adopt a more human-centered and less organizational-centered approach. Service learning (SL) is introduced as a possibility to offer didactical opportunities for participants to connect real-world experiences to system thinking in various ways. We suggest an approach called a “Prism of Reflections” to pique participants' hermeneutical, technical, and emancipatory interests so they can delve deeply into local social and environmental issues and be able to connect them to broader global issues as encapsulated in the Sustainable Development Goals (SDGs). We exemplify our method by demonstrating how students reflect on their experiences working at food banks, and how they relate to concerns of sustainability, poverty, and access to food. Our research suggests that this approach offers a way to situate organizational thinking and instrumental reasoning in a larger framework that considers the aims of hermeneutics, technical and emancipatory discourses. Our findings demonstrate that there are conflicts and dissonances when connecting intersubjective real-world perceptions to emancipatory interests and technical knowledge, particularly when it comes to challenges in the realm of food.

The purpose of the study is to evaluate mobile apps developed to prevent food waste from the perspective of responsible production and consumption.

The study employs qualitative methods, and the dataset consists of the 1,382 content and online reviews and comments for 11 mobile apps in addition to 30 in-depth interviews conducted with consumers and restaurant managers/owners using the apps.

Mobile apps are proving to be popular with people, inviting users to fight food waste in a sustainable and responsible way. While users' main motivations for becoming food waste fighters are responsibility, positive emotions and economic sensitivity, restaurants' motivations are different, such as social responsibility, innovative business models, attracting new customers and cost orientation.

Creating digital value and contributing significantly to sustainability in the food industry, mobile apps offer different opportunities to the market. By supporting and encouraging the use of these apps, businesses can reduce food waste, prevent financial losses and offer consumers more choice and variety.

The present study aims to address this lack of empirical research based on a holistic perspective by providing a multidimensional assessment. Such an approach should contribute to a thorough understanding of the motivations that drive users and restaurants to become food waste fighters.