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Development of urban-rural integration is essential to fulfill sustainable development goals worldwide, and comprehension about urban-rural integration types has been highlighted as increasingly relevant for an efficient policy design. This paper aims to utilize an unsupervised machine learning approach to identify urban-rural integration typologies based on multidimensional metrics regarding economic, population and social integration in China.

The study introduces partitioning around medoids (PAM) for the identification of urban-rural integration typologies. PAM is a powerful tool for clustering multidimensional data. It identifies clusters by the representative objects called medoids and can be used with arbitrary distance, which help make clustering results more stable and less susceptible to outliers.

The study identifies four clusters: high-level urban-rural integration, urban-rural integration in transition, low-level urban-rural integration and early urban-rural integration in backward stage, showing different characteristics. Based on the clustering results, the study finds continuous improvement in urban-rural integration development in China which is reflected by the changes in the predominate type. However, the development still presents significant regional disparities which is characterized by leading in the east regions and lagging in the western and central regions. Besides, achievement in urban-rural integration varies significantly across provinces.

The machine learning techniques could identify urban-rural integration typologies in a multidimensional and objective way, and help formulate and implement targeted strategies and regionally adapted policies to boost urban-rural integration.

This is the first paper to use an unsupervised machine learning approach with PAM for the identification of urban-rural integration typologies from a multidimensional perspective. The authors confirm the advantages of this machine learning techniques in identifying urban-rural integration types, compared to a single indicator.

The purpose of this study is to examine whether PV uptake is associated with key housing market determinants and linked to socio-economic profiles. An abundance of extant literature has examined the role of solar photovoltaic (PV) adoption and user costs, with an emerging corpus of literature investigating the role of the determinants of PV uptake, particularly in relation to the built environment and the spatial variation of PV dependency and dissimilarity. Despite this burgeoning literature, there remains limited insights from the UK perspective on housing market characteristics driving PV adoption and in relation spatial differences and heterogeneity that may exist.

Applying micro-based data at the Super Output Area-level geography, this study develops a series of ordinary least squares, spatial econometric models and a logistic regression analysis to examine built environment, housing tenure and deprivation attributes on PV adoption at the regional level in Northern Ireland, UK.

The findings emerging from the research reveal the presence of some spatial clustering and PV diffusion, in line with several existing studies. The findings demonstrate that an urban-rural dichotomy exists seemingly driven by social interaction and peer effects which has a profound impact on the likelihood of PV adoption. Further, the results exhibit tenure composition and “economic status” to be significant and important determinants of PV diffusion and uptake.

Housing market characteristics such as tenure composition across local market structures remain under-researched in relation to renewable energy uptake and adoption. This study examines the role of housing market attributes relative to socio-economic standing for adopting renewable energy.

This study aims to investigate consumers’ construction of food localness through the politics of belonging in a regional context.

Following a socio-spatial lens and considering the “realm of meaning” of place, this research focusses on local consumers’ lived meanings of “local” food choice, and hence adopts a phenomenological approach to the data collection and analysis of 20 in-depth interviews with residents of the Italian region of Marche.

Drawing on Trudeau’s (2006) politics of belonging, this study reveals three interconnected themes which show how local consumers articulate a local food “orthodoxy” and how their discourses and practices draw and maintain a boundary between local and non-local food, whereby local food is considered “autochthonous” of rural space. Thus, this study’s participants construct a local food landscape, conveying rural (vs urban) meanings through which food acquires “localness” (vs non-“localness”) status.

There exists further theoretical opportunity to consider local consumers’ construction of food localness through the politics of belonging in terms of non-representational theory (Thrift, 2008), to help reveal added nuances to the construction of food localness as well as to the complex process of formulating place meaning.

The findings provide considerable scope for food producers, manufacturers and/or marketers to differentiate local food products by enhancing consumers’ direct experience of it in relation to rural space. Thus, enabling local food producers to convey rural (vs urban) meanings to consumers, who would develop an orthodoxy guiding future choice.

The findings enable regional promoters and food policymakers to leverage the symbolic distinctiveness of food autochthony to promote place and encourage consumers to participate in their local food system.

By using the politics of belonging as an analytical framework, this study shows that the urban–rural dichotomy – rather than being an obsolete epistemological category – fuels politics of belonging dynamics, and that local food consumers socially construct food localness not merely as a romanticisation of rurality but as a territorial expression of the contemporary local/non-local cultural conflict implied in the politics of belonging. Thus, this study advances our theoretical understanding by demonstrating that food “becomes” local and therefore, builds on extant food localness conceptualisations.

Social sciences have discussed the host–guest relation from many theoretical lenses and perspectives. Violence as well as local crime has been studied as one of the major risks concerning tourism security. Anyway, less attention was given to homeless people and their interaction with foreign or local tourists. The purpose of this paper is oriented to explain how globalization has winners and losers, in which case, as noted, thousands of persons are excluded from the formal labor marketplace or the economic system year by year.

This is a conceptual paper that discusses critically not only the recent advances of sociology in urban tourism but also the connection between homeless people and tourists.

There is an urban underclass formed by those who have been excluded from the economic system. What is more important, such an underclass situates nearby luxury hotels and tourist destinations creating serious contradictions or zones of disputes. These contradictions have been approached by different sociologists since the turn of the 20th century.

The question of sustainability, as well as the idea of liveable cities, and the efficient organization of the city, have occupied a central position in the academic debate, above all after the COVID-19 pandemic. In the present paper, the authors put in dialogue the contributions of Marc Auge with Zyggy Bauman toward a new understanding of this postmodern phenomenon.

Based on the metaphor of vagabonds and tourists, we give a snapshot of the problem of homelessness in Buenos Aires city and its effects on the tourism industry. Unlike other English-speaking countries where the cities are actively organized by the state, Buenos Aires city lacks a planned program to regulate and relocate homeless people. They dwell in nonplaces nearby tourists sleeping in the streets near luxury hotels (but for sure escaping any planning or governmental control).

Growing food insecurity is a leading cause of fatalities, particularly in developing nations like Sub-Saharan Africa and Southeast Asia. However, the rising energy consumption and carbon dioxide (CO2) emissions are mostly associated with food production. Balancing the trade-offs between energy intensity and food security remains a top priority for environmentalists. Despite the critical role of the environment in food security, there is a scarcity of substantial studies that explore the statistical connections among food security, CO2 emissions, energy intensity, foreign direct investment (FDI) and per capita income. Therefore, this study aims to provide more precise and consistent estimates of per capita CO2 emissions by considering the interplay of food security and energy intensity within the context of emerging economies.

To examine the long-term relationships between CO2 emissions, food security, energy efficiency, FDI and economic development in emerging economies, this study employs correlated panel-corrected standard error, regression with Newey–West standard error and regression with Driscoll–Kraay standard error models (XTSCC). The analysis utilizes data spanning from 1980 to 2018 and encompasses 32 emerging economies.

The study reveals that increasing food security in a developing economy has a substantial positive impact on both CO2 emissions and energy intensity. Each model, on average, demonstrates that a 1 percent improvement in food security results in a 32% increase in CO2 levels. Moreover, the data align with the Environmental Kuznets Curve (EKC) theory, as it indicates a positive correlation between gross domestic product (GDP) in developing nations and CO2 emissions. Finally, all experiments consistently demonstrate a robust correlation between the Food Security Index (FSI), energy intensity level (EIL) and exchange rate (EXR) in developing markets and CO2 emissions. This suggests that these factors significantly contribute to environmental performance in these countries.

This study introduces novelty by employing diverse techniques to uncover the mixed findings regarding the relationship between CO2 emissions and economic expansion. Additionally, it integrates energy intensity and food security into a new model. Moreover, the study contributes to the literature by advocating for a sustainable development goal (SDG)-oriented policy framework that considers all variables influencing economic growth.

This paper aims to test the causal relationship between urban-biased policy and urban-rural income gap and further examine the moderating role of government intervention.

Based on the provincial Government Work Reports and the long-term policy practice of implementing the target responsibility system, the authors construct a unique indicator of urban-biased policy in China. Further, applying the panel data of 30 Chinese provinces in 2003–2018, the authors explore the causal relationship between urban-biased policy and urban-rural income gap.

The results show that urban-biased policy has contributed to the widen urban-rural income gap in China, which supports Lipton's urban-biased hypothesis. Further research shows that the stronger the government intervention, the bigger the role of urban-biased policy in widening urban-rural income gap.

On the one hand, this study not only investigates the direct effect of urban-biased policy on urban-rural income gap, but also examines the moderating effect from the perspective of government intervention, which helps to enrich the relevant studies of urban-biased theory. On the other hand, the authors' findings provide the latest empirical evidence for urban-biased policy to widen urban-rural income gap and presents a reference and warning for China and other developing countries about balancing the relationship between equity and efficiency during economic development.

The digital economy is expected to revive the countryside and reduce the current level of urban–rural inequality. Nevertheless, whether rural e-commerce can narrow the urban–rural income gap still requires further analysis. The purpose of this paper is to clarify whether this goal is, in fact, being achieved.

Taobao villages have become the epitome of rural e-commerce development in China. Therefore, this paper matches the data of Taobao villages and the data of prefecture-level cities from 2014 to 2019, and employs a two-way fixed effect model, nonlinear model, instrumental variable model and interactive fixed effects model to explore the impact of rural e-commerce on the urban–rural income gap.

Firstly, the ability of urban residents to share rural e-commerce development is higher than that of rural residents, which actually widens the urban–rural income gap. Secondly, the migration to cities of rural families that have profited from e-commerce, and the return of working-class people to the countryside, are two factors that are contributing to the widening of the urban–rural income gap. Thirdly, the farther the distance from the urban area and the higher the spatial agglomeration of the rural e-commerce cluster is, the weaker the impact on widening the urban–rural income gap will be. Finally, while industrial-led rural e-commerce is responsible for widening the urban–rural income gap, agricultural-led rural e-commerce has no significant impact on the urban–rural income gap.

To the best of the authors' knowledge, this paper is the first to analyze the impact of rural e-commerce on the urban–rural income gap from the perspective of the coverage of Taobao villages. This empirical study will enrich existing theoretical perspectives on urban–rural integration under the backdrop of the digital economy.

Since the opening of China (aka, reform and opening-up), a great number of rural residents have migrated to large cities in the past 40 years. Such a one-way population inflow to urban areas introduces nontrivial social conflicts between urban natives and migrant workers. This study aims to investigate the most discussed topics about migrant workers on Sina Weibo along with the corresponding sentiment divergence.

An exploratory-descriptive-explanatory research methodology is employed. The study explores the main topics on migrant workers discussed in social media via manual annotation. Subsequently, guided LDA, a semi-supervised topic modeling approach, is applied to describe the overall topical landscape. Finally, the authors verify their theoretical predictions with respect to the sentiment divergence pattern for each topic, using regression analysis.

The study identifies three most discussed topics on migrant workers, namely wage default, employment support and urban/rural development. The regression analysis reveals different diffusion patterns contingent on the nature of each topic. In particular, this study finds a positive association between urban/rural development and the sentiment divergence, while wage default exhibits an opposite relationship with sentiment divergence.

The authors combine unique characteristics of social media with well-established theories of social identity and framing, which are applied more to off-line contexts, to study a unique phenomenon of migrant workers in China. From a practical perspective, the results provide implications for the governance of urbanization-related social conflicts.

Since 2017, China's digital economy has accounted for more than 30% of the country's GDP. The digital economy has become the main driving force of China's economic development. Moreover, the digital economy has also changed the traditional modes of production and distribution between urban and rural areas. This paper aims to explore the influential mechanism of digital economy infrastructure (DEI) on the urban-rural income gap (URIG).

By analyzing the theoretical model of the URIG, this paper constructs a theoretical analysis framework and clarifies the key roles of rural land circulation (RLC) and resident population urbanization (RPU) in the relationship between DEI and the URIG.

The DEI can effectively reduce the URIG; the regression coefficient (RC) was −0.109. The reduction effect is mainly reflected in: 1) the wage income gap between urban and rural residents (RC = −0.128) and 2) the net property income gap of urban and rural residents (RC = −0.321). Also, for the spatial spillover effect, the path effect of “DEI – RLC – URIG” is almost equal to the path effect of “DEI – RPU – URIG”; for the local effect, the path effect of the former is far smaller than the latter. Moreover, when the RPU reaches the threshold of 86.29%, the DEI will expand the URIG (RC = 0.201).

This paper proposes a theoretical framework for the impact of DEI on the URIG, explores the mechanism of RLC and RPU in the DEI and URIG and enriches the theory of traditional research on URIG.

This study investigates the factors influencing household financial choices in Malawi. The authors also compare how household financial decisions differ in urban and rural areas.

The authors utilize the logit model to examine the factors that influence household financial decisions using the Malawi Integrated Household Survey 2019–20, while Oaxaca–Blinder decomposition is used to estimate the variations in household financial decisions between urban and rural areas.

The authors find that the likelihood of saving increases with income, secondary and tertiary education, and age. The likelihood of saving also decreases with household size and remittances. Additionally, the authors report that marriage reduces the likelihood of loans, whereas sex, age, and income raise the likelihood of loans. According to this study’s findings, income discrepancies between urban and rural samples account for most observed household financial variations. The authors also find that most of the observed variations in household financial decision-making between urban and rural households are reduced when income equality, participation in agriculture, university education, and household size are considered.

Using data from the Malawi Integrated Household Survey 2019–20, this research analyzes the components that affect household financial decisions. While most studies only look at one component of household finances, this study concurrently addresses debt and savings. The study also evaluates whether changes in the variables between urban and rural households impact those households' financing choices.