Urban Bias Research Articles

The Urban Bias in Latin American Avoidable Mortality

The Urban Bias in Latin American Avoidable Mortality

Agrarian Platform Capitalism: Digital Rentiership Comes to Farming

AbstractWith the rise of digital technologies, a political‐economic configuration recognised as “platform capitalism” has raised concerns over monopolistic tendencies, lack of accountability, expanded rentiership, workers’ precarity, and more. Existing analyses, however, show a distinctly urban bias—centring on housing, transportation, retail, and gig labour—and have yet to engage with the agrarian dimensions of this phenomenon despite considerable potential impacts on the future of farming. Here we begin the process of theorising agrarian platform capitalism, offering a typology of platforms in the agri‐food sector, and bringing together critiques of platform capitalism with the distinctive features of agrarian political economy. Our analysis identifies four prominent characteristics of agrarian platform capitalism which largely corroborate existing critiques albeit with some distinctive contours. As in other sectors, platforms intensify rentiership regarding both real estate and digital assets. Agricultural platforms also display a familiar tendency to thrive in spaces of regulatory retreat and are in some cases even endorsed by regulatory agencies, highlighting the potential for public–private platformisation. Some agricultural platform companies deploy populist rhetoric beyond established tropes of consumer welfare, latching onto farmers’ deep frustrations with the highly concentrated agribusiness sector. Efforts to reign in agrarian platform power may be further constrained by legitimising discourses of hunger relief and sustainability.

Effects of economic growth target on the urban–rural income gap in China: An empirical study based on the urban bias theory

Urban–rural imbalance is a common phenomenon, and its prominent manifestation is the sustained expansion of the urban–rural income gap. For emerging countries, this gap encountered during the development process should be addressed by promoting development, which also puts forward high requirements for economic management. Inspired by political economy theory and the widening urban–rural income gap in China, this study constructs a theoretical framework of “purpose-behavior-performance-structure” based on the urban bias theory, and then quantitatively examines the effect of economic growth target on the urban–rural income gap based on panel data at the provincial level. In addition, we explore the mechanism by which economic growth targets affect the urban–rural income gap with the mediating effect model, and investigate the impact of marketization and spatial structure on the relationship between economic growth targets and urban–rural income gap with the moderating effect model. The results show that the economic growth target significantly contributes to the widening urban–rural income gap in the face of the political promotion incentives and economic growth target accountability system, and this effect exhibits substantial spatiotemporal heterogeneity. In addition, fiscal expenditure, fixed asset investment, and labor mismatch under urban bias are important mechanisms that lead to the widening urban–rural income gap, and the marketization level and spatial structure significantly affect this relationship. We put economic growth management, urban bias, and urban–rural income gap into the “purpose-behavior-performance-structure” theoretical framework, which provides a new explanatory perspective for understanding the urban–rural income gap in emerging economies, and new policy insights for rational economic growth management.

Urban bias in political tournaments in China: Implications for urban land markets

This paper studies the applicability and implications of the political tournament model in Chinese cities by arguing that there is urban bias in political tournaments, especially in prefecture-level cities. Urban district leaders have to cooperate more and compete less than their counterparts in counties. The implication is that the percentage of urban districts is positively associated with monopolistic behavior of local government. Based on land leasing data of 290 prefecture-level cities from 2004 to 2014 (except 2012), it is found that the percentage of districts is positively associated with the speed of land price decline in leases through tender, auction and listing (TAL). This result supports the urban bias hypothesis in the context of the Coase Conjecture. It is also found that the total number of districts or population size is negatively associated with the speed of land price decline.

Adjustment of the urbanization bias in surface air temperature series based on urban spatial morphologies and using machine learning

Urbanization bias is one of the main systematic biases in surface air temperature (SAT) in climate change monitoring. The traditional linear approach assumes that the impact of urbanization on SAT sequences increases at a uniform rate, but this contradicts the real urbanization processes that occurred around observation stations. This paper puts forward a new adjustment method by using urban spatial morphologies and a random forest (RF) model. Results showed a warming rate of 26.2% (59.1%) during 1991–2020 (2006–2020) that was caused by urbanization. The RF model achieved better performance between urbanization bias and urban morphologies around 4 km buffer areas of the national meteorological stations than other buffers. It was noted that the importance analysis illustrated the urban horizontal morphologies had a stronger explanation for urbanization bias than vertical morphologies. Finally, the importance values were introduced into the traditional adjustment method. Taking Hefei as an example, the adjustment results obtained using the new method reveal that the urbanization bias maintains dynamic consistency with the observed changes in the surrounding environment of the target station. The new adjustment method might help improve our understanding of the relationship between human activities and regional climate change and also would provide technical references for the protection of the observation environment and the construction of reference climate data.

Measuring Levels of Infrastructure Development and its Impact on Regional Growth - Insights from Indonesia

Infrastructure development is a linchpin for regional growth, especially in emerging economies like Indonesia. There have been many debates over the necessity of infrastructure investment over direct economic stimuli. This study delves into the critical role of infrastructure in shaping regional dynamics in Indonesia. We employ the Infrastructure Development Index (IDI), a composite metric amalgamating various infrastructure clusters into a singular value. A sample of strategic region in Indonesia are observed in this research, classified into urban and non-urban areas to highlight the different levels of impact the infrastructure development brings. Several regional development indicators such as: (a) economic growth; (b) poverty rate; (c) gini index; (d) human development index; and (e) unemployment rate is used to explore its relationship with infrastructure development. This research shows a nuanced pattern where mainly urban areas exhibiting higher IDI value and higher impact on its regional growth and development. While non-urban areas still experiencing lower impact due to the difficulties in infrastructure access and the disparity of access between the areas. It indicates the prevailing urban bias in infrastructure development, needing an improved urban-rural linkage on overall economic input-output processes. In sum, this study provides further insights into the pivotal role of infrastructure in regional development in Indonesia, offering a roadmap for informed decision-making in the pursuit of comprehensive and sustainable regional progress.

Unfulfilled promises: reconsidering World Bank assistance projects as drivers of inclusive development in sub-Saharan Africa

ABSTRACT ‘Urban bias’ has been a common criticism of World Bank (WB) assistance, but to what extent this will lead to non-inclusive development remains unclear. Measuring the multidimensional poverty gap of rural individuals in 31 sub-Saharan Africa (SSA) countries compared to the average level of urban areas in their respective first-order administrative divisions, we introduce a spatial-temporal estimation strategy and explore whether and how the WB assistance projects from 2000 to 2014 can impact SSA’s inclusive growth. Our findings suggest that while WB assistance promotes local economic growth and reduces poverty, it widens the poverty gaps between urban and rural, and even among different income groups within urban areas. Furthermore, our mechanism analysis reveals that the expanding opportunity gap between rural and urban residents is a critical channel for the non-inclusiveness of WB assistance.

Bridging the Gap: Analyzing the Relationship between Environmental Justice Awareness on Twitter and Socio-Environmental Factors Using Remote Sensing and Big Data

Mounting awareness of the discriminatory distribution of environmental factors has increasingly placed environmental justice at the forefront of discussions on sustainable development, but responses to these disparities are often too little, too late. Remote sensing has emerged as a potential solution to this problem, capitalizing on the ability to capture high-resolution, spatially explicit data in near-real time. However, a conventional reliance on physical measurements and surface-level analyses risks overlooking the experiences and perceptions of affected communities. It is against this backdrop that the potential integration of remote sensing imagery and socially sensed big data such as social media data assumes a novel and promising role. This study aims to discern the feasibility, opportunities, and implications of integrating the spatial insights provided by remote sensing with the experiential narratives shared on social media platforms, bridging the gap between objective environmental data and community-driven perspectives. We explore this subject in two ways, analyzing the geographic relationship between environmental justice Tweets and environmental justice factors, and reviewing Tweets produced during an extensive wildfire. Remote sensing indexes for green and blue space were reviewed and tested, selecting the measures of best fit to act as independent variables alongside traditional environmental justice factors in the broader analysis. Results from regression models indicate a negative relationship between the number of Tweets utilizing environmental justice relevant terms and the presence of ecosystem services as captured by an NDMI, suggesting a broad awareness of injustice and a relationship between remote sensing and social media. However, there is simultaneously a negative relationship between socially vulnerable populations and Tweets with environmental justice words. This suggests that generally, there is discussion on Twitter about injustice when resources are not present, but the voices of vulnerable populations are often less visible, either as a result of urban bias or a lack of concern for injustices due to habitual ignorance. Our study demonstrates the potential for integrating remote sensing imagery and social sensing data to play a substantial role in detecting injustices and corroborating data collected through community science initiatives.

Assessment of Primary Health care Facilities on readiness for Antenatal Care and Childhood Immunization Services in Zamfara State, Nigeria

Effective maternal and child healthcare delivery centers on availability of essential medical equipment, drugs and trained medical personnel in health facilities. Poor funding, inadequate supply of physicians, essential equipment and drugs as well as mismanagement characterized healthcare delivery system in Nigeria. This study assessed public primary health care facilities on readiness of Antenatal Care (ANC) and childhood immunization services in Zamfara State, Nigeria. Based on theoretical orientation of Political Economy of Health Care, quantitative and qualitative data were used for the study. Descriptive statistic was used for quantitative data analysis using Statistical Package for Social Sciences (SPSS), while ATLASti8 software was used for qualitative data. The study found most of the visited health facilities offered various components of ANC and childhood immunisation services but did not comply with the current eight (8) ANC visits as recommended by WHO. Other essential services including hepatitis B tests, breast examination, Fansida for malaria prevention, free folic acid and iron supplements, insecticide treated nets, weight scale, ROTA vaccine and midwives were grossly inadequate in most health facilities. The study also found urban bias in terms of distribution of healthcare staff in Zamfara State. This study recommends that Zamfara State government, non-governmental organisations and donor agencies should urgently channel their efforts to PHC centers and address the challenges confronting primary health care centers in Zamfara State.

“Their lives don't matter to politicians”: The necropolitical ecology of Thailand's dangerous and unequal roads

Thailand is consistently ranked as having the worst road safety record in Asia, and one of the worst globally. Most deaths are of the rural poor, a function of necropolitical ecology. A primary factor is the materiality and design of the road system, built to increase mobility rather than improve safety, and lacking a hierarchical structure. Highways are poorly integrated into both rural areas and Thai cities, and ignore the needs of motorcycle drivers, who comprise most road users and victims. Traffic regulations are not enforced by poorly paid police officers, without adequate resources or ability to enforce traffic fines, and who engage in corrupt practices. This is compounded by inadequate driving education. These factors are complicated and caused by Thailand's fragmented and weak governance. A plethora of agencies do not cooperate and have overlapping mandates. No lead agency has been given sufficient power to improve governance of the system. The public blame drivers rather than the state. Political leadership, in favour of quick results, has attached little importance to this issue and consequently devoted insufficient resources to address it, an inaction, ignorance and urban bias that have contributed to unnecessarily high levels of rural injuries and mortality.

Spatially Explicit Correction of Simulated Urban Air Temperatures Using Crowdsourced Data.

Urban climate model evaluation often remains limited by a lack of trusted urban weather observations. The increasing density of personal weather sensors (PWSs) make them a potential rich source of data for urban climate studies that address the lack of representative urban weather observations. In our study, we demonstrate that carefully quality-checked PWS data not only improve urban climate models' evaluation but can also serve for bias correcting their output prior to any urban climate impact studies. After simulating near-surface air temperatures over London and south-east England during the hot summer of 2018 with the Weather Research and Forecasting (WRF) Model and its building Effect parameterization with the building energy model (BEP-BEM) activated, we evaluated the modeled temperatures against 402 urban PWSs and showcased a heterogeneous spatial distribution of the model's cool bias that was not captured using official weather stations only. This finding indicated a need for spatially explicit urban bias corrections of air temperatures, which we performed using an innovative method using machine learning to predict the models' biases in each urban grid cell. This bias-correction technique is the first to consider that modeled urban temperatures follow a nonlinear spatially heterogeneous bias that is decorrelated from urban fraction. Our results showed that the bias correction was beneficial to bias correct daily minimum, daily mean, and daily maximum temperatures in the cities. We recommend that urban climate modelers further investigate the use of quality-checked PWSs for model evaluation and derive a framework for bias correction of urban climate simulations that can serve urban climate impact studies.

Economic openness, financial bias, and the urban–rural income gap

AbstractWe develop a theoretical model to analyze the impact of economic openness and financial bias (i.e., open sector bias and urban area bias) on the urban–rural income gap. Our theoretical model puts forth three propositions, which we empirically test using a dynamic panel data model. We use data from 30 provinces in China spanning from 2004 to 2017 for our empirical analysis. Our results reveal a U‐shaped relationship between the urban–rural income gap and the level of regional economic openness. We also find an inverted U‐shaped relationship between the urban–rural income gap and the open sector bias of regional financial development. Moreover, our analysis indicates a positive relationship between the urban–rural income gap and urban area bias in regional financial development. These three propositions hold true for both nominal and real measures of the income gap.

The Detection and Attribution of Northern Hemisphere Land Surface Warming (1850–2018) in Terms of Human and Natural Factors: Challenges of Inadequate Data

A statistical analysis was applied to Northern Hemisphere land surface temperatures (1850–2018) to try to identify the main drivers of the observed warming since the mid-19th century. Two different temperature estimates were considered—a rural and urban blend (that matches almost exactly with most current estimates) and a rural-only estimate. The rural and urban blend indicates a long-term warming of 0.89 °C/century since 1850, while the rural-only indicates 0.55 °C/century. This contradicts a common assumption that current thermometer-based global temperature indices are relatively unaffected by urban warming biases. Three main climatic drivers were considered, following the approaches adopted by the Intergovernmental Panel on Climate Change (IPCC)’s recent 6th Assessment Report (AR6): two natural forcings (solar and volcanic) and the composite “all anthropogenic forcings combined” time series recommended by IPCC AR6. The volcanic time series was that recommended by IPCC AR6. Two alternative solar forcing datasets were contrasted. One was the Total Solar Irradiance (TSI) time series that was recommended by IPCC AR6. The other TSI time series was apparently overlooked by IPCC AR6. It was found that altering the temperature estimate and/or the choice of solar forcing dataset resulted in very different conclusions as to the primary drivers of the observed warming. Our analysis focused on the Northern Hemispheric land component of global surface temperatures since this is the most data-rich component. It reveals that important challenges remain for the broader detection and attribution problem of global warming: (1) urbanization bias remains a substantial problem for the global land temperature data; (2) it is still unclear which (if any) of the many TSI time series in the literature are accurate estimates of past TSI; (3) the scientific community is not yet in a position to confidently establish whether the warming since 1850 is mostly human-caused, mostly natural, or some combination. Suggestions for how these scientific challenges might be resolved are offered.

Evidence of Urban Blending in Homogenized Temperature Records in Japan and in the United States: Implications for the Reliability of Global Land Surface Air Temperature Data

Abstract To reduce the amount of nonclimatic biases of air temperature in each weather station’s record by comparing it with neighboring stations, global land surface air temperature datasets are routinely adjusted using statistical homogenization to minimize such biases. However, homogenization can unintentionally introduce new nonclimatic biases due to an often-overlooked statistical problem known as “urban blending” or “aliasing of trend biases.” This issue arises when the homogenization process inadvertently mixes urbanization biases of neighboring stations into the adjustments applied to each station record. As a result, urbanization biases of the original unhomogenized temperature records are spread throughout the homogenized data. To evaluate the extent of this phenomenon, the homogenized temperature data for two countries (Japan and the United States) are analyzed. Using the Japanese stations in the widely used Global Historical Climatology Network (GHCN) dataset, it is first confirmed that the unhomogenized Japanese temperature data are strongly affected by urbanization bias (possibly ∼60% of the long-term warming). The U.S. Historical Climatology Network (USHCN) dataset contains a relatively large amount of long, rural station records and therefore is less affected by urbanization bias. Nonetheless, even for this relatively rural dataset, urbanization bias could account for ∼20% of the long-term warming. It is then shown that urban blending is a major problem for the homogenized data for both countries. The IPCC’s estimate of urbanization bias in the global temperature data based on homogenized temperature records may have been low as a result of urban blending. Recommendations on how future homogenization efforts could be modified to reduce urban blending are discussed. Significance Statement Most weather station–based global land temperature datasets currently use a process called “statistical homogenization” to reduce the amount of nonclimatic biases. However, using temperature data from two countries (Japan and the United States), we show that the homogenization process unintentionally introduces new nonclimatic biases into the data as a result of an “urban blending” problem. Urban blending arises when the homogenization process inadvertently mixes the urbanization (warming) bias of the neighboring stations into the adjustments applied to each station record. As a result, the urbanization biases of the unhomogenized temperature records are spread throughout all of the homogenized data. The net effect tends to artificially add warming to rural stations and subtract warming from urban stations until all stations have about the same amount of urbanization bias.

Implementering av DBT i en rural kontekst

Background and purpose: Dialectical behaviour therapy (DBT) is a widespread evidence-based method of treating emotionally unstable personality disorder, but the method has barely been studied in a rural context. The purpose of this study is to identify rural challenges for the four treatment channels of DBT, to pinpoint implicit urban starting points in DBT, and to suggest possible adaptations of DBT in rural areas, based on Finnmark as a case. Method: The authors, who are clinicians with extensive experience in psychiatric healthcare in Finnmark, integrates findings from previous research of culturally adapted DBT for rural cultures with local clinical experience in accordance with the declaration of evidence-based psychological practice. Our methodical basis is traditional literary study, case study, autoethnography and theory generation. Results: DBT in its original format falls short in rural areas without adaptations. We found that key challenges for implementations of DBT in our local clinical practise are geographical distances, lack of personnel, difficulties in maintaining anonymity in group therapy, difficulties with group guidance and lack of continuity in treatment due to high turnover. Vi lay out possible adjustments, such as sectional skill training, short periodic admissions at DPS with intensive skill training, pragmatic use of phone consultations, digital treatment (and guidance) and inclusion of other departments in treatments. Conclusion: DBT is a principle-driven method which is originally designed for an urban setting, but which is possible to adapt to a rural context such as Finnmark. Possible challenges are geographical distance, lack of continuity, personnel shortage and lack of anonymity (group treatment). Keywords: rural, DBT, pragmatic psychology, geographical narcissism, urban bias

Evidence from systematic reviews on policy approaches to improving access to medicines

The prevailing frameworks on access to medicines advise global procurement as a solution by assuming the presence of medicines on the global market. Yet access to medicines remains challenging, especially in developing countries. This is a global worry because the UN considers limited access to essential medicines as one of the five indicators of securing the right to health. To fill a research gap in health system studies and inform policymaking, we synthesized evidence from systematic reviews of how government policies affect low- and middle-income country (LMIC) medicine access. We chose a rapid review approach to reduce timelines and avoid missing policy “windows of opportunity.” To include only studies published after the start of COVID-19, we chose systematic reviews published between 2019 and November 2nd, 2022. This was also in line with recommendations in the literature to look at recent systematic reviews. The themes were grouped using a thematic and textual narrative approach. This review included 32 studies that examined access to medicine from various perspectives. Both supply- and demand-side policies are needed to improve medical access. LMICs cannot afford medicines, and supply never meets demand. LMICs will continue to struggle with pharmaceutical pricing due to their limited bargaining power. The urban bias in health facilities and policy changes reduce medicine availability and use. Leaders must make policy decisions to sustain domestic funds. Policymakers should consider that organizations may act against policy goals. Instead of copying developed nations, LMIC governments must develop multipronged strategies to address their unique challenges.

Placing the platform economy: the emerging, developing and upgrading of Taobao villages as a platform-based place making phenomenon in China

Abstract The actions of platforms and their all-inclusive impact on place development is coined platform-based place making in this article. We use the actor-network theory to analyse a typical e-commerce platform-based place making, namely the emergence, development and upgrading of Taobao villages in China, and to explore the mechanisms of platform place making power. Our study shows that platforms ‘make’ places by platform–place interactions of progressively expanding the enrolment of intra-regional and extra-regional actors. In addition, our research advances the actor-network theory and its application and transcends the urban and production-side bias of economic geography.

Repositioning urban bias: Non‐state providers' use of spatialised networks in Bangladesh

AbstractUrban bias theory predicts urban areas of developing countries receive disproportionately more resources than rural areas due to their concentration of numerically large, politically important “vote banks.” This has not been the case in Bangladesh. This study finds that this variation occurs due to non‐state providers (NSPs) changing the landscape of resource allocation. Operating on the premise that state control leads to more services in urban areas, urban bias fails to account for NSPs as critical service providers. Employing a grounded theory strategy to explore urban‐rural dynamics in service provision and to build on urban bias theory, this research highlights interactions between state and non‐state actors. It argues that spatialized political networks, networks of formal and informal leadership more difficult to access in urban areas, influence the locality of service provision. Though NSPs recognize increased need in urban areas of Bangladesh, their interventions in those areas remain peripheral due to differing structures of government accountability and differing levels of community acceptance facilitating these networks. The need for NSPs to adapt their activities to restrictive governance mechanisms reflects the changing space for NSPs in the context of semi‐democratic regimes.

Green Urban Development—Concepts and Praxis

This paper focuses on the green urban development paradigm. Its inspiration is the work of the Global Green Growth Institute (GGGI). It cites perspectives developed by GGGI HQ in Seoul, Korea, enriched by its praxis in Uganda. The latter is through the European Union (EU) funded project, greening urbanisation and industrialisation (2020-24). The argument is in five sections, beyond its introduction and conclusion. Firstly is the green policy impact with an urban bias. Secondly is the green practical impact, explicitly urban, from a responsive institutional perspective. Thirdly, the responsive description is taken to the smart city idea. Fourthly is infrastructure, specifically its shaping or reinforcing urban form. Finally is superstructure—in this case, green buildings. Each section is offered as a step towards a greater appreciation and potential application of green urban development.

Updated analysis of surface warming trends in North China based on in-depth homogenized data (1951-2020)

The reliability of climate change detection and research is significantly impacted by the inhomogeneity of surface climate observation data. However, there is an ongoing debate regarding whether comprehensive homogenization has been performed in large-scale homogenized data sets. In this study, we examined the homogeneity of the original maximum and minimum temperature (Tmax and Tmin) data for 662 meteorological stations in North China by using multiple methods and combining with metadata. The quantile matching method was employed to adjust the daily Tmax and Tmin series. In order to avoid the potential systematic bias resulting from homogenization, no reference series were introduced during the adjustment process. The adjustment results indicate that Tmin in North China is significantly affected by non-climatic factors, particularly station relocations and environmental changes around the stations. The application of homogenization in this study led to a notable increase in the overall temperature trends of the stations, with Tmin exhibiting a larger increase and the diurnal temperature range demonstrating a more significant downward trend. Based on the homogenized data, the annual and seasonal mean temperature trends in North China from 1951 to 2020 were re-evaluated. These temperature trends generally surpass those reported in previous research for the same period from 1961 to 2000. The higher estimate of temperature trends may be attributed to the recovered urbanization effect in the newly homogenized data. Thus, the obtained homogenization data still exhibit a significant urbanization bias that requires further assessment and adjustment.