City Level Research Articles

Recent progress in hukou reform and labor market integration in China: 1996–2022

To investigate the household registration system (hukou) reform in China, it is important to quantify the thresholds of obtaining hukou in urban areas at the city level. We build a new index to measure the likelihood of obtaining urban hukou for migrants in each destination city, termed the “settlement probability”, based on individual information of floating population from the population census and hukou policies of 332 cities from 1996 to 2022. We find that the average settlement probability has been steadily increasing since 1999, reaching 91% in 2022, and 15 provinces have achieved low thresholds. We further build a simple general spatial equilibrium model with the factor of settlement probability to investigate the impacts of hukou reform. The empirical analysis shows there is a mismatch between settlement probability and settlement intentions. Moreover, we find that higher settlement probability is significantly correlated with more migrants' inflow, lower migrants' wages and higher urban housing prices, especially in more developed cities. Therefore, hukou reform is crucial for enhancing the integration of China's labor market.

Community Responsiveness on Climate Change and Natural Hazards Towards Sustainable Cities: An Empirical Study of Da Nang, Vietnam

Understanding of community responsiveness on climate change and the natural hazards is an essential component of urban planning and adaptation strategies. The coastal cities are seriously affected by the impact of rising sea levels and climate change-induced hazards. In this paper, we investigate the urbanization level on climate change and natural hazards awareness of local communities at the households’ level in Da Nang city, the largest municipality in Central Vietnam using 1903 surveyed households. The results showed that the urbanization level significantly influenced the local communities' responsiveness to climate change. Communities with robust infrastructure and strong economic status demonstrate greater resilience and awareness of climate change and natural hazards compared to others. Therefore, it is recommended to prioritize enhancing local people’s responsiveness to develop bottom-up climate change response strategies and policies, instead of enforcing top-down decisions.

Analisis Kelayakan Penerapan Inklusivitas Kota Ramah Pejalan Kaki di Surabaya: Studi Komparatif dengan Kota Bandung dalam Teori Kepublikan

This research discusses the concept of a pedestrian-friendly city or Walkable City in Surabaya, relating it to Bandung. Based on the Most Liveable City Index (MLCI), Bandung ranks at the Average Tier City level, indicating it has the best city implementation in Indonesia. Similarities in city layout, road conditions, and the economic characteristics of the population suggest that Surabaya and Bandung have similar city identities. This research employs qualitative methods with content analysis techniques to analyze data and interpret the meaning behind data and information from social media users. The findings reveal that Jalan Braga retains a historical impression and features pedestrian-friendly sidewalks, including inclusive public spaces and street designs. However, the lighting on Jalan Braga is dim, causing visitors to feel anxious at night. In Surabaya, especially on Jalan Tunjungan, the walkable city conditions are similar to Jalan Braga. However, the crossing lights are ineffective, and the lack of canopies along the sidewalks discourages walking during the day due to the extreme heat, which is a drawback for implementing a walkable city on Jalan Tunjungan.This research suggests that to improve safety, resilience, and sustainability in inclusive road and urban traffic design comprehensively, the integration framework needs to be expanded beyond conventional views—limited to technology or products, business or economics, and infrastructure or platforms—to encompass space, speed, and priority. Each of these factors directly influences accessibility, security, and social usability and has a reciprocal and interdependent relationship with the others.

Digital Infrastructure Construction and Improvement of Non-Farm Employment Quality of Rural Labor Force—From the Perspective of Informal Employment

The pivotal role of digital infrastructure as hardware support for fostering economic efficiency in the digital economy is widely acknowledged. However, it begs the question, can the development of digital infrastructure also advance social equity, particularly concerning horizontal equity, as exemplified by the quality of non-farm employment among rural laborers, which serves as a barometer for the fairness and inclusivity of the social opportunity landscape? This article delves into the ramifications of digital infrastructure development on the quality of non-farm employment for rural laborers. Initially, it conducts a theoretical exploration of the impact and mechanisms of digital infrastructure construction on non-farm employment quality within rural labor sectors, drawing upon the Todaro model framework and existing scholarly discourse. Subsequently, by integrating data on digital infrastructure construction at the prefecture-level city level with four periods of the China Family Panel Studies (CFPS) data spanning 2014 to 2020, employing various endogenous treatment methods including two-way fixed effects, sensitivity analysis, and instrumental variable techniques, it empirically tests and analyzes the internal mechanisms. The findings reveal that digital infrastructure construction plays a beneficial role in enhancing the quality of non-farm employment for rural laborers, encompassing both subjective perceptions and objective circumstances of non-farm work. Notably, it is observed that digital infrastructure construction significantly fosters improvements in the quality of informal employment among rural laborers, with notable disparities across gender and skill levels. This discovery exerts a positive influence on advancing the sustainable development of the labor market. Specifically, female rural laborers necessitate higher skill proficiency and educational attainment to attain commensurate benefits as their male counterparts. Moreover, caution is warranted regarding the potential for digital infrastructure construction to exacerbate existing power differentials and widen socioeconomic disparities through the perpetuation of the digital divide.

Nexus among government digital development, resource dependence, and carbon emissions in China

Government digital development uses data technology to empower data for economic and social benefits, making social governance more scientific and efficient. Using unbalanced panel data of Chinese cities from 2006 to 2019, this paper explores the relationship between government digital development, fossil energy dependence, and urban carbon emissions. The results show that government digital development can effectively curb urban carbon emissions. The " carbon curse " phenomenon does exist at the city level in China, and government digital development is an effective way to break the "carbon curse." Heterogeneity analyses show that the digitalization of government significantly reduces the carbon emissions of cities in the East-Central region and the Yangtze River Economic Belt. Promoting government digital development in large cities can effectively break the "carbon curse." In addition, government digital development can also curb urban carbon emissions through structural, innovation, and resource allocation effects. Therefore, due attention should be paid to the digital development of government, which in turn will leverage digital advantages and eliminate the adverse effects of fossil energy dependence. This is of profound theoretical and practical significance for curbing urban carbon emissions and promoting green economic development.

Who can access what? Uncovering urban inequality in access to service for senior citizens

The scarcity or lack of access to essential services at the local and neighbourhood levels in cities can result in significant spatial inequalities, as some areas and their residents can deal with disadvantages and a lower quality of daily life. In particular, the spatial distribution and the variety of amenities at the local scale represent an important feature of the liveliness of places. The local availability and access to essential services are particularly relevant for some demographic groups experiencing limited mobility or mobility poverty, such as older adults living in cities, and spatial disparities have been further exacerbated by the COVID-19 pandemic, which highlighted severe difficulties in accessing essential services. This work explores the issue focussing on the following question: who can access what depending on where they live in cities? Using Machine Learning and Spatial Autocorrelation applied to different data sources for spatial information on the location of urban amenities and Internet access, this work aims to identify the most underserved places in terms of the variety of available amenities and access to quality broadband in three European capital cities. A comparison to urban areas where high percentages of older adults reside makes it possible to identify where residents can locally access several essential services (green spaces, health care, and local shopping) and where this need cannot be satisfied because of a lack in the amenity variety available at walking distance to their home. The combination of underserved areas with a high concentration of senior residents identifies left-behind areas in these cities, where interventions on inequalities are most needed. Results can inform policies aiming at favouring fair access to services at the local scale, possibly including slow and active mobility modes, and in general to develop comprehensive and sustainable planning strategies for cities, leaving no place and no person behind.

Changing associations of built environment with usage of urban space due to the COVID-19 pandemic in the United States

This paper aims to analyze how the associations of built environment and usage of urban space changed due to the social restrictions during COVID-19 pandemic in the United States. A total of 57,185 census block groups (CBGs) in 459 cities were analyzed. Built environment elements including road network, green space, and land use associated with usage of urban space (measured by over two billion mobile phone data) before/during COVID-19 were evaluated at both CBG and city level. Our results showed drastic decreases of urban space usage, ranging from 0.25 % to 76.35 %, among cities during COVID-19 social restriction. Specifically, CBG-level road length-node ratio, road intersection index and land use densities contributed less to usage of urban space during COVID-19. Meanwhile, the heterogeneity of city-level road network, commercial, public places, as well as hospitals and health cares exhibited stronger relationship with usage of urban space. The heterogeneity of green space at both CBG and city level showed less associations with usage of urban space during COVID-19. The discrepancies of the built environment effects on usage of urban space between CBG and city level suggest the importance of varying-scale analysis. Future interventions should consider space usages and health inequality from a space-varying perspective for post-pandemic period.

Utilizing External Knowledge to Enhance Location Prediction for Twitter/X Users in Low Resource Settings

Accurate estimates of user location are important for many online services, including event detection, disaster management and determining public opinion. Neural network-based techniques have proven to be highly effective in predicting user location. However, these models typically require a large amount of labeled training data, which can be difficult to obtain in real-world scenarios. In this paper, we present two approaches to tackle the issue of limited training data when predicting city level location. First, we consider a self-supervised approach that trains a state level model without labeled data and then integrate this knowledge into the training data set used for city level predictions. Second, we explore the option of increasing the number of training examples by utilizing external resources to generate synthetic users . Finally, we combine these two strategies, exploiting the benefits of both. We empirically evaluate our proposed techniques on multiple Twitter/X data sets and show that our models perform significantly better than the state-of-the-art with improvements of up to 6% for Acc@161 and 8% for F1 score.

The M-Estimator and S-Estimator in Robust Improved Geographically and Temporally Weighted Regression for Modelling GRDP in West Java, Indonesia

The success of development in a region in Indonesia can be measured by economic growth, especially in the financial sector using the Gross Regional Domestic Product (GRDP) growth rate. The GRDP figure at the Regency and City level in West Java is one of Indonesia's highest and most diverse. This is due to various factors, including the geographical location of West Java, which is directly adjacent to DKI Jakarta, which is the center of the national economy. Although classified as high, the diversity of GRDP values between regions in West Java needs attention to equalize economic growth. The diversity of GRDP values can be modeled by the Improved Geographically and Temporally Weighted Regression (I-GTWR) method by taking samples in 2018-2022. The I-GTWR modeling method considers the influence of spatial heterogeneity and spatial-temporal interaction, which has been proven to produce better results than the GTWR method in modeling GRDP in Central Java in 2011-2015. This study also adds M-estimators and S-estimators to improve the model's performance and make it robust to outliers. The explanatory variables we use are Regional Original Revenue, General Allocation Fund, Foreign Investment, Regional Minimum Wage, Domestic Investment, Poverty, Per-Capita Expenditure, and Number of Job Vacancies. The analysis shows that the Robust I-GTWR model, especially the M-estimator, produces better model performance than the I-GTWR model in modeling West Java GRDP. The coefficient of determination made by the Robust I-GTWR method using the M-estimator is 94.62% with a mean absolute deviation value of 0.1491 and an Akaike Information Criterion value of 110.4899.

Harmonising Contributions: Exploring Diversity in Software Engineering through CQA Mining on Stack Overflow

The need for collective intelligence in technology means that online Q&A platforms, such as Stack Overflow and Reddit, have become invaluable in building the global knowledge ecosystem. Despite literature demonstrating a prevalence of inclusion and contribution disparities in online communities, studies investigating the underlying reasons behind such fluctuations remain scarce. The current study examines Stack Overflow users’ contribution profiles, both in isolation and relative to various diversity metrics, including GDP and access to electricity. This study also examines whether such profiles propagate to the city and state levels, supplemented by granular data such as per capita income and education, before validating quantitative findings using content analysis. We selected 143 countries and compared the profiles of their respective users to assess implicit diversity-related complications that impact how users contribute. Results show that countries with high GDP, prominent R&D presence, less wealth inequality, and sufficient access to infrastructure tend to have more users, regardless of their development status. Similarly, cities and states where technology is more prevalent (e.g., San Francisco and New York) have more users who tend to contribute more often. Qualitative analysis reveals distinct communication styles based on users’ locations. Urban users exhibited assertive, solution-oriented behaviour, actively sharing information. Conversely, rural users engaged through inquiries and discussions, incorporating personal anecdotes, gratitude, and conciliatory language. Findings from this study may benefit scholars and practitioners, allowing them to develop sustainable mechanisms to bridge the inclusion and diversity gaps.

Spatiotemporal Modeling of Carbon Fluxes over Complex Underlying Surfaces along the North Shore of Hangzhou Bay

Urban areas contribute to over 80% of carbon dioxide emissions, and considerable efforts are being undertaken to characterize spatiotemporal variations of CO2 (carbon dioxide) at a city, regional, and national level, aiming at providing pipelines for carbon mission reduction. The complex underlying surface composition of urban areas makes process-based and physiology-based models inadequate for simulating carbon flux in this context. In this study, long short-term memory (LSTM), support vector machine (SVM), random forest (RF), and artificial neural network (ANN) were employed to develop and investigate their viability in estimating carbon flux at the ecosystem level. All the data used in our study were derived from the long-term chronosequence observations collected from the flux towers within urban complex underlying surface, along with meteorological reanalysis datasets. To assess the generalization ability of these models, the following statistical metrics were utilized: coefficient of determination (R2), root mean square error (RMSE), and mean absolute error (MAE). Our analysis revealed that the RF model performed the best in simulating carbon flux over long time series, with the highest R2 values reaching up to 0.852, and exhibiting the smallest RMSE and MAE values at 0.293 μmol·m−2·s−1 and 0.157 μmol·m−2·s−1. As a result, the RF model was chosen for simulating carbon flux at spatial scale and assessing the impact of urban impervious surfaces in the simulation. The results showed that the RF model performs well in simulating carbon flux at the spatial scale. The input of impervious surface area index can improve the performance of the RF model in simulating carbon flux, with R2 values of 84.46% (with the impervious surface area index in) and 83.74% (without the impervious surface area index in). Furthermore, the carbon flux in Fengxian District, Shanghai, exhibited significant spatial heterogeneity: the CO2 flux in the western part of Fengxian District was less than in the eastern part, and the CO2 flux gradually increased from the west to the east. In addition, we creatively introduced the diurnal impervious surface area index based on the Kljun model, and clarified the influence of impervious surface on the spatiotemporal simulation of CO2 flux over the complex urban underlying surface. Based on these findings, we conclude that the RF models can be effectively applied for estimating carbon flux on the complex underlying urban surface. The results of our study reduce the uncertainty in modeling carbon cycling in terrestrial ecosystems, and make the variety of models for the carbon cycling of terrestrial ecosystems more diverse.

Temperature and domestic trade: City evidence from China

Climate change poses an enormous global challenge, particularly for human economic activities. Previous literature has paid limited attention to the impact of temperature change on domestic trade. To examine the nexus between temperature and domestic trade, we employ panel data from 285 Chinese cities from 2000 to 2021 and a spatial econometric model. First, we reveal significant spatial correlation between domestic trade between cities. More importantly, a city's domestic trade is found to be significantly influenced by temperature changes in neighbouring cities. Specifically, the significantly negative spillover effect of temperature changes on domestic trade is consistent with the significantly negative direct effect, suggesting an overall negative total effect. Temperature changes also have a cumulative impact on domestic trade. Labour productivity, population migration and meteorological disasters are the mechanisms through which temperature changes influence Chinese cities’ domestic trade. Furthermore, the negative impact of temperature changes is more pronounced in the retail sector and in cities with low air–conditioning penetration. We provide city–level evidence of the relationship between climate change and economic activities from a new perspective of domestic trade, highlight the influential channels and confirm the role of temperature change in impeding the prosperity of domestic trade.

How does the digital economy affect the sustainability of pensions? Evidence from China

ABSTRACT The relationship between an ageing population and pension sustainability is interrelated with the positive effect of the digital economy on both. However, there has been limited research conducted on the connection between the digital economy and pensions. In this study, we analyse the impact channels of the digital economy on pension sustainability using panel data at the city and provincial levels and an intermediary effect model. The analysis indicates that the digital economy can enhance the sustainability of pension funds through the mediating transmission mechanisms of economic growth, income, and employment effects. Furthermore, the development of a digital economy can effectively regulate the impact of ageing on the sustainability of pensions. We also discuss the relationship between digital development, population structure, and sustainable development of pension systems, along with policy advice.

Energy security governance and its adaptive capacity

PurposeThis study aims to introduce the Adaptive Capacity Wheel (ACW) Framework to assess the adaptive capacity of local institutions to address energy security issues. This study used two comparative cases, representing Indonesia's city level (Palembang) and the province level (South Sumatra Province).Design/methodology/approachData used in the analysis were from face-to-face interviews, site observation and document review. This study conducted a set of semi-structured interviews with the key interviewees.FindingsThe two government institutions (city and province) report the challenges in dealing with energy security. The results found that the stakeholders at the city level are less adaptive than those at the province level. This challenge is substantially visible regarding variety, resources, room for autonomous change and leadership. On the other hand, South Sumatra Province is quite adaptive in terms of variety, resources and leadership. This study offers a potential adoption of the ACW framework as an analytical instrument to review the adaptive capacity of local institutions in addressing energy security.Research limitations/implicationsTransforming data from the qualitative to the quantitative form can lead to a biased interpretation of data.Originality/valueThis study is the first attempt to explore the adaptive capacity of local institutions to address the energy security agenda through the lens of the ACW framework.

Research on the Spatial Structure of the Beijing–Tianjin–Hebei Urban Agglomeration Based on POI and Impervious Surface Coverage

Exploring urban spatial structures through spatial coupling analysis methods is an important method to provide theoretical support for the construction of sustainable urban structures. In order to make up for the neglect of POI species differences in previous studies, information entropy was introduced to calculate POI confusion, and a comprehensive POI index was constructed by combining kernel density and the entropy weight method; impervious surface coverage was extracted based on land cover data. The spatial distribution of the Beijing–Tianjin–Hebei urban agglomeration and some typical cities was analyzed by coupling two types of data using the dual-factor mapping method. The research indicates the following: (1). The spatial distribution of the two sets of data in the Beijing–Tianjin–Hebei region is highly consistent, indicating a state of high spatial coupling; Beijing has the highest proportion of coupling in the same region at the city level (73.39%). (2). The areas with different coupling of the two types of data are mainly distributed in the urban fringe areas transitioning from the city center to the suburbs, as well as in large-scale areas with single functionality such as airports, scenic spots, and ports. This study shows that analysis combining the POI comprehensive index and impervious surface coverage can effectively characterize urban spatial structure characteristics, providing a new perspective for the study of the spatial structure of the Beijing–Tianjin–Hebei urban agglomeration. It is of great significance for a deeper understanding of the laws of urban agglomeration spatial structures and guiding the coordinated development of the Beijing–Tianjin–Hebei urban agglomeration.

A Future Growth Model for Building More Housing and Infrastructure with Less Embodied Greenhouse Gas.

Global demand for housing and the climate crisis have created a seemingly impossible choice between the need to build more and the need to emit less from construction materials. Here, we present the future infrastructure growth (FIG) model, a generalizable method for finding pathways to build enough housing and infrastructure while reducing material emissions, in line with climate commitments. FIG uses open data to quantify the emissions of existing neighborhoods as if they were built new; it then uses these quantifications to forecast future cradle-to-gate embodied emissions from new residential buildings and linear infrastructure construction. This novel approach allows for detailed analysis that scales to a city, region, and/or national level and captures variability in construction norms, designs, and codes. We demonstrate FIG on Canada, using the model to find neighborhood-level drivers of embodied emissions and the most effective reduction strategies through 2030 and 2050. Current construction practices will cause a 437% overshoot of Canada's climate commitments if housing growth targets are met. Avoiding this overshoot requires a near-total reliance on multiunit buildings and best-in-class design supported by improvements in material manufacturing, building within existing urban boundaries, and halving the use of new materials.

From poverty to prosperity: assessing of sustainable poverty reduction effect of “welfare-to-work” in Chinese counties

The “welfare-to-work” program is a comprehensive supportive policy in the 14th five-year plan period in China. In this paper, a systematical assessment of the long-run effectiveness of the welfare-to-work policy on poverty reduction is of great significance to stimulate the internal impetus of people who are lifted out of poverty to achieve income growth and prosperity and promote regional economic development. Based on the data at the county (city) level in China from 2000 to 2019 and the sustainable development theory, in this paper, a county-relative poverty evaluation system was constructed. Besides, the double difference method was employed to evaluate the effect of the welfare-to-work policy on poverty reduction and test its action mechanism. The findings are as follows: (1) the welfare-to-work policy has a significant poverty reduction effect and presents an inverted “U” shape. In addition, significant achievements have been made in “maintaining employment stability, ensuring income, strengthening skills, and supporting the economy” ; (2) the welfare-to-work policy boosts poverty reduction in counties through infrastructure construction, fiscal intervention, and financial tools; however, the financial tools play a positive role in poverty reduction in the northwest region and suppressed role in the southwest region, and has an insignificant effect in the central region; and (3) there are differences in the effect of poverty alleviation policies of the counties with different sustainable development levels, and the regions with higher development level have a stronger driving effect.

Geospatial analysis for environmental noise mapping: A land use regression approach in a metropolitan city

Environmental noise can lead to adverse health outcomes. Understanding the spatial variability of environmental noise is crucial for mitigating potential health risks and developing influential urban strategies for reducing noise levels. This study aimed to measure noise levels and develop a land use regression (LUR) model to determine the spatial variability of environmental noise in Shiraz, Iran. A grid-based technique was used to establish 191 noise measurement sites (summer) across the city to generate the LUR model based on two noise metrics: Lden and Lnight. Leave-one-out cross-validation (LOOCV) and 38 additional measurement sites (winter) were used for the LUR model assessment. The mean values of Lden and Lnight during summer were 68.20 (±8.05) and 58.95 (±9.55), respectively, while during winter, the corresponding values were 69.46 (±5.46) and 58.81 (±6.79). The LUR models explained 67% and 65% of the spatial variability in Lden and Lnight, respectively. LOOCV analysis demonstrated R2 values of 0.64 and 0.61. Moreover, findings indicated mean absolute error (MAE) values of 3.96 dB(A) for Lden and 4.74 dB(A) for Lnight. Validation based on an additional set of 38 measurement sites revealed R2 values of 0.62 for both Lden and Lnight, with MAE of 2.78 and 3.31, respectively. In addition, the adjusted R2 values were 0.54 and 0.53. The results indicated no significant temporal variations between summer and winter. The results revealed that road-related variables significantly influenced noise levels. Moreover, the results indicated that Lden and Lnight levels were higher than the World Health Organization recommendations for exposure to road traffic noise. The results of our study showed that the LUR modeling approach based on geographical predictors is an effective tool for assessing changes in ambient noise levels in other cities in Iran and around the globe.

Organizational redesign for government of Jakarta administrative city

The Jakarta City Government organization as a service instrument in order to meet the needs of the organization is currently considered no longer in accordance with the demands of the growing and increasing needs of the community. The overlapping main tasks of several organizational units and the long span of control became the reason for conducting research so that an efficient organizational format of the Jakarta City Government was found in serving the needs of Jakarta citizens The research method used was qualitative descriptive with 16 informants taken by purposive sampling. Data was taken by interviews, observations, focus group discussions and documentation which was then analyzed using NVIVO software. The novelty of this research is the majority of previous research examining organizational redesign in business enterprises, although some studies have also discussed the redesign of government organizations but not specific to the problems of the City of Jakarta. From the results of research that has been carried out with reference to five organizational points including strategic apex, operating core, middle line, technostructure, support staff, it was found that several changes need to be made starting at the strategic apex stage, it is necessary to restructure through the abolition of positions at the City Level such as the position of Vice Mayor. The operating core stage found that there are still overlapping authorities, uneven workloads, lack of facilities, lack of human resources and the need for service acceleration. The middel line stage found that the lack of sub-district role was proposed to be removed and needed to strengthen the authority of the sub-district. The stage of staff support was found that the official tribe had inappropriate duties and functions so that it was more effective and efficient when it was under the coordination and responsibility of the mayor. Keywords: Redesign of Government Organization, Adaptation and Innovation, Sustainable City

Top-down or bottom-up? Government policies, public awareness and carbon reduction

Environmental governance approaches are critical for carbon reduction and energy security. Using the data from government work reports and public search, this paper investigates the impact of government policies and public awareness on carbon emissions. The ‘top-down’ administrative regulation is found to have more effective in promoting carbon reduction in cities than ‘bottom-up’ public awareness, and this carbon reduction effect is more pronounced in central, western and lower levels of economic development cities. The findings suggest that regions with higher economic development tend to exhibit greater dependency on carbon, posing challenges in achieving prompt reductions in carbon emissions through governmental policies and public awareness. Additional discoveries indicate that ‘top-down’ administrative regulation exhibits a certain sustainability, mainly through industrial structure adjustments. This aspect proves beneficial for enhancing the city's developmental path and quality in the long term.