Urban Trends Research Articles

Demographic ageing: an opportunity to rethink economy, society and regions

Abstract Ageing is often depicted negatively, but we challenge passive views, suggesting that an “active ageing” agenda requires rethinking economy, urban form and societal responses. We present demographic trends in ageing, migration and urbanization across global regions. Building from psychological theories of ageing regarding disengagement and continuity, we argue for an adjustment response at the societal level to address needed structural changes in economy, social policy and regional planning. These adjustments should prioritize well-being and purpose over economic growth; social policy that links household, informal and formal care and new approaches to urban and regional planning that remake cities for all ages. Ageing requires new paradigms. These societal adjustments will permit new approaches that could build a more inclusive world.

Perception and Attitude of Consumer Patronage of Ghana-Grown Rice: Evidence from Kumasi, Ghana

Increasing human population, urbanization, and changing consumer trends have contributed to the emergence of rice as the leading staple food in Ghana. Despite its importance in the Ghanaian diet, patronage and consumption of locally grown rice are not yet widespread. This study investigates consumer patronage of locally grown rice in the Kumasi Metropolis, Ghana, and fills a critical gap in understanding the specific factors that influence consumer preferences in urban settings. Data were collected through a cross-sectional survey of 120 respondents across different income categories using a multistage sampling technique and analyzed using descriptive statistics, a Probit model, and Kendall's coefficient of concordance. The study found that cooking quality, palatability, colour, and fragrance were the primary attributes that influenced consumer preferences. Probit analysis revealed that income and the frequency of rice consumption significantly influenced the likelihood of purchasing locally grown rice. Specifically, a higher income decreases, while frequent rice consumption increases the likelihood of choosing local rice. Key constraints identified include perceived high cost, limited availability, and concerns about the quality of rice. This study recommends that local rice producers, agricultural organizations, and government bodies should make efforts to improve the sensory and culinary qualities of locally grown rice through improved processing techniques, increased promotion, and campaigns to ensure the commodity is readily available to consumers. These strategies aim to boost local rice consumption and support agricultural sustainability and food security in Ghana.

The Impact of Urbanization on Rural Population Structure in China

Urbanization has been speeding up in China since reform and opening up policies were promoted, which promotes the progression of rural economy, but furthermore provides a certain degree of negative impact on the rural population structure. On the basis of summarizing the concept of urbanization, this study provides a thorough analysis of China's recent rural population structure and urbanization trend, and finally concludes that the quickening pace of urbanization in China makes the rural population structure face some problems and challenges. This study also offers some thoughts and recommendations in this area. At the same time, when analyzing the trend and population structure, by comparing and summarizing the secondary data and relevant literature, this paper concludes that China's urbanization has caused problems such as talent shortage in rural areas, rural aging and gender structure imbalance of the left-behind population. To solve these problems, China should establish a sound and perfect social security system and encourage the integration of rural and urban development. China ought to also establish a sound pension system and optimize the population structure; in rural areas, the promotion of respect for women has been strengthened and relevant laws have been introduced to gradually eliminate gender discrimination. It is beneficial to promote the national economy, cultural development.

Deciphering decadal urban ozone trends from historical records since 1980

Abstract Ozone pollution is a major environmental threat to human health. Timely assessment of ozone trends is crucial for informing environmental policy. Here we show that for the most recent decade (2013–2022) in the Northern Hemisphere, warm-season (April-September) mean daily 8-h average maximum (MDA8) ozone increases much faster in urban regions with top ozone levels (mainly in North China Plain, 1.2 ± 1.3 ppbv year−1) than that in other low-ozone regions (0.2 ± 0.9 ppbv year−1). These trends widen the ozone differences across urban regions, and increase extreme pollution levels and health threats from a global perspective. Comparison of historical trends in different urban regions reveals that, ozone increases in China during 2013–2022 differ in magnitude and mechanisms from historical periods in other regions since 1980. This reflects a unique chemical environment characterized by exceptionally high nitrogen oxides and aerosol concentrations, where reducing ozone precursor emissions leads to substantial ozone increase. Ozone increase in China has slowed down in 2018–2022 compared to 2013–2017, driven by ongoing emission reductions but with ozone-favorable weather conditions. Historical ozone evolution in Japan and South Korea indicates that ozone increases should be suppressed with continuous emission reduction. Increasing temperature and associated wildfires have also reversed ozone decreases in the US and Europe with anthropogenic ozone control slowing down in recent decades.

Predictive Understanding of Stream Salinization in a Developed Watershed Using Machine Learning.

Stream salinization is a global issue, yet few models can provide reliable salinity estimates for unmonitored locations at the time scales required for ecological exposure assessments. Machine learning approaches are presented that use spatially limited high-frequency monitoring and spatially distributed discrete samples to estimate the daily stream-specific conductance across a watershed. We compare the predictive performance of space- and time-unaware Random Forest models and space- and time-aware Recurrent Graph Convolution Neural Network models (KGE: 0.67 and 0.64, respectively) and use explainable artificial intelligence methods to interpret model predictions and understand salinization drivers. These models are applied to the Delaware River Basin, a developed watershed with diverse land uses that experiences anthropogenic salinization from winter deicer applications. These models capture seasonality for the winter first flush of deicers, and the streams with elevated predictions correspond well with indicators of deicer application. This result suggests that these models can be used to identify potential salinity-impaired streams for winter best management practices. Daily salinity predictions are driven primarily by land cover (urbanization) trends that may represent anthropogenic salinization processes and weather at time scales up to three months. Such modeling approaches are likely transferable to other watersheds and can be applied to further understand salinization risks and drivers.

Unveiling Kerala’s Distinct Urbanisation: A Comparative Analysis within India

Urbanisation is a pivotal demographic transformation that characterised the twentieth century, serving as a crucial indicator of modernisation intimately intertwined with economic growth and development. Significantly, in its journey as a developing nation, India is witnessing a rapid surge in urbanisation, a trend that surpassed expectations, as evidenced by the 2011 Census. The surge in India’s urban population is primarily attributed to a combination of net rural–urban reclassification and rural-to-urban migration. It is essential to recognise the distinctive nature of Kerala in this context, as the state diverges significantly from the patterns observed in other regions. The urbanisation unfolding in Kerala is not confined solely to designated towns and cities; instead, the entire state portrays a nuanced urban–rural continuum. In this study, an exploration is undertaken to dissect the trends and patterns of urbanisation in Kerala, delving into its unique characteristics and associated challenges based on data provided by the Census of India. Regression analysis is used to determine the factors that contribute to Kerala’s urbanisation, which shows that urbanisation process is influenced by a complex interplay of financial, economic and demographic factors. Over the decade, banking has become a crucial driver of urbanisation, highlighting its growing role in supporting urban growth and development. In contrast, the significance of investment in small-scale industries/micro, small and medium enterprises has diminished by 2011, suggesting shifts in the economic landscape and pointing to the rising importance of services and remittances as sources of urban expansion. Household dynamics, consistently significant in both years, emphasise the critical role of population changes and family structures in shaping urbanisation trends. Despite the continuous rise in urbanisation rates, there persists a noticeable sluggishness in development, especially regarding the provision of essential amenities. This aspect necessitates focused policy attention to address the challenges accompanying Kerala’s evolving urban landscape. JEL Codes: O18, R23, R12

A Modeling Framework of Atmospheric CO2 in the Mediterranean Marseille Coastal City Area, France

As atmospheric CO2 emissions and the trend of urbanization both increase, the ability to accurately assess the CO2 budget from urban environments becomes more important for effective CO2 mitigation efforts. This task can be difficult for complex areas such as the urban–coastal Mediterranean region near Marseille, France, which contains the second most populous city in France as well as a broad coastline and nearby mountainous terrain. In this study, we establish a CO2 modeling framework for this region for the first time using WRF-Chem and demonstrate its efficacy through comparisons against cavity-ringdown spectrometer measurements recorded at three sites: one 75 km north of the city in a forested area, one in the city center, and one at the urban/coastal border. A seasonal CO2 analysis compares Summertime 2016 and Wintertime 2017, to which Springtime 2017 is also added due to its noticeably larger vegetation uptake values compared to Summertime. We find that there is a large biogenic signal, even in and around Marseille itself, though this may be a consequence of having limited fine-scale information on vegetation parameterization in the region. We further find that simulations without the urban heat island module had total CO2 values 0.46 ppm closer to the measured enhancement value at the coastal Endoume site during the Summertime 2016 period than with the module turned on. This may indicate that the boundary layer on the coast is less sensitive to urban influences than it is to sea-breeze interactions, which is consistent with previous studies of the region. A back-trajectory analysis with the Lagrangian Particle Dispersion Model found 99.83% of emissions above 100 mol km−2 month−1 captured in Summer 2016 by the three measurement towers, providing evidence of the receptors’ ability to constrain the domain. Finally, a case study showcases the model’s ability to capture the rapid change in CO2 when transitioning between land-breeze and sea-breeze conditions as well as the recirculation of air from the industrial Fos region towards the Marseille metroplex. In total, the presented modeling framework should open the door to future CO2 investigations in the region, which can inform policymakers carrying out CO2 mitigation strategies.

Decoding Urban Intelligence: Clustering and Feature Importance in Smart Cities

The rapid urbanization trend underscores the need for effective management of city resources and services, making the concept of smart cities increasingly important. This study leverages the IMD Smart City Index (SCI) dataset to analyze and rank smart cities worldwide. Our research has a dual objective: first, we aim to apply a set of unsupervised learning models to cluster cities based on their smartness indices. Second, we aim to employ supervised learning models such as random forest, support vector machines (SVMs), and others to determine the importance of various features that contribute to a city’s smartness. Our findings reveal that while smart living was the most critical factor, with an importance of 0.259014. Smart mobility and smart environment also played significant roles, with the importance of 0.170147 and 0.163159, respectively, in determining a city’s smartness. While the clustering provides insights into the similarities and groupings among cities, the feature importance analysis elucidates the critical factors that drive these classifications. The integration of these two approaches aims to demonstrate that understanding the similarities between smart cities is of limited utility without a clear comprehension of the importance of the underlying features. This holistic approach provides a comprehensive understanding of what makes a city ’smart’ and offers a robust framework for policymakers to enhance urban living standards.

Surat’s sustainable future: analyzing compact urban development and population density trends

Rapid urbanization is a challenge for the cities of developing countries like India. The share of an urban area is rapidly increasing in dynamically growing cities. Due to this, the land use is altered, causing sprawl and unplanned development. The consumption of resources is greater, and the impact on the surroundings is negative. The compact city concept is always more efficient and cost effective compared to unplanned organic growth. The compact city optimizes available land resources with its maximum possible capacity in accordance with other available natural resources. The primary study objective is to assess the growth trend of Surat city using satellite images, geographic information system techniques, and census data for the last few years. The analysis considers population density for different city wards from 1991 to 2020. Based on the land use land cover change carried out using the maximum likelihood method from the satellite images, the rate of spreading out, its direction, and causes are also found. In this study, population density is a crucial attribute of the compact city concept. The study shows that wardwise density is increasing in a specific direction and making that portion of the city a dense, compact development. This is observed in ward numbers 48, 42, and 35 in the east, north, and southeast directions from Central Business District, respectively. The average population density in persons per sq. km is continuously increasing from 5006 to 20329 at city level which also shows the compact city development for Surat. The governing authorities must incorporate the compact city concept into the planning policy for sustainable urban development. By this, policymakers can understand housing needs to promote affordable housing, invest in infrastructure development and public services, and identify areas where sustainable transportation options, such as public transit, walking, or cycling, need improvement.

Expanding the associations between built environment characteristics and residential mobility in high-density neighborhood unit

Global trend of urbanization has led to frequent residential mobility and urban shrinkage issues. Planners and policy makers advocate for enhancing built environment quality of neighbourhood units to address these issues. Although the impact factors and degree of the influence of the built environment on residential mobility have been studied, the nonlinear effects at the neighbourhood level, and the relative importance when considered alongside other factors, remain unclear. In this study, the central area of Nanjing using mobile phone signalling data, the 5Ds framework, machine learning algorithms, and interpretable model Shapley Additive exPlanations (SHAP) are examined. The finding shows that (1) economy and neighbourhood ties are still key drivers of relocation. (2) Optimizing highly accessible road network for short-distance travel and developing low-density urban mode has become significant methods to attract relocators, interacting with other factors to influence residential mobility. (3) High-quality neighbourhood design, diverse amenities, and proximity to natural landscapes increase willingness to relocate, (4) while religion, socio-demographics, and large-scale transportation accessibility have minimal impact. The study offers four urban development recommendations to help municipal planners and policy makers create liveable cities and cohesive communities, providing essential insights for early or renewal stage urban planning stages.

Trends and social determinants of the obesity epidemic among reproductive-age women in ten Asian countries

The rapid epidemiological transition in Asian countries, resulting from the rising trend of urbanisation and lifestyle changes, is associated with an increasing risk of obesity in women of reproductive age. This is the first study to investigate the trends and population-attributable fraction (PAF) of obesity, and the interaction effects of education and wealth on obesity among reproductive-age women aged 15–49 years in ten Asian countries. This cross-sectional study examined the most recent (2000 to 2022) Demographic and Health Surveys (DHS) data from ten Central and Southeast Asian countries. Multilevel multinomial logistic regression models were used to compute odds ratios (ORs). PAFs adjusted for communality were calculated using adjusted ORs and prevalence estimates for each risk factor. This study included a weighted sample of 743,494 reproductive-age women. All the countries showed an increasing trend for obesity and a decreasing trend for underweight, except for the Maldives. The highest PAFs of obesity were associated with women who were married (PAF = 22.2%; 95% CI 22.1, 22.4), aged 35–49 years (PAF = 16.4%; 95% CI 15.5, 17.1), resided in wealthy households (PAF = 14.5%; 95% CI 14.4, 14.5), watched television regularly (PAF = 12.5%; 95% CI 12.1, 12.8), and lived in urban areas (PAF = 7.8%; 95% CI 7.7, 8.0). The combined PAF showed that these five risk factors were associated with 73.3% (95% CI 71.8, 74.9) of obesity among reproductive-age women. Interaction analysis between women’s education and household wealth revealed that having a secondary or higher level of education and residing in a wealthier household was associated with a lower risk of obesity (OR = 0.71, 95% CI 0.66, 0.76). The findings of this study suggest that, in order to address the rising rate of obesity among women in Asian countries, education and lifestyle modifications in urban areas should be a priority. Pakistan and the Maldives need to be a priority given the rapidly increasing trends in obesity and underweight subpopulations in their respective countries.

Geo-Sensing-Based Analysis of Urban Heat Island in the Metropolitan Area of Merida, Mexico

Urban Heat Islands are a major environmental and public health concern, causing temperature increase in urban areas. This study used satellite imagery and machine learning to analyze the spatial and temporal patterns of land surface temperature distribution in the Metropolitan Area of Merida (MAM), Mexico, from 2001 to 2021. The results show that land surface temperature has increased in the MAM over the study period, while the urban footprint has expanded. The study also found a high correlation (r> 0.8) between changes in land surface temperature and land cover classes (urbanization/deforestation). If the current urbanization trend continues, the difference between the land surface temperature of the MAM and its surroundings is expected to reach 3.12 °C ± 1.11 °C by the year 2030. Hence, the findings of this study suggest that the Urban Heat Island effect is a growing problem in the MAM and highlight the importance of satellite imagery and machine learning for monitoring and developing mitigation strategies.

Empirical Analysis on the Mechanism of Industrial Park Driving Urban Expansion: A Case Study of Xining City

Taking Xining City as an example, this article analyzes the mechanism by which industrial park construction drives the expansion of urban population size and built-up area, based on a review of the process of industrial park development and urban population growth. It also discusses future urban governance models in light of urban development trends. The research finds: (1) In the process of urban development, industrial park construction is often the initial factor in the cumulative and cyclical development of a city; (2) As the level of development improves and the mode of economic growth changes, the government should timely adjust its strategies, shifting from the expansion of industrial park construction towards structural optimization and quality improvement. The most significant difference from previous research is that this paper emphasizes the importance of government planning. This study can not only demonstrate the general process of industrial parks promoting urban expansion, but more importantly, it explains the fundamental reasons for the transition of urban expansion to adjustment from a mechanism perspective, thereby eliminating the drawbacks of simply predicting urban scale evolution through data models.

Study on the Coupled Coordinated Development of New Urbanization and Urban Resilience in Huaihe River Basin

The coupled and coordinated development of new urbanization and urban resilience can promote the disaster response, disaster resistance and high-quality development of urban agglomerations in the Huaihe River Basin. The article constructs a new urbanization system and a chain-type urban resilience evaluation system with the mechanism of "pressure-feedback", selects the central city of Huaihe River Basin as the research object, and measures the new urbanization and urban resilience level and change trend of the central city of Huaihe River Basin in 2012-2021 with the help of the coupling coordination model. Using the entropy value method, we measured the new urbanization and urban toughness level and the trend of change of the central cities in Huaihe River Basin from 2012 to 2021, measured the type of city cluster coupling coordination and its internal factors with the help of the coupling coordination model, and analyzed the coupling coordination relationship between the two.

Optimization of Embodied Energy and Construction Cost of Low-Income Housing in Urban India

India is the most populous country in the world, having a population of 1.42 billion in 2022. It is urbanizing rapidly, with the present urbanization level at about 35%, which is expected to reach about 40% by 2030. There was an estimated demand of 11.22 million homes in urban India in 2017, of which 95% was in the affordable housing sector. This demand is expected to increase with the current urbanization trends. The Indian government is promoting the construction of millions of affordable houses under its ambitious Prime Minister’s Housing Program. These houses are planned, designed, and constructed using local materials and techniques, considering local climatic, geological, hazard, and socio-economic conditions. We examined the 30 most commonly applied housing typologies to determine which typologies and materials have minimum embodied energy and construction costs. The results indicate that load-bearing housing construction of up to three stories, with a plinth–carpet area ratio of 1.31, constructed with any of the blocks-based masonry techniques, has the lowest embodied energy and construction cost, and houses with a plinth–carpet area ratio of 1.51 have the highest. Further, houses constructed with Hollow CC block masonry have the lowest embodied energy, and HF Fly Ash block-based masonry has the lowest construction cost.

Quantifying urban climate response to large-scale forcing modified by local boundary layer effects

Over the past two decades, the joint manifestation of global warming and rapid urbanization has significantly increased the occurrence of heatwaves and the formation of urban heat islands in temperate cities. Consequently, this synergy has amplified the frequency and duration of periods with tropical nights (TNs) in these urban areas. While the occurrences of such extreme events demonstrate irregular and nonlinear annual patterns, they consistently manifest a discernible rising decadal trend in local or regional climatic data. In urban regions situated amidst hilly or mountainous landscapes, changing wind directions—often associated with uphill or downhill thermal flows—profoundly impact the spread and dispersion of heat-related pollution, creating unique natural ventilation patterns. Using the Lausanne/Pully urban area in Switzerland as examples of hilly and lakeshore temperate cities, this study explores the influence of wind patterns and natural urban ventilation on the nonlinearity of recorded climatic data within an urban environment. This study integrates a mesoscale numerical weather prediction model (COSMO-1), a microscale Computational Fluid Dynamics (CFD) model, field observations, variational mode decomposition technique, and statistical analysis to investigate how wind speed and direction critically influence the nonlinearity of recorded long-term trends of extreme events, specifically focusing on the frequency and duration of TNs in lakeshore and hilly cities. The results strongly indicate a direct correlation between the frequency of TNs and the occurrence of specific moderate wind patterns. These wind patterns are exclusively captured by the microscale CFD model, unlike the mesoscale model, which neglects both urban morphology and complex hilly terrains. The impact of temporal and spatial variability of the wind field on long-term observations at fixed measurement stations suggests that caution should be exercised when relying on limited spatial measurement points to monitor and quantify long-term urban climate trends, particularly in cities located in complex terrains.

The Impact of the Expansion and Contraction of China Cities on Carbon Emissions, 2002–2021, Evidence from Integrated Nighttime Light Data and City Attributes

Exploring the impact of urbanization on carbon emissions is crucial for formulating effective emission reduction policies. Using nighttime light data and attribute data from 68 Chinese cities (2002–2021), this paper develops an urban development evaluation system with the entropy method. The Lasso method is employed to select key factors affecting carbon emissions, and hierarchical regression models are utilized to analyze these factors across different city types. The results show the following: (1) The extraction of built-up areas using integrated nighttime light data yields an overall accuracy ranging from 70.90% to 98.87%, reflecting high precision. (2) Expanding cities have predominated over the past two decades, indicating a continued upward trend in urbanization in China. (3) Urban development is influenced by internal characteristics and geographic location: contracting cities are mainly inland heavy industrial centers, while expanding cities are located in economically advanced coastal regions. Additionally, it is also impacted by the growth of surrounding cities, exemplified by the imbalance between central cities and their peripheries within metropolitan areas. (4) The expansion of built-up areas is a significant factor affecting carbon emissions across all city types. For expanding cities, managing population growth and promoting tertiary sector development are recommended, while contracting cities should focus on judicious economic planning and virescence area protection.

Analyzing urban footprints over four coastal cities of India and the association with rainfall and temperature using deep learning models

Capabilities of deep-learning models CNN and ConvLSTM are explored for analyzing and projecting future urban growth in four Indian coastal cities, viz., Mumbai, Chennai, Kochi, and Vishakhapatnam. ConvLSTM performed better with higher overall accuracy, Cohen's kappa, macro F1-score (> 95 %), and R2 and NSE values (> 0.87). The urbanization trends indicate a higher growth in Kochi (∼90.5 %), and greater projected rate for Mumbai (∼42.5 %). The monthly accumulated rainfall and mean temperature are analyzed and forecasted through the ConvLSTM model. The Mann-Kendall test-based analysis (with p-value <0.05), suggest no significant rainfall pattern for the cities except Mumbai, which exhibits an increasing trend. The projected rainfall trend is unaltered except for Vishakhapatnam, which is expected to increase in the coming years. The mean temperature over all the cities, shows an increasing trend (slope ∼ 0.02). However, in the coming years, the ‘increasing trend’ is expected to change into a ‘no significant trend’ for Mumbai and Vishakhapatnam. The forecasted results indicate a continuous urban expansion, which is expected to have a significant impact on rainfall and temperature trends. The outcome could assist urban planners in defining the timeline for improving drainage and include green spaces in the city development plans.

Monitoring Changes with Spatiotemporal Resolution in Land Surface Temperature (LST) Impact on Land Cover Around Al-Dora Refinery in Downtown Baghdad, Iraq

This study analyses the differences in temperature of the land surface across various land cover types and uses geospatial approaches to explore the changes that are taking place in the Dora refinery and its surrounding areas. Landsat 8 OLI/TIRS made use of Landsat imagery during the months of April and August in the years 2013 and 2023. ArcGIS 10.8 software was used to process the satellite images. The brightness temperature data from a satellite sensor was used to calculate the land surface temperature and the study's land cover classifications were created by applying supervised classification techniques.The aim of the study is to monitor the changes occurring in Dora refinery and its surrounding areas for periods of time 2013-2023 as a result of the influence of urban surface temperature , the growth and expansion that occurring in human activities and represented in the filtering and production of fossil energy and crude oil as well. The results of the investigation indicate an increase in the land surface temperature (LST) during the year 2023, in contrast to the year 2013, while we notice an increase in( NDVI ) in 2013 and a decrease in 2023, and a decrease in (NDBI) in 2013 and an increase in 2023. An inverse relationship was discovered between vegetated area and ( LST) between 2013 and 2023, whereas a positive relationship between (NDBI) and (LST) has been obtained suggesting an expanding surface urban heat island effect, as indicated by the correlation coefficients of statistical significance. It significantly affects how the land surface temperature varies. The main differences in the ways that land cover and use impact (LST) were also emphasized by this study during the whole duration of the inquiry, thus, methods for monitoring, examining, and assessing urban growth trends and their effects on land surface temperature can be achieved through the use of remote sensing and geographic information system tools.

Direct and indirect effects of urbanization on vegetation: A survey of Yunnan central urban Economic Circle, China

Urbanization affects vegetation distribution by changing land cover. However, it also significantly changes urban water and heat conditions, affecting vegetation growth and development. Vegetation coverage is an effective indicator of vegetation growth. This study analyzed trends in vegetation coverage over the past 35 years, taking the Yunnan Central Urban Economic Circle as the study area. The study was based on applying the pixel dichotomy model and correlation statistical analysis on joint Landsat 5, 7, and 8 long- term remote sensing data, meteorological, and land use data. The direct and indirect effects of urbanization on vegetation coverage were also further explored by constructing an urbanization impact framework. The results revealed that: (1) The urban area during urbanization from 1986 to 2021 increased by 720.29 km2. There was a continuous decline in vegetation cover in and around urban areas, which intensified with accelerating urbanization, with the effect being more pronounced in suburban areas. (2) There were consistent increasing trends in urbanization’s direct and indirect effects on vegetation over the last 35 years, with average negative and positive effects of − 0.41 and 1.59, respectively. (3) Direct effects could mainly be attributed to the expansion of impervious surfaces, whereas the main indirect effect during the late urbanization period (2011–2020) was increasing average temperature. The average temperature showed a correlation coefficient with urbanization of 0.7767, and this relationship showed seasonal heterogeneity due to the significant growth of urban vegetation in summer and winter. (4) Cities that developed faster showed better environmental planning and construction. The direct and indirect effects of urbanization on vegetation during the early and middle stages were higher in cities developing at slow and moderate rates, with this trend reversing only in the later stages of urbanization. The results of this study can increase understanding of the effect of urbanization on vegetation coverage in the Yunnan Central Urban Economic Circle. They can assist in improving urban green spaces and urban ecological resilience.