Multiple environmental inequalities between Global South and Global North in over 10,000 urban centers

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Three environmental indicators: greenness, air-pollution (PM 2.5 ), CO 2 emissions are used, representing three differently environmental roles to society and human health (contribution (benefit), victmisation (harm) and destruction (cost)). The relative status and change of these three indicators from 2000 to 2015 are assessed. Our findings indicate that the CO₂ emissions in Global North is more than twice those in the Global South, whereas the mean PM₂.₅ concentration is less than half, reflecting significantly higher environmental destruction (indicated by CO₂ emissions) but lower environmental victimization (indicated by PM₂.₅). Global South and Global North exhibit similar trends in greenness but have different causes with a luxury effect in Global North. The socio-economy plays a dominant role in environmental development in Global North while both socio-economic and natural endowments in Global South. Earth and environmental sciences/Environmental sciences/Environmental impact Earth and environmental sciences/Environmental social sciences/Sustainability Scientific community and society/Geography Urban centers Global South and North Heterogeneity Inequalities Climate and environment Piecewise SEM Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Highlights CO 2 emission of Global North is more than twice higher than Global South but mean PM 2.5 is more than twice lower, reflecting significantly higher environmental destruction but lower environmental victimization Global South and Global North exhibit similar trends in greenness but have different causes with a luxury effect in Global North. Global South has a much larger diversity in all the environmental indicators than those in Global North. CO₂ levels mainly exhibited deterioration in both Global North and Global South, with all three environmental improvements 13.08% in Global North but only 5.10% in Global South and all three environmental degradation 5.21% in Global North but 16.53% in Global South from 2000 to 2015 The biggest environmental inequality is CO 2 emission (environmental destructions), the secondary is PM 2.5 (environmental victimisation) andthe minimal is greenness (environmental contributions) between Global South and Global North The socio-economy plays a dominant role in environmental development in Global North while both socio-economic and natural endowments in Global South 1 Introduction Urban centers occupy only 2–3% of the global land surface but generate approximately 80% of global GDP and 70% of carbon emissions (Edenhofer et al., 2014; Fu et al., 2023 ). Urban development has dominated the global process of land use transformation with economic development and societal prosperity (Keeler et al., 2019 ; Meng et al., 2018 ). It has aggravated environmental degradation such as deforestation, habitat loss, air pollution, rising temperatures, and increased CO 2 emissions (Strokal et al., 2021 ; Wiebe & Wilcove, 2025 ). Moreover, the extent of environmental deterioration varies across cities, exacerbating global environmental inequality among urban centers. Therefore, integrating environmental sustainability into urban development is essential for achieving long-term sustainable growth. Global urban development exhibits a range of well-documented phenomena, including the luxury effect in urban greenness (Li et al., 2024 ), the urban heat island effect (Wu et al., 2024 ), the Environmental Kuznets Curve (EKC) (Sulemana et al., 2017 ), the decoupling effect of PM 2.5 concentrations from urban development (Li et al., 2025 ), energy poverty (Chan & Delina, 2023 ), and carbon leakage (Cao et al., 2025 ). While these phenomena provide critical insights into the interactions between environmental conditions and socio-economic processes for a certain environmental issue, they fail to capture the underlying mechanisms for multiple interactive environmental issues that often intertwine in many urban centers. Urban growth creates significantly environmental inequalities. Built on the definition of social inequality, the environmental inequality refers to the uneven distribution of benefits and harms, rewards and costs with respect to the environment among social units such as individuals, groups, regions, and states. Recent literature on urban environmental inequalities has mainly emphasized positive contributions of natural environments such as greenspaces to health and well-being (Fanning et al., 2022 ; Green & Healy, 2022 ). Significant knowledge gaps remain: 1) most existing research tends to focus on a single environmental factor (urban greenspace), only reflecting benefits of the environmental change to the society and human health. However, urban environmental change are multi-dimensional, such as air pollution and increased CO 2 emissions which can reflect environmental inequality from harms and costs. 2) a large proportion of existing research focuses on regional scales—often based on selected urban centers—thereby covering only a narrow range of global climatic and sociodemographic conditions, which may restrict the generalizability of findings to other regions (Shao et al., 2022 ). 3) limited studies have investigated the net environmental change across a global sample of urban centres for a comparable time failing to explain the net outcomes of the destructive and constructive processes of urban development; 4) the geographical and socio-economic drivers of changes in environmental inequality remain unclear, limiting our capacity of promoting environmental equality in future. Urban centers in Global North and Global South are widely heterogeneous (Arvidsson et al., 2023 ; Balland et al., 2020 ; Bettencourt et al., 2007 ). By 2022, GDP per capita in Global North was 12 times that of Global South, double the disparity observed in 1950 (Freeman, 2024 ). Urban built infrastructure per capita is also significantly disproportionate, with some Global North countries surpassing those in Global South by more than 30-fold (Zhou et al., 2022 ). Limited economic development in Global South has constrained environmental management and protection efforts. As a result, urban centers in Global South possess only 70% of the cooling capacity of those in Global North (Li et al., 2024 ). High-risk areas for PM 2.5 concentrations are pervasive in Global South, including countries such as India and China (Lim et al., 2020 ). Although most of these challenges and opportunities are concentrated in Global South, yet research remains largely focused on global megacities or urban centers in developed countries in Global North (Balland et al., 2020 ; Nagendra et al., 2018 ). Our study aims to assess multiple environmental inequalities between Global South and Global North in over 10,000 urban centers. Three environmental indicators: urban greenness, air pollution (PM 2.5 ), CO 2 emissions, representing three differently environmental roles to society and human health (positive contribution (benefit), victmisation (harm) and destruction (cost)) are used. The relative status of these three indicators are assessed with Ternary Diagram to partition their different roles and the temporal changes of these three indicators between 2000 and 2015 are assessed with a three-dimensional (eight-quadrants) diagram. Finally, structural equation modelling is developed to capture global gradients in key geographical and socio-economic drivers of the different environmental development patterns of urban centers between Global North and Global South. The findings from this study are expected to inform future planning and management that shape urban sustainability trajectories. 2 Results 2.1 Difference in environmental development and its geographical, social, economic drivers between Global North and Global South Environmental indicators of the urban centers present big differences between Global North and Global South, illustrated in Fig. 1. The global map of carbon emissions shows lower emissions in Global South (especially in Africa, India and South-East Asia) while much higher in North America, Europe and East Asia. On average, carbon emissions per capita are 1.73 tons in Global North, significantly higher than the 0.55 tons in the Global South. The global greenness map reveals that Europe, India, Southeast Asia, Eastern North America and Central Africa are greener. Global North has a relatively higher mean greenness score (0.45) than Global South (0.40). Conversely, the air quality indicator shows higher PM 2.5 levels in Asia and Africa. Global North has a lower mean PM 2.5 concentration of 14.50 than Global South (38.77). In a word, Global North has twice higher mean CO 2 , slightly higher greenness and twice lower mean PM 2.5 than Global South while some urban centers in Global South present the case opposite to the mean values. The geographical, social, and economic drivers are detailed in the Supplementary Information. Global North shows significantly lower average values of elevation, temperature, and precipitation compared to Global South. In contrast, Global North demonstrates notably higher socio-economic levels, while Global South exhibits greater variability in these indicators. 2.2 Difference in relative status of the three environmental indicators between Global South and Global North In Global North, the mean values are 2.34 t a⁻¹ for CO₂ per capita, 1.40×10 -4 μg m⁻³ for PM 2.5 per capita, and 0.44 for greenness. After normalization, the percentage shares are 0.53 for CO₂, 0.05 for PM 2.5 , and 0.42 for greenness. As the consequence of the change in shares and location of three dividing lines, the "high-contributing" zone contains the most urban centers (24%), the "high-destruction" and "victimization-contribution trade-off" zones each account for 20%, all of them are higher than when thee area is equally shared. Contrarily, the "high-victimization" zone has the least (10%) and is concentrated in the lower part of these zones (Fig. 2a). The spatial map of the triangular contribution of the environment (Fig. 2c) shows that eastern North America and northern Europe in Global North are “high contribution areas”. Western North America, Japan and Australia are “high destruction areas”. Spatially, urban centers in Global North are highly concentrated, with the area within the 10th contour covering only 0.026 of the map and the highest densities surpassing the 80th contour, reflecting a high degree of spatial consistency (Fig. 2a). In Global South, the mean values are 0.53 t a⁻¹ for CO₂ per capita, 3.70×10 -4 μg m⁻³ for PM 2.5 per capita, and 0.40 for greenness. After normalization, the shares are 0.44 for CO₂, 0.15 for PM 2.5 , and 0.41 for greenness. In terms of zonal distribution (Fig. 2b), the "high-destructive" and "high-contributing" zones contain the largest share of urban centers (22% and 21%, respectively). The "destructive-contributing trade-off" zone has the smallest share (13%), with other zones averaging around 15%. In Global South, central Africa, South-East Asia and India are in the “high contribution area”. The “high destruction area” is found in East Asia, West Asia, South America and northern Mexico (Fig. 2d). Unlike Global North, urban centers in Global South are more dispersed, with the area within the 10th contour covering 0.49 of the map, and the highest density surpassing the 20th contour, indicating significant spatial heterogeneity. Both Global North and Global South exhibit a substantially high share of CO₂, indicating the importance of CO₂ in environmental development in both regions. Both regions have very similar shares of “high contribution area” (over 20%) . They have also similar trends in a relatively low share of PM 2.5 , but magnitude of largely differs: 0.05 in Global North and 0.15 in Global South, with a difference of 10%. Spatial patterns also differ. While Global North has a concentrated urban distribution, Global South is far more dispersed, with the 10th-contour area being 18.53 times larger than that of Global North. 2.3 Difference in environmental development from 2000 to 2015 between Global North and Global South In Global North, Quadrant I accounts for 13.08% of the regions, which comprises urban centers with comprehensive environmental improvements and GDP levels ranging from $10,000 to $20,000. It is distributed in North America, Japan and parts of Europe (Fig. 3i; and Table 1), indicating that these urban centers achieving all-dimension improvements tend to have high economic levels. Quadrant II, accounting for 12.2% of the regions, features urban centers with GDP levels ranging from $5,000 to $15,000 which have made progress in air quality and CO₂ reduction but show limited advancements in greening and they are located in southern North America and Australia. Quadrant V, which includes 35.58% of the region. As the largest quadrant, it reflects the dominance of urban centers that have improved greening and air quality, despite challenges in reducing CO₂ emissions. It also has the broadest GDP distribution, peaking in the medium-to-high range of $5,000 to $25,000. These urban centers are spatially distributed in eastern North America and Western Europe. Quadrant VII representing 19.34% of the regions with greening improvements but deteriorations in air quality and CO₂ emissions. They include urban centers with lower GDP levels ($5,000 to $10,000) are primarily distributed in Eastern Europe and Japan. Table 1 Distribution of Global North and Global South in the eight quadrants Scenarios Contents of each scenario Percentage North (%) Percentage South (%) I Improved CO₂ emissions, PM₂.₅ concentration, and greenness (Reduced destruction and victimization, increased contribution) 13.08 5.10 II Improved CO₂ emissions and PM₂.₅ concentration but worse greenness (Reduced destruction, victimization and reduced contribution) 12.21 6.93 III Improved CO₂ emissions and greenness but worse PM₂.₅ concentration (Reduced destruction, but increased contribution and victimization) 5.39 1.19 IV Improved CO₂ emissions but worse PM₂.₅ concentration and greenness (Reduced destruction, increased victimization and reduced contribution) 2.54 1.87 V Worse CO₂ emissions but improved PM₂.₅ concentration and greenness (Increased destruction but decreased victimization and increased contribution) 35.59 25.04 VI Worse CO₂ emissions and greenness but improved PM₂.₅ concentration (Increased destruction but reduced contribution and victimization) 6.63 26.23 Ⅶ Worse CO₂ emissions and PM₂.₅ concentration but improved greenness (Increased destruction, victimization and contribution) 19.34 17.10 Ⅷ Worse CO₂ emissions, PM₂.₅ concentration and greenness (Increased destruction and victimization, reduced contribution) 5.21 16.53 Unlike Global North, most Global South urban centers are represented in reddish-orange tones, indicating lower GDP per capita levels. Quadrant I in Fig. 3e-h and Table 1, comprising only 5.10% of urban centers which is 13.08% in Global North. They are spatially distributed in Africa with limited GDP per capita, concentrated below $2,000. Quadrant V (improved greening and air quality but increased CO₂ emissions ) accounts for 25.04% which is 35.58% in the Global North. They have a GDP per capita peak of approximately $500 and are distributed mainly in Mexico, Africa and Asia. Quadrant VII with greening improvements but deteriorations in air quality and CO₂ emissions includes 17.10%, similarly in Global North (19.34%), with a GDP distribution similar to Quadrant V but skewed toward higher income levels. They are spatially distributed mainly in Asia. Quadrant VI with improved air quality while struggling with deteriorations in greening and CO₂ emissions, representing 26.23% which is only 6.63% in Global North, features GDP levels below $1,500 are spatially distributed in South America and Asia. Quadrant VIII accounts for 16.53% of the urban centers in Global South which is only 5.1% in Global North, with the highest population density and GDP per capita concentrated around $1,000. They are spatially distributed in West Africa, West Asia and East Asia. 2.4 Difference in driving mechanism of environmental development between Global North and Global South In Global North, CO₂ emissions are primarily driven by economic, demographic, and geographic factors, with an explanatory power of 69% (Fig. 4). GDP and urban area have the strongest positive influence, followed by population. Geography plays a dual role, directly suppressing emissions while indirectly increasing them through GDP growth or land constraints. PM₂.₅ has a lower explanatory power (29%), with population and income levels contributing positively, while carbon emissions mitigate PM₂.₅. Population affects PM₂.₅ both directly and indirectly through carbon emissions. Greenness, with an explanatory power of 14%, is negatively influenced by income and urban prosperity (-0.273 and -0.080), creating conflicting effects. In Global South, carbon emission patterns are similar but with different factor influences, yielding an explanatory power of 62%. Population has a strong direct effect (0.414) and an indirect impact via GDP and built-up land. Geography restricts construction land but promotes income growth, which drives emissions. PM₂.₅ has a weak explanatory power (5%), with income groups and greenness exerting negative effects (-0.122 and -0.023), while carbon emissions have a minor positive effect (0.047). Indirectly, income groups suppress greenness, reducing PM₂.₅, but also promote carbon emissions, increasing PM₂.₅. Greenness, with an explanatory power of 27%, is negatively affected by geography, population, prosperity, and income, with prosperity having the strongest dampening effect (-0.345). In a word, in Global North, population drives GDP, and GDP influences carbon emissions, but in Global South, population directly drives carbon emissions. Both regions share the role of built-up land area in driving carbon emissions. Another path shared by both regions is the dampening effect of prosperity on greenness, which is less in Global North than in Global South. In addition, the geography in Global South also plays a role in greenness. Population in Global North influences PM 2.5 , but greenness in Global South influences PM 2.5 . These different driving patterns under different explanatory powers highlight the complex pathways shaping the three environmental inqualities between Global North and Global Sourth. However, in general, the economy of the Global North plays a dominant role while Global South is dependent on both socio-economic and natural endowments. 3 Discussion There is a clear and statistically significant difference in the three environmental indicators and almost all of its geographic, social, and economic indicators of urban centers in Global South and Global North. This finding aligns with Zhou et al. ( 2022 ) on GDP, Nagendra et al. ( 2018 ) on urban prosperity and Chen et al. ( 2024 ) on more diversity in Global South. We found that the biggest environmental inequality is CO 2 emission (environmental destructions) between Global North and Global South although both are in the direction of deterioration. Average CO 2 emission of Global North is more than twice higher than Global South. In terms of temporal changes, there is an increase in carbon emissions in both Global North and Global South. In line with Cheng et al. ( 2022 ),, this is primarily due to the persistent increase in greenhouse gas emissions from fossil fuel combustion and land use changes since the 19th century. We also find a strong correlation between CO 2 and socio-economic indicators, with the explanation of more than 60% in both Global North and Global South, aligning with Zheng et al. ( 2020 ) and Betts-Davies et al. ( 2024 ). Rising CO₂ emissions are also partly driven by population expansion, particularly in low-income nations (Dong et al., 2020 ). The contribution of built-up land area to CO 2 is confirmed as the third global greenhouse gases emissions source (Lamb et al., 2021 ). Finally, our results also find that Global North has a 27.88% share of urban centers with improved CO 2 , which is better than the 16.12% in the Global South. This disparity may be associated with regional differences in energy transitions (Lamb et al., 2021 ). The secondary environmental inequality between Global North and Global South is PM 2.5 (environmental victimisation). PM 2.5 in Global North is more than twice lower than Global South. The spatial distribution of regions with high PM 2.5 concentrations is in Asia and Africa with the more share of urban centers in high- victimisation areas in Global South (14%) than Global North (10%). Historical changes see 72.66% of Global North urban centers improved PM 2.5 , while only 62.63% in Global South. The geographical, social economic factors have very low explanation of PM 2.5 in Global North (29%) and Global South (5%), indicating the causative complexity of PM 2.5 , agreed with Li et al. ( 2019 ), Yang et al. ( 2018a ) and Behrer and Heft-Neal ( 2024 ). Behrer and Heft-Neal ( 2024 ) found there was a “decoupling” of PM 2.5 in developed countries: as urban centers continue to expand, PM 2.5 concentrations decline. Moreover, the occurrence and transport of PM2.5 are influenced by a complex interplay of interrelated factors acting across multiple spatial scales, which complicates the identification of their individual effects (Lim et al., 2020 ; Zhang et al., 2017 ). The minimal environmental inequality is greenness (environmental contributions). Greenness is above 0.40 in both Global North and Global South. However, from 2000 to 2015, greenness has been improved in 74% of urban centers in Gobal North and only 48% in Global South. Although the geographical, social and economic factors have low explanation of greenness in Global North (14%) and Global South (27%), the common recognitions include heterogeneity of geography leads to differences in greenness between Global North and Global South (Haaland & van den Bosch, 2015 ). Then, there is a well-documented positive correlation between green space availability and wealth, known as the "luxury effect" (Li et al., 2024 ; Yin et al., 2023 ). Finally, the destruction and construction of greenspace can also occur periodically and are not always strictly chronological (Zhang et al., 2025 ). Therefore, there are different causes for the similar greenness in Global North and Global South. In addition, our study finds that greenness has a minimal impact on carbon emissions in both Global North and South. In Global North, CO₂ suppresses PM₂.₅, whereas in Global South, CO₂ increases PM₂.₅. Additionally, greenness mitigates PM₂.₅ in Global South. These findings are not a surprise from the existing studies with different results (Anenberg et al., 2019 ; Yang et al., 2018b ). Therefore, although greenspace often used a proxy for evaluating urban environmental sustainability, based on our findings, urban greening strategies are not a panacea for addressing all environmental inequalities. Further research to improving an understanding of the interactions among the three environmental dimensions will assist in more simple and cost-effective policies for reducing gaps of environmental sustainable development between Global North and Global South. The inequalities of the three environmental indicators between Global North and Global South may also arise from the unequal exchange between them. Global economic integration has led to a situation where resource and labor flows from the Global South—amplified by trade price differentials—substantially support the economic growth of the Global North (Amin, 1978 ; Hickel et al., 2022 ). In the meanwhile, some backward enterprises have difficulties in surviving in developed regions (countries) and move industries with high carbon emissions and other pollutants to regions (countries) that need to develop their economies, thus leading to developing regions in Global South becoming pollution havens (Akizu-Gardoki et al., 2021 ; Bai et al., 2023 ; Yang et al., 2024 ). Meng et al. ( 2018 ) confirmed that unequal exchange is a significant driver of global inequality of environmental development, even ecological breakdown. This partly explains our finding that CO 2 in Global North is strongly bound to economic development but to population size in Global South and more urban centers in Global South are environmental victims. Built upon the Red Ring-Green Ring model proposed by Cumming et al. ( 2014 ) and Cumming and von Cramon-Taubadel ( 2018 ), by combining the results of structural equation modeling with this environmentally unequal trade between Global North and Global South, we develop a more whole-of-system development patterns of Global North and Global South in which the economy of Global North plays a dominant role while Global South is more dependent on both socio-economic and natural endowments (Fig. 5 ). To achieve sustainable development of urban centers around the world, it is necessary to pay attention to the development dilemma of Global South and to break the unequal exchange pattern between Global North and Global South. Finally, our study reveals a considerable degree of heterogeneity within Global South, encompassing urban centers that have already begun to resemble Global North. Consequently, generalizations within the Global South are not perfectly-suited. The scale and intensity of urbanization, environmental pressures, and their social and economic effects underscore the Global South’s critical role in shaping the trajectory of international sustainability. Future research could potentially offer a more nuanced typification of Global South and incorporate a wider range of geographic and socio-economic indicators and temporal data. 4 Methods 4.1 Data source The Global Human Settlement Urban Centre Database (GHS-UCDB) was used in this study, covering over 10,000 urban centres in various areas across the globe (Melchiorri et al., 2024 ). This database includes the data on geography, socio-economics, environment, disaster risk reduction, and sustainable development, as well as the location and extent of each urban centre, covering 1990, 2000, and 2015. It is generated by integrating rich geospatial data provided under open data policies and new Earth observation programs, such as the Copernicus program. The Global Human Settlement Layer (GHSL) project delineates urban centers using the "Degree of Urbanisation" method based on population density and built-up area thresholds. Utilizing Geographic Information System (GIS) technologies, multi-thematic and multi-temporal variables are linked to these urban centers through methods like zonal statistics and spatial analysis, with quality ensured via high-resolution imagery verification. Therefore, this database offers researchers a robust foundation for examining complex urban phenomena at a regional and global scale. Global South includes urban centers in South America, Africa and parts of Asia, while Global North includes urban centers in North America, Europe, Australia and developed Asia (Simone, 2020 ). Among the 11,683 urban centres in the database, there are 10,070 urban centers in Global South, accounting for 86.2% of the urban centres, while 1,613 urban centers in Global North, account for 13.8% of the global urban centers. For instance, in Global South, urban centers such as São Paulo and Rio de Janeiro in South America, Lagos and Nairobi in Africa, and Mumbai and Jakarta in Asia are representative examples. In Global North, typical urban centers include New York and Toronto in North America, London and Paris in Europe, Sydney and Melbourne in Australia, as well as Tokyo and Seoul in developed Asia. 4.2 Characterising urban environments as a complex social-ecological system With the rapid urbanization in the past decades, the interconnectedness and interdependency of the urban environment with other various components of urban centres have been raised to a level that has never happened before at which the urban environment could not be examined as a single system any longer. We considered urban environments as a complex socio-ecological system, in which urban environmental development is dependent upon with its geographical features (natural endowments) and social and economic drivers. We included three indicators for characterizing environmental development: carbon dioxide (CO₂), PM 2.5 and Greenness. CO₂ emission mainly indicates the degree of greenhouse gases changes which is often regarded as a symbol of the adverse effects of human activities on the Earth climate system, leading to a series of environmental crises. Thus, it plays a “destruction (cost)” role in urban environmental sustainability. PM 2.5 serves as a key indicator of air quality, reflecting the deterioration of air environmental quality by pollution and doing harm to human health. It plays a “victmisation (harm)” role in urban environmental sustainability. Greenness, represented by the average greenness within built-up areas facilitating the comparison between urban centers, is typically associated with the ecosystem services provided by urban green spaces. An elevated level of greenness reflects a positive contribution to ecosystems and human health. Thus greenness plays a “contribution (benefit)” role in urban environmental sustainability. These three environmental indicators reflect the environmental inequality of urban centers from differences of urban centers in increase in environmental cost, exposure to environmental harms, and access to benefits. Geographical indicators included in this paper are temperature, precipitation and elevation, which primarily represent natural endowments and express the geographic and climatic context of urban centers. Social indicators include per capita built-up land area and prosperity measured by nighttime light intensity, which mainly reflect urban spatial organization and the intensity of human activities. Economic indicators include per capita GDP and income group classifications, which primarily indicate the level of economic development and wealth distribution. 4.3 Analysis of the differences in the environmental development of the urban centres between Global North and Global South First, we analysed the distributional difference of the urban centres between the Global North and Global South on the geographic, social, economic and environmental indicators to provide the basis for further analysis. The Mann-Whitney U test, a non-parametric method for non-normally distributed data is empoyed. The method calculates statistics through ranked comparisons and assesses the significance of the difference in terms of p-value. Its advantage is that it requires fewer assumptions about the distribution of the data and can effectively deal with outliers and asymmetric distribution. Then, we analysed the difference in relative status of the three environmental indicators of the urban centres to identify the most degraded environmental development and its difference between Global North and Global South. To differentiate the relative status of the urban centers between Global North and Global South, we employed Ternary Diagram to partition the different roles of the three environmental indicators with their relative proportion (Fig. 6 ). Three vertices of the triangle are carbon dioxide, PM 2.5 , and greenness. The coordinate axes increase in a counter clockwise direction. A dividing line is drawn at 1/3 of each indicator, dividing the triangle into six parts. According to the difference in the values of the indicators, the six sections are formed as follows: “high destruction area”, “high victimization area”, “high contribution area”, “destruction and victimization trade-off area”, “contribution and victimization trade-off area”, “destruction and contribution trade-off area”. The location and size of these six areas can be used to assess the relative environmental status of each urban center and compare them between Global North and Global South. In order to eliminate dimensional differences to ensure data comparability, CO₂ emissions, PM₂.₅ concentration, and greenness were normalized. Subsequently, the three indicators were then aggregated to calculate the proportion of CO₂ emissions, PM₂.₅ concentration, and greenness for each urban center, thereby deriving the relative proportions of these indicators. Finally, these relative proportions of the urban centers were mapped onto a ternary plot to visually present the balance among CO₂ emissions, air quality, and greening levels and six areas. Following that, we analysed the temporal changes of the three environmental indicators of the urban centers between Global North and Global South to identify if they are improved/degraded. The temporal changes were analysed between 2000 and 2015, with 2000 when environmental governance started globally and 2015 when the most updated data were available. The findings can assist in identifying the urban centre where the most urgent actions should be taken, and, if linked to the policies implemented, identifying the effectiveness of the environmental policies. The environmental improvements are calculated as follows: For CO₂ emissions and PM₂.₅ concentration: $$\:\begin{array}{c}\varDelta\:X=\frac{{X}_{t1}-{X}_{t2}}{{X}_{t1}}\#\left(3\right)\end{array}$$ For greenness: $$\:\begin{array}{c}\varDelta\:X=\frac{{X}_{t2}-{X}_{t1}}{{X}_{t1}}\#\left(4\right)\end{array}$$ Where: \(\:\varDelta\:X\) represents the percentage improvement of the environmental indicator, \(\:{X}_{t1}\) is the value of the indicator in the year 2000, \(\:{X}_{t2}\) is the value of the indicator in the year 2015. We used a three-dimension (eight-quadrants) diagram to map the distribution of urban centers in different scenarios of environmental improvements/degradation together with their per capita GDP and population size. Among the eight scenarios, except the first quadrant in which the three indicators are all improved, we considered the priority order of future actions among the three environmental indicators as greenness, then PM 2.5 , the third CO 2 emission based on their environmental roles and interactions between them. This prioritization is justified by several lines of evidence and economic considerations. First, enhancing greenness, besides its positive contribution to ecosystems and human health, plays a dual role in environmental management by contributing to both CO₂ sequestration and PM 2.5 reduction (Lin & Jiang, 2022 ). Second, policy measures specifically targeting PM 2.5 pollution have been shown to yield significant reductions in CO₂ emissions as a co-benefit. In contrast, initiatives that focus exclusively on CO₂ reduction do not effectively lower PM 2.5 concentrations. Third, from an economic perspective, the financial investment required for PM 2.5 mitigation is considerably lower compared to that needed for substantial CO₂ emission reductions (Anenberg et al., 2019 ; Yang et al., 2018b ). The eight quadrants were ranked as Quadrant I with the lowest mitigation efforts while Quadrant VIII with the highest mitigation efforts. Other quadrants are between. Finally, we explored the difference in driving patterns of the environmental development of the urban centers between Global South and Global North for identifying the causative chains (paths) of global urban environmental degradation. The structural equation model, using the piecewise SEM package (version 2.3.0) in R (Lefcheck, 2016 ) were employed in this study. SEM was selected for its ability to simultaneously evaluate multiple interrelated pathways and integrate both latent and observed variables, making it particularly well-suited for analysing the complex interactions within urban environmental systems. Specifically, SEM incorporates temperature, precipitation, and elevation as geographical variables; population, total built-up area, and prosperity as social variables; GDP and income group classifications as economic variables; CO₂ emissions as environmental destructor (cost); PM 2.5 level as the indicator of environmental victim (harm) ; and greenness as a measure of environmental contribution (benefit). A Linear Mixed-Effects Model (LMEM) with fixed and random effects was created, and the model was continually updated by adding random effects and adding and removing potential paths until the following two criteria are met: 1) no insignificant paths in the model, and 2) no significant relationships in Shipley's directional separation test. After determining the best model, we assessed the goodness-of-fit of the segmented SEM based on Fisher C and chi-square tests (p > 0.05). The final model is that met the criteria for adequate model fit. Then we compared the models between Global North and Global South. 5 Conclusion Our study assesses multiple environmental inequalities between Global South and Global North in over 10,000 urban centers. Three environmental indicators: urban greenness, air pollution (PM 2.5 ), CO 2 emissions, representing three differently environmental roles to society and human health (contribution, victmisation and destruction) are used, comparing their situation in 2000 and 2015. We find that the biggest environmental inequality is CO 2 emission (environmental destructions) although both are in the direction of deterioration, the secondary environmental inequality is PM 2.5 (environmental victimisation) and the minimal environmental inequality is greenness (environmental contributions) between Global South and Global North. The socio-economy plays a dominant role in environmental development in Global North while both socio-economic and natural endowments in Global South. The pronounced North-South disparity underscores the urgency of addressing the development dilemma of Global South and breaking the unequal exchange pattern between Global North and Global South. Declarations Author Contribution W.L. and K.Y. data extraction; Y.W., W.L., L.C. and Z.C., conceptualization; Y.W., L.C., W.L. and Z.C., methodology; W.L. and D.X., processing and empirical analysis; W.L., W.C. and D.X. software; W.L., W.C, K.Y. and Q.Z. visualization; W.L., Y.W. and L.C., writing – original draft; W.L., Y.W. and L.C., writing – review & editing; Y.W., Z.C. and M.L. supervision. Wei Li (W.L.); Yongping Wei (Y.W.); Lijuan Chen (L.C.); Zhenjie Chen (Z.C.); Manchun Li (M.L.); Wenqi Chen (W.C.); Kunshu Yang (K.Y.); Diandian Xu (D.X.); Qiqi Zhao (Q.Z.) Data Availability The primary data used in this study are publicly accessible and available for download or use from the following sources: The GHS-UCDB v1.212 is freely available for download via the JRC Open Data Catalogue, the official repository for JRC datasets (https://data.jrc.ec.europa.eu/dataset/53473144-b88c-44bc-b4a3-4583ed1f547e), or GHSL website (https://ghsl.jrc.ec.europa.eu/ghs_stat_ucdb2015mt_r2019a.php).The code that supports the findings of this study is available at: https://github.com/weii20230124/Mutiple-environmental-inequalities. References Akizu-Gardoki, O., Wakiyama, T., Wiedmann, T., Bueno, G., Arto, I., Lenzen, M., & Manuel Lopez-Guede, J. (2021). Hidden Energy Flow indicator to reflect the outsourced energy requirements of countries [Article]. J Cleaner Prod , 278 , Article 123827. https://doi.org/10.1016/j.jclepro.2020.123827 Amin, S. (1978). Unequal development: An essay on the social formations of peripheral capitalism. Sci Soc , 42 (2). Anenberg, S. 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CO\u003csub\u003e2\u003c/sub\u003e. b. Distribution of global CO\u003csub\u003e2\u003c/sub\u003e. c. Greenness. d. Distribution of global greenness. e. PM\u003csub\u003e2.5\u003c/sub\u003e. f. Distribution of global PM\u003csub\u003e2.5\u003c/sub\u003e.)\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-6374663/v1/6dd37fdf62ea47bc89e2e6f3.png"},{"id":82713068,"identity":"0bcd4ca4-a4ed-4175-a7ac-e306c70f8273","added_by":"auto","created_at":"2025-05-14 11:49:28","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":1825945,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eUrban environmental triangular contribution in Global North and Global South \u003c/strong\u003e(a. Global North b. Global South. Spatial distribution of the six areas c. Global North; d. Global South.)\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-6374663/v1/addea63f87776e0f9309ee81.png"},{"id":82712661,"identity":"982294d0-2354-4e56-97d7-023f85c52815","added_by":"auto","created_at":"2025-05-14 11:41:37","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":3330376,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eEnvironmental changes and socio-economic distribution in Global North and Global South\u003cbr\u003e\n\u003c/strong\u003e(a. Greenness vs Carbon Change of Global North. b. PM\u003csub\u003e2.5\u003c/sub\u003e vs Carbon Emission Change of Global North. c. Greenness vs PM\u003csub\u003e2.5\u003c/sub\u003e Change of Global North. d. Distribution of GDP per capita density in eight quadrants of Global North. e. Greenness vs Carbon Change of Global South. f. PM\u003csub\u003e2.5\u003c/sub\u003e vs Carbon Emission Change of Global South. g. Greenness vs PM\u003csub\u003e2.5\u003c/sub\u003e Change of Global South. i. Distribution of Global North environmental changes. h. Distribution of GDP per capita density in eight quadrants. j. Distribution of Global South environmental changes)\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-6374663/v1/f8435045c4da39ce66bb0c87.png"},{"id":82712621,"identity":"0cd8022c-d7e0-40dd-85cd-0106911e5260","added_by":"auto","created_at":"2025-05-14 11:41:28","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":1338061,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDriving pattern of urban environmental development \u003c/strong\u003e(a. Global North; b. Global South)\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-6374663/v1/637be7b851650db6b784ecaa.png"},{"id":82712622,"identity":"a65dce9f-6c27-494a-8150-b3196bef79c5","added_by":"auto","created_at":"2025-05-14 11:41:28","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":829032,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eConceptual model of a global North-South development model\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-6374663/v1/d38d2304bd1f1cc0557efe02.png"},{"id":82713070,"identity":"08134264-01e8-43e5-ac79-8545845c3a04","added_by":"auto","created_at":"2025-05-14 11:49:29","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":639338,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eConceptual ternary diagram of three environmental roles of the urban centers\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-6374663/v1/8be98430222322d1ad57566e.png"},{"id":97179626,"identity":"fa727243-e025-4712-b0a5-cec2cd9b1a67","added_by":"auto","created_at":"2025-12-01 16:16:24","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":10470516,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6374663/v1/4b17b4a1-3d11-4322-a253-3ad4727c00b3.pdf"},{"id":82712687,"identity":"7ed2d38e-66b7-470b-846b-d40072cdd9c6","added_by":"auto","created_at":"2025-05-14 11:41:40","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":1864780,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryInformation.docx","url":"https://assets-eu.researchsquare.com/files/rs-6374663/v1/d8388e5c492860470a51544e.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Multiple environmental inequalities between Global South and Global North in over 10,000 urban centers","fulltext":[{"header":"Highlights","content":"\u003cul\u003e\n \u003cli\u003eCO\u003csub\u003e2\u003c/sub\u003e emission of Global North is more than twice higher than Global South but mean PM\u003csub\u003e2.5\u003c/sub\u003e is more than twice lower, reflecting significantly higher environmental destruction but lower environmental victimization\u003c/li\u003e\n \u003cli\u003eGlobal South and Global North exhibit similar trends in greenness but have different causes with a luxury effect in Global North.\u003c/li\u003e\n \u003cli\u003eGlobal South has a much larger diversity in all the environmental indicators than those in Global North.\u003c/li\u003e\n \u003cli\u003eCO₂ levels mainly exhibited deterioration in both Global North and Global South, with all three environmental improvements 13.08% in Global North but only 5.10% in Global South and all three environmental degradation 5.21% in Global North but 16.53% in Global South from 2000 to 2015\u003c/li\u003e\n \u003cli\u003eThe biggest environmental inequality is CO\u003csub\u003e2\u003c/sub\u003e emission (environmental destructions), the secondary is PM\u003csub\u003e2.5\u003c/sub\u003e (environmental victimisation) andthe minimal is\u0026nbsp;greenness (environmental contributions) between Global South and Global North\u003c/li\u003e\n \u003cli\u003eThe socio-economy plays a dominant role in environmental development in Global North while both socio-economic and natural endowments in Global South\u003c/li\u003e\n\u003c/ul\u003e"},{"header":"1 Introduction","content":"\u003cp\u003eUrban centers occupy only 2\u0026ndash;3% of the global land surface but generate approximately 80% of global GDP and 70% of carbon emissions (Edenhofer et al., 2014; Fu et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Urban development has dominated the global process of land use transformation with economic development and societal prosperity (Keeler et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Meng et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). It has aggravated environmental degradation such as deforestation, habitat loss, air pollution, rising temperatures, and increased CO\u003csub\u003e2\u003c/sub\u003e emissions (Strokal et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Wiebe \u0026amp; Wilcove, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Moreover, the extent of environmental deterioration varies across cities, exacerbating global environmental inequality among urban centers. Therefore, integrating environmental sustainability into urban development is essential for achieving long-term sustainable growth.\u003c/p\u003e \u003cp\u003eGlobal urban development exhibits a range of well-documented phenomena, including the luxury effect in urban greenness (Li et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), the urban heat island effect (Wu et al., \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), the Environmental Kuznets Curve (EKC) (Sulemana et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), the decoupling effect of PM\u003csub\u003e2.5\u003c/sub\u003e concentrations from urban development (Li et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2025\u003c/span\u003e), energy poverty (Chan \u0026amp; Delina, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), and carbon leakage (Cao et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). While these phenomena provide critical insights into the interactions between environmental conditions and socio-economic processes for a certain environmental issue, they fail to capture the underlying mechanisms for multiple interactive environmental issues that often intertwine in many urban centers.\u003c/p\u003e \u003cp\u003eUrban growth creates significantly environmental inequalities. Built on the definition of social inequality, the environmental inequality refers to the uneven distribution of benefits and harms, rewards and costs with respect to the environment among social units such as individuals, groups, regions, and states. Recent literature on urban environmental inequalities has mainly emphasized positive contributions of natural environments such as greenspaces to health and well-being (Fanning et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Green \u0026amp; Healy, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Significant knowledge gaps remain: 1) most existing research tends to focus on a single environmental factor (urban greenspace), only reflecting benefits of the environmental change to the society and human health. However, urban environmental change are multi-dimensional, such as air pollution and increased CO\u003csub\u003e2\u003c/sub\u003e emissions which can reflect environmental inequality from harms and costs. 2) a large proportion of existing research focuses on regional scales\u0026mdash;often based on selected urban centers\u0026mdash;thereby covering only a narrow range of global climatic and sociodemographic conditions, which may restrict the generalizability of findings to other regions (Shao et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). 3) limited studies have investigated the net environmental change across a global sample of urban centres for a comparable time failing to explain the net outcomes of the destructive and constructive processes of urban development; 4) the geographical and socio-economic drivers of changes in environmental inequality remain unclear, limiting our capacity of promoting environmental equality in future.\u003c/p\u003e \u003cp\u003eUrban centers in Global North and Global South are widely heterogeneous (Arvidsson et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Balland et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Bettencourt et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). By 2022, GDP per capita in Global North was 12 times that of Global South, double the disparity observed in 1950 (Freeman, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Urban built infrastructure per capita is also significantly disproportionate, with some Global North countries surpassing those in Global South by more than 30-fold (Zhou et al., \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Limited economic development in Global South has constrained environmental management and protection efforts. As a result, urban centers in Global South possess only 70% of the cooling capacity of those in Global North (Li et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). High-risk areas for PM\u003csub\u003e2.5\u003c/sub\u003e concentrations are pervasive in Global South, including countries such as India and China (Lim et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Although most of these challenges and opportunities are concentrated in Global South, yet research remains largely focused on global megacities or urban centers in developed countries in Global North (Balland et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Nagendra et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eOur study aims to assess multiple environmental inequalities between Global South and Global North in over 10,000 urban centers. Three environmental indicators: urban greenness, air pollution (PM\u003csub\u003e2.5\u003c/sub\u003e), CO\u003csub\u003e2\u003c/sub\u003e emissions, representing three differently environmental roles to society and human health (positive contribution (benefit), victmisation (harm) and destruction (cost)) are used. The relative status of these three indicators are assessed with Ternary Diagram to partition their different roles and the temporal changes of these three indicators between 2000 and 2015 are assessed with a three-dimensional (eight-quadrants) diagram. Finally, structural equation modelling is developed to capture global gradients in key geographical and socio-economic drivers of the different environmental development patterns of urban centers between Global North and Global South. The findings from this study are expected to inform future planning and management that shape urban sustainability trajectories.\u003c/p\u003e"},{"header":"2 Results","content":"\u003cp\u003e\u003cstrong\u003e2.1\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eDifference in environmental development and its geographical, social, economic drivers between Global North and Global South\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eEnvironmental indicators of the urban centers present big differences between Global North and Global South, illustrated in Fig. 1. The global map of carbon emissions shows lower emissions in Global South (especially in Africa, India and South-East Asia) while much higher in North America, Europe and East Asia. On average, carbon emissions per capita are\u0026nbsp;1.73 tons in Global North,\u0026nbsp;significantly higher than the 0.55 tons in the\u0026nbsp;Global South. The global greenness map\u0026nbsp;reveals\u0026nbsp;that Europe, India, Southeast Asia, Eastern North America and Central Africa are greener. Global North has a relatively higher mean greenness\u0026nbsp;score (0.45) than\u0026nbsp;Global South\u0026nbsp;(0.40).\u0026nbsp;Conversely,\u0026nbsp;the\u0026nbsp;air quality indicator\u0026nbsp;shows\u0026nbsp;higher\u0026nbsp;PM\u003csub\u003e2.5\u003c/sub\u003e levels in Asia and Africa. Global North has a lower mean PM\u003csub\u003e2.5\u003c/sub\u003e concentration of 14.50 than Global South (38.77). In a word, Global North has twice higher mean CO\u003csub\u003e2\u003c/sub\u003e, slightly higher greenness and twice lower mean PM\u003csub\u003e2.5\u003c/sub\u003e than Global South while some urban centers in Global South present the case opposite to the mean values.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe geographical, social, and economic drivers are detailed in the Supplementary Information. Global North shows significantly lower average values of elevation, temperature, and precipitation compared to Global South. In contrast, Global North demonstrates notably higher socio-economic levels, while Global South exhibits greater variability in these indicators.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.2 \u0026nbsp; \u0026nbsp; \u0026nbsp; Difference in relative status of the three environmental indicators between Global South and Global North \u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn Global North, the mean values are 2.34 t a⁻\u0026sup1; for CO₂ per capita, 1.40\u0026times;10\u003csup\u003e-4\u003c/sup\u003e \u0026mu;g m⁻\u0026sup3; for PM\u003csub\u003e2.5\u003c/sub\u003e per capita, and 0.44 for greenness. After normalization, the percentage shares are 0.53 for CO₂, 0.05 for PM\u003csub\u003e2.5\u003c/sub\u003e, and 0.42 for greenness. As the consequence of the change in shares and location of three dividing lines, the \u0026quot;high-contributing\u0026quot; zone contains the most urban centers (24%), the \u0026quot;high-destruction\u0026quot; and \u0026quot;victimization-contribution trade-off\u0026quot; zones each account for 20%, all of them are higher than when thee area is equally shared. Contrarily, the \u0026quot;high-victimization\u0026quot; zone has the least (10%) and is concentrated in the lower part of these zones (Fig. 2a). The spatial map of the triangular contribution of the environment (Fig. 2c) shows that eastern North America and northern Europe in Global North are \u0026ldquo;high contribution areas\u0026rdquo;. Western North America, Japan and Australia are \u0026ldquo;high destruction areas\u0026rdquo;. Spatially, urban centers in Global North are highly concentrated, with the area within the 10th contour covering only 0.026 of the map and the highest densities surpassing the 80th contour, reflecting a high degree of spatial consistency (Fig. 2a).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn Global South, the mean values are 0.53 t a⁻\u0026sup1; for CO₂ per capita, 3.70\u0026times;10\u003csup\u003e-4\u003c/sup\u003e \u0026mu;g m⁻\u0026sup3; for PM\u003csub\u003e2.5\u0026nbsp;\u003c/sub\u003eper capita, and 0.40 for greenness. After normalization, the shares are 0.44 for CO₂, 0.15 for PM\u003csub\u003e2.5\u003c/sub\u003e, and 0.41 for greenness. In terms of zonal distribution (Fig. 2b), the \u0026quot;high-destructive\u0026quot; and \u0026quot;high-contributing\u0026quot; zones contain the largest share of urban centers (22% and 21%, respectively). The \u0026quot;destructive-contributing trade-off\u0026quot; zone has the smallest share (13%), with other zones averaging around 15%. In Global South, central Africa, South-East Asia and India are in the \u0026ldquo;high contribution area\u0026rdquo;. The \u0026ldquo;high destruction area\u0026rdquo; is found in East Asia, West Asia, South America and northern Mexico (Fig. 2d). Unlike Global North, urban centers in Global South are more dispersed, with the area within the 10th contour covering 0.49 of the map, and the highest density surpassing the 20th contour, indicating significant spatial heterogeneity.\u003c/p\u003e\n\u003cp\u003eBoth Global North and Global South exhibit a substantially high share of CO₂, indicating the importance of CO₂ in environmental development in both regions. Both regions have very similar shares of \u0026ldquo;high contribution area\u0026rdquo; (over 20%) . They have also similar trends in a relatively low share of PM\u003csub\u003e2.5\u003c/sub\u003e, but magnitude of largely differs: 0.05 in Global North and 0.15 in Global South, with a difference of 10%. Spatial patterns also differ. While Global North has a concentrated urban distribution, Global South is far more dispersed, with the 10th-contour area being 18.53 times larger than that of Global North.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.3\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Difference in environmental development from 2000 to 2015 between Global North and Global South\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn Global North, Quadrant I accounts for 13.08% of the regions, which comprises urban centers with comprehensive environmental improvements and GDP levels ranging from $10,000 to $20,000. It\u0026nbsp;is distributed in North America, Japan and parts of Europe (Fig. 3i; and Table 1), indicating that these urban centers achieving all-dimension improvements tend to have high economic levels. Quadrant II, accounting for 12.2% of the regions, features urban centers with GDP levels ranging from $5,000 to $15,000 which have made progress in air quality and CO₂ reduction but show limited advancements in greening and they are located in southern North America and Australia. Quadrant V, which includes 35.58% of the region. As the largest quadrant, it reflects the dominance of urban centers that have improved greening and air quality, despite challenges in reducing CO₂ emissions. It also has the broadest GDP distribution, peaking in the medium-to-high range of $5,000 to $25,000. These urban centers are spatially distributed in eastern North America and Western Europe. Quadrant VII representing 19.34% of the regions\u0026nbsp;with\u0026nbsp;greening improvements but deteriorations in air quality and CO₂ emissions. They include urban centers with lower GDP levels ($5,000 to $10,000) are primarily distributed in Eastern Europe and Japan.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e1\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;Distribution of Global North and Global South in the eight quadrants\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"559\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003eScenarios\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 314px;\"\u003e\n \u003cp\u003eContents of each scenario\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003ePercentage North (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003ePercentage South (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003eI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 314px;\"\u003e\n \u003cp\u003eImproved CO₂ emissions, PM₂.₅ concentration, and greenness\u003c/p\u003e\n \u003cp\u003e(Reduced destruction and victimization, increased contribution)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e13.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e5.10\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003eII\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 314px;\"\u003e\n \u003cp\u003eImproved CO₂ emissions and PM₂.₅ concentration but worse greenness\u003c/p\u003e\n \u003cp\u003e(Reduced destruction, victimization and reduced contribution)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e12.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e6.93\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003eIII\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 314px;\"\u003e\n \u003cp\u003eImproved CO₂ emissions and greenness but worse PM₂.₅ concentration\u003c/p\u003e\n \u003cp\u003e(Reduced destruction, but increased contribution and victimization)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e5.39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e1.19\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003eIV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 314px;\"\u003e\n \u003cp\u003eImproved CO₂ emissions but worse PM₂.₅ concentration and greenness\u003c/p\u003e\n \u003cp\u003e(Reduced destruction, increased victimization and reduced contribution)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e2.54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e1.87\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003eV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 314px;\"\u003e\n \u003cp\u003eWorse CO₂ emissions but improved PM₂.₅ concentration and greenness\u003c/p\u003e\n \u003cp\u003e(Increased destruction but decreased victimization and increased contribution)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e35.59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e25.04\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003eVI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 314px;\"\u003e\n \u003cp\u003eWorse CO₂ emissions and greenness but improved PM₂.₅ concentration\u003c/p\u003e\n \u003cp\u003e(Increased destruction but reduced contribution and victimization)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e6.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e26.23\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003eⅦ\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 314px;\"\u003e\n \u003cp\u003eWorse CO₂ emissions and PM₂.₅ concentration but improved greenness\u003c/p\u003e\n \u003cp\u003e(Increased destruction, victimization and contribution)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e19.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e17.10\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003eⅧ\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 314px;\"\u003e\n \u003cp\u003eWorse CO₂ emissions, PM₂.₅ concentration and greenness\u003c/p\u003e\n \u003cp\u003e(Increased destruction and victimization, reduced contribution)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e5.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e16.53\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eUnlike Global North, most Global South urban centers are represented in reddish-orange tones, indicating lower GDP per capita levels. Quadrant I in Fig. 3e-h and Table 1, comprising only 5.10% of urban centers which is 13.08% in Global North. They are spatially distributed in Africa with limited GDP per capita, concentrated below $2,000. Quadrant V (improved greening and air quality but increased CO₂ emissions ) accounts for 25.04% which is 35.58% in the Global North. They have a GDP per capita peak of approximately $500 and are distributed mainly in Mexico, Africa and Asia. Quadrant VII with greening improvements but deteriorations in air quality and CO₂ emissions includes 17.10%, similarly in Global North (19.34%), with a GDP distribution similar to Quadrant V but skewed toward higher income levels. They are spatially distributed mainly in Asia. Quadrant VI with improved air quality while struggling with deteriorations in greening and CO₂ emissions, representing 26.23% which is only 6.63% in Global North, features GDP levels below $1,500 are spatially distributed in South America and Asia. Quadrant VIII accounts for 16.53% of the urban centers in Global South which is only 5.1% in Global North, with the highest population density and GDP per capita concentrated around $1,000. They are spatially distributed in West Africa, West Asia and East Asia.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.4\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Difference in driving mechanism of environmental development between Global North and Global South\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn Global North, CO₂ emissions are primarily driven by economic, demographic, and geographic factors, with an explanatory power of 69% (Fig. 4). GDP and urban area have the strongest positive influence, followed by population. Geography plays a dual role, directly suppressing emissions while indirectly increasing them through GDP growth or land constraints. PM₂.₅ has a lower explanatory power (29%), with population and income levels contributing positively, while carbon emissions mitigate PM₂.₅. Population affects PM₂.₅ both directly and indirectly through carbon emissions. Greenness, with an explanatory power of 14%, is negatively influenced by income and urban prosperity (-0.273 and -0.080), creating conflicting effects.\u003c/p\u003e\n\u003cp\u003eIn Global South, carbon emission patterns are similar but with different factor influences, yielding an explanatory power of 62%. Population has a strong direct effect (0.414) and an indirect impact via GDP and built-up land. Geography restricts construction land but promotes income growth, which drives emissions. PM₂.₅ has a weak explanatory power (5%), with income groups and greenness exerting negative effects (-0.122 and -0.023), while carbon emissions have a minor positive effect (0.047). Indirectly, income groups suppress greenness, reducing PM₂.₅, but also promote carbon emissions, increasing PM₂.₅. Greenness, with an explanatory power of 27%, is negatively affected by geography, population, prosperity, and income, with prosperity having the strongest dampening effect (-0.345).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn a word, in Global North, population drives GDP, and GDP influences carbon emissions, but in Global South, population directly drives carbon emissions. Both regions share the role of built-up land area in driving carbon emissions. Another path shared by both regions is the dampening effect of prosperity on greenness, which is less in Global North than in Global South. In addition,\u0026nbsp;the geography in Global South also plays a role in greenness. Population in Global North influences PM\u003csub\u003e2.5\u003c/sub\u003e, but greenness in Global South influences PM\u003csub\u003e2.5\u003c/sub\u003e. These different driving patterns under different explanatory powers highlight the complex pathways shaping the three environmental inqualities between Global North and Global Sourth. However, in general, the economy of the Global North plays a dominant role while Global South is dependent on both socio-economic and natural endowments.\u0026nbsp;\u003c/p\u003e"},{"header":"3 Discussion","content":"\u003cp\u003eThere is a clear and statistically significant difference in the three environmental indicators and almost all of its geographic, social, and economic indicators of urban centers in Global South and Global North. This finding aligns with Zhou et al. (\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) on GDP, Nagendra et al. (\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) on urban prosperity and Chen et al. (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) on more diversity in Global South.\u003c/p\u003e \u003cp\u003eWe found that the biggest environmental inequality is CO\u003csub\u003e2\u003c/sub\u003e emission (environmental destructions) between Global North and Global South although both are in the direction of deterioration. Average CO\u003csub\u003e2\u003c/sub\u003e emission of Global North is more than twice higher than Global South. In terms of temporal changes, there is an increase in carbon emissions in both Global North and Global South. In line with Cheng et al. (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2022\u003c/span\u003e),, this is primarily due to the persistent increase in greenhouse gas emissions from fossil fuel combustion and land use changes since the 19th century. We also find a strong correlation between CO\u003csub\u003e2\u003c/sub\u003e and socio-economic indicators, with the explanation of more than 60% in both Global North and Global South, aligning with Zheng et al. (\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) and Betts-Davies et al. (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Rising CO₂ emissions are also partly driven by population expansion, particularly in low-income nations (Dong et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). The contribution of built-up land area to CO\u003csub\u003e2\u003c/sub\u003e is confirmed as the third global greenhouse gases emissions source (Lamb et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Finally, our results also find that Global North has a 27.88% share of urban centers with improved CO\u003csub\u003e2\u003c/sub\u003e, which is better than the 16.12% in the Global South. This disparity may be associated with regional differences in energy transitions (Lamb et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe secondary environmental inequality between Global North and Global South is PM\u003csub\u003e2.5\u003c/sub\u003e (environmental victimisation). PM\u003csub\u003e2.5\u003c/sub\u003e in Global North is more than twice lower than Global South. The spatial distribution of regions with high PM\u003csub\u003e2.5\u003c/sub\u003e concentrations is in Asia and Africa with the more share of urban centers in high- victimisation areas in Global South (14%) than Global North (10%). Historical changes see 72.66% of Global North urban centers improved PM\u003csub\u003e2.5\u003c/sub\u003e, while only 62.63% in Global South. The geographical, social economic factors have very low explanation of PM\u003csub\u003e2.5\u003c/sub\u003e in Global North (29%) and Global South (5%), indicating the causative complexity of PM\u003csub\u003e2.5\u003c/sub\u003e, agreed with Li et al. (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), Yang et al. (\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2018a\u003c/span\u003e) and Behrer and Heft-Neal (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Behrer and Heft-Neal (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) found there was a \u0026ldquo;decoupling\u0026rdquo; of PM\u003csub\u003e2.5\u003c/sub\u003e in developed countries: as urban centers continue to expand, PM\u003csub\u003e2.5\u003c/sub\u003e concentrations decline. Moreover, the occurrence and transport of PM2.5 are influenced by a complex interplay of interrelated factors acting across multiple spatial scales, which complicates the identification of their individual effects (Lim et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Zhang et al., \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2017\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe minimal environmental inequality is greenness (environmental contributions). Greenness is above 0.40 in both Global North and Global South. However, from 2000 to 2015, greenness has been improved in 74% of urban centers in Gobal North and only 48% in Global South. Although the geographical, social and economic factors have low explanation of greenness in Global North (14%) and Global South (27%), the common recognitions include heterogeneity of geography leads to differences in greenness between Global North and Global South (Haaland \u0026amp; van den Bosch, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Then, there is a well-documented positive correlation between green space availability and wealth, known as the \"luxury effect\" (Li et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Yin et al., \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Finally, the destruction and construction of greenspace can also occur periodically and are not always strictly chronological (Zhang et al., \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Therefore, there are different causes for the similar greenness in Global North and Global South.\u003c/p\u003e \u003cp\u003eIn addition, our study finds that greenness has a minimal impact on carbon emissions in both Global North and South. In Global North, CO₂ suppresses PM₂.₅, whereas in Global South, CO₂ increases PM₂.₅. Additionally, greenness mitigates PM₂.₅ in Global South. These findings are not a surprise from the existing studies with different results (Anenberg et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Yang et al., \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2018b\u003c/span\u003e). Therefore, although greenspace often used a proxy for evaluating urban environmental sustainability, based on our findings, urban greening strategies are not a panacea for addressing all environmental inequalities. Further research to improving an understanding of the interactions among the three environmental dimensions will assist in more simple and cost-effective policies for reducing gaps of environmental sustainable development between Global North and Global South.\u003c/p\u003e \u003cp\u003eThe inequalities of the three environmental indicators between Global North and Global South may also arise from the unequal exchange between them. Global economic integration has led to a situation where resource and labor flows from the Global South\u0026mdash;amplified by trade price differentials\u0026mdash;substantially support the economic growth of the Global North (Amin, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e1978\u003c/span\u003e; Hickel et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). In the meanwhile, some backward enterprises have difficulties in surviving in developed regions (countries) and move industries with high carbon emissions and other pollutants to regions (countries) that need to develop their economies, thus leading to developing regions in Global South becoming pollution havens (Akizu-Gardoki et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Bai et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Yang et al., \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Meng et al. (\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) confirmed that unequal exchange is a significant driver of global inequality of environmental development, even ecological breakdown. This partly explains our finding that CO\u003csub\u003e2\u003c/sub\u003e in Global North is strongly bound to economic development but to population size in Global South and more urban centers in Global South are environmental victims. Built upon the Red Ring-Green Ring model proposed by Cumming et al. (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2014\u003c/span\u003e) and Cumming and von Cramon-Taubadel (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), by combining the results of structural equation modeling with this environmentally unequal trade between Global North and Global South, we develop a more whole-of-system development patterns of Global North and Global South in which the economy of Global North plays a dominant role while Global South is more dependent on both socio-economic and natural endowments (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). To achieve sustainable development of urban centers around the world, it is necessary to pay attention to the development dilemma of Global South and to break the unequal exchange pattern between Global North and Global South.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFinally, our study reveals a considerable degree of heterogeneity within Global South, encompassing urban centers that have already begun to resemble Global North. Consequently, generalizations within the Global South are not perfectly-suited. The scale and intensity of urbanization, environmental pressures, and their social and economic effects underscore the Global South\u0026rsquo;s critical role in shaping the trajectory of international sustainability. Future research could potentially offer a more nuanced typification of Global South and incorporate a wider range of geographic and socio-economic indicators and temporal data.\u003c/p\u003e"},{"header":"4 Methods","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\n \u003ch2\u003e4.1 Data source\u003c/h2\u003e\n \u003cp\u003eThe Global Human Settlement Urban Centre Database (GHS-UCDB) was used in this study, covering over 10,000 urban centres in various areas across the globe (Melchiorri et al., \u003cspan class=\"CitationRef\"\u003e2024\u003c/span\u003e). This database includes the data on geography, socio-economics, environment, disaster risk reduction, and sustainable development, as well as the location and extent of each urban centre, covering 1990, 2000, and 2015. It is generated by integrating rich geospatial data provided under open data policies and new Earth observation programs, such as the Copernicus program. The Global Human Settlement Layer (GHSL) project delineates urban centers using the \u0026quot;Degree of Urbanisation\u0026quot; method based on population density and built-up area thresholds. Utilizing Geographic Information System (GIS) technologies, multi-thematic and multi-temporal variables are linked to these urban centers through methods like zonal statistics and spatial analysis, with quality ensured via high-resolution imagery verification. Therefore, this database offers researchers a robust foundation for examining complex urban phenomena at a regional and global scale.\u003c/p\u003e\n \u003cp\u003eGlobal South includes urban centers in South America, Africa and parts of Asia, while Global North includes urban centers in North America, Europe, Australia and developed Asia (Simone, \u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003e). Among the 11,683 urban centres in the database, there are 10,070 urban centers in Global South, accounting for 86.2% of the urban centres, while 1,613 urban centers in Global North, account for 13.8% of the global urban centers. For instance, in Global South, urban centers such as S\u0026atilde;o Paulo and Rio de Janeiro in South America, Lagos and Nairobi in Africa, and Mumbai and Jakarta in Asia are representative examples. In Global North, typical urban centers include New York and Toronto in North America, London and Paris in Europe, Sydney and Melbourne in Australia, as well as Tokyo and Seoul in developed Asia.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\n \u003ch2\u003e4.2 Characterising urban environments as a complex social-ecological system\u003c/h2\u003e\n \u003cp\u003eWith the rapid urbanization in the past decades, the interconnectedness and interdependency of the urban environment with other various components of urban centres have been raised to a level that has never happened before at which the urban environment could not be examined as a single system any longer. We considered urban environments as a complex socio-ecological system, in which urban environmental development is dependent upon with its geographical features (natural endowments) and social and economic drivers.\u003c/p\u003e\n \u003cp\u003eWe included three indicators for characterizing environmental development: carbon dioxide (CO₂), PM\u003csub\u003e2.5\u003c/sub\u003e and Greenness. CO₂ emission mainly indicates the degree of greenhouse gases changes which is often regarded as a symbol of the adverse effects of human activities on the Earth climate system, leading to a series of environmental crises. Thus, it plays a \u0026ldquo;destruction (cost)\u0026rdquo; role in urban environmental sustainability. PM\u003csub\u003e2.5\u003c/sub\u003e serves as a key indicator of air quality, reflecting the deterioration of air environmental quality by pollution and doing harm to human health. It plays a \u0026ldquo;victmisation (harm)\u0026rdquo; role in urban environmental sustainability. Greenness, represented by the average greenness within built-up areas facilitating the comparison between urban centers, is typically associated with the ecosystem services provided by urban green spaces. An elevated level of greenness reflects a positive contribution to ecosystems and human health. Thus greenness plays a \u0026ldquo;contribution (benefit)\u0026rdquo; role in urban environmental sustainability. These three environmental indicators reflect the environmental inequality of urban centers from differences of urban centers in increase in environmental cost, exposure to environmental harms, and access to benefits.\u003c/p\u003e\n \u003cp\u003eGeographical indicators included in this paper are temperature, precipitation and elevation, which primarily represent natural endowments and express the geographic and climatic context of urban centers. Social indicators include per capita built-up land area and prosperity measured by nighttime light intensity, which mainly reflect urban spatial organization and the intensity of human activities. Economic indicators include per capita GDP and income group classifications, which primarily indicate the level of economic development and wealth distribution.\u003c/p\u003e\u003cspan\u003e\n \u003cp\u003e\u003cstrong\u003e4.3 Analysis of the differences in the environmental development of the urban centres between Global North and Global South\u003c/strong\u003e\u003c/p\u003e\n \u003c/span\u003e\n \u003cp\u003eFirst, we analysed the distributional difference of the urban centres between the Global North and Global South on the geographic, social, economic and environmental indicators to provide the basis for further analysis. The Mann-Whitney U test, a non-parametric method for non-normally distributed data is empoyed. The method calculates statistics through ranked comparisons and assesses the significance of the difference in terms of p-value. Its advantage is that it requires fewer assumptions about the distribution of the data and can effectively deal with outliers and asymmetric distribution.\u003c/p\u003e\n \u003cp\u003eThen, we analysed the difference in relative status of the three environmental indicators of the urban centres to identify the most degraded environmental development and its difference between Global North and Global South. To differentiate the relative status of the urban centers between Global North and Global South, we employed Ternary Diagram to partition the different roles of the three environmental indicators with their relative proportion (Fig. \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e). Three vertices of the triangle are carbon dioxide, PM\u003csub\u003e2.5\u003c/sub\u003e, and greenness. The coordinate axes increase in a counter clockwise direction. A dividing line is drawn at 1/3 of each indicator, dividing the triangle into six parts. According to the difference in the values of the indicators, the six sections are formed as follows: \u0026ldquo;high destruction area\u0026rdquo;, \u0026ldquo;high victimization area\u0026rdquo;, \u0026ldquo;high contribution area\u0026rdquo;, \u0026ldquo;destruction and victimization trade-off area\u0026rdquo;, \u0026ldquo;contribution and victimization trade-off area\u0026rdquo;, \u0026ldquo;destruction and contribution trade-off area\u0026rdquo;. The location and size of these six areas can be used to assess the relative environmental status of each urban center and compare them between Global North and Global South.\u003c/p\u003e\n \u003cp\u003eIn order to eliminate dimensional differences to ensure data comparability, CO₂ emissions, PM₂.₅ concentration, and greenness were normalized. Subsequently, the three indicators were then aggregated to calculate the proportion of CO₂ emissions, PM₂.₅ concentration, and greenness for each urban center, thereby deriving the relative proportions of these indicators. Finally, these relative proportions of the urban centers were mapped onto a ternary plot to visually present the balance among CO₂ emissions, air quality, and greening levels and six areas.\u003c/p\u003e\n \u003cp\u003eFollowing that, we analysed the temporal changes of the three environmental indicators of the urban centers between Global North and Global South to identify if they are improved/degraded. The temporal changes were analysed between 2000 and 2015, with 2000 when environmental governance started globally and 2015 when the most updated data were available. The findings can assist in identifying the urban centre where the most urgent actions should be taken, and, if linked to the policies implemented, identifying the effectiveness of the environmental policies. The environmental improvements are calculated as follows:\u003c/p\u003e\n \u003cp\u003eFor CO₂ emissions and PM₂.₅ concentration:\u003c/p\u003e\n \u003cdiv id=\"Equa\" class=\"Equation\"\u003e\n \u003cdiv class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e$$\\:\\begin{array}{c}\\varDelta\\:X=\\frac{{X}_{t1}-{X}_{t2}}{{X}_{t1}}\\#\\left(3\\right)\\end{array}$$\u003c/div\u003e\n \u003c/div\u003e\n \u003cp\u003eFor greenness:\u003c/p\u003e\n \u003cdiv id=\"Equb\" class=\"Equation\"\u003e\n \u003cdiv class=\"mathdisplay\" id=\"FileID_Equb\" name=\"EquationSource\"\u003e$$\\:\\begin{array}{c}\\varDelta\\:X=\\frac{{X}_{t2}-{X}_{t1}}{{X}_{t1}}\\#\\left(4\\right)\\end{array}$$\u003c/div\u003e\n \u003c/div\u003e\n \u003cp\u003eWhere: \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\varDelta\\:X\\)\u003c/span\u003e\u003c/span\u003e represents the percentage improvement of the environmental indicator, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{X}_{t1}\\)\u003c/span\u003e\u003c/span\u003e is the value of the indicator in the year 2000, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{X}_{t2}\\)\u003c/span\u003e\u003c/span\u003e is the value of the indicator in the year 2015.\u003c/p\u003e\n \u003cp\u003eWe used a three-dimension (eight-quadrants) diagram to map the distribution of urban centers in different scenarios of environmental improvements/degradation together with their per capita GDP and population size. Among the eight scenarios, except the first quadrant in which the three indicators are all improved, we considered the priority order of future actions among the three environmental indicators as greenness, then PM\u003csub\u003e2.5\u003c/sub\u003e, the third CO\u003csub\u003e2\u003c/sub\u003e emission based on their environmental roles and interactions between them. This prioritization is justified by several lines of evidence and economic considerations. First, enhancing greenness, besides its positive contribution to ecosystems and human health, plays a dual role in environmental management by contributing to both CO₂ sequestration and PM\u003csub\u003e2.5\u003c/sub\u003e reduction (Lin \u0026amp; Jiang, \u003cspan class=\"CitationRef\"\u003e2022\u003c/span\u003e). Second, policy measures specifically targeting PM\u003csub\u003e2.5\u003c/sub\u003e pollution have been shown to yield significant reductions in CO₂ emissions as a co-benefit. In contrast, initiatives that focus exclusively on CO₂ reduction do not effectively lower PM\u003csub\u003e2.5\u003c/sub\u003e concentrations. Third, from an economic perspective, the financial investment required for PM\u003csub\u003e2.5\u003c/sub\u003e mitigation is considerably lower compared to that needed for substantial CO₂ emission reductions (Anenberg et al., \u003cspan class=\"CitationRef\"\u003e2019\u003c/span\u003e; Yang et al., \u003cspan class=\"CitationRef\"\u003e2018b\u003c/span\u003e). The eight quadrants were ranked as Quadrant I with the lowest mitigation efforts while Quadrant VIII with the highest mitigation efforts. Other quadrants are between.\u003c/p\u003e\n \u003cp\u003eFinally, we explored the difference in driving patterns of the environmental development of the urban centers between Global South and Global North for identifying the causative chains (paths) of global urban environmental degradation. The structural equation model, using the piecewise SEM package (version 2.3.0) in R (Lefcheck, \u003cspan class=\"CitationRef\"\u003e2016\u003c/span\u003e) were employed in this study. SEM was selected for its ability to simultaneously evaluate multiple interrelated pathways and integrate both latent and observed variables, making it particularly well-suited for analysing the complex interactions within urban environmental systems. Specifically, SEM incorporates temperature, precipitation, and elevation as geographical variables; population, total built-up area, and prosperity as social variables; GDP and income group classifications as economic variables; CO₂ emissions as environmental destructor (cost); PM\u003csub\u003e2.5\u003c/sub\u003e level as the indicator of environmental victim (harm) ; and greenness as a measure of environmental contribution (benefit). A Linear Mixed-Effects Model (LMEM) with fixed and random effects was created, and the model was continually updated by adding random effects and adding and removing potential paths until the following two criteria are met: 1) no insignificant paths in the model, and 2) no significant relationships in Shipley\u0026apos;s directional separation test. After determining the best model, we assessed the goodness-of-fit of the segmented SEM based on Fisher C and chi-square tests (p\u0026thinsp;\u0026gt;\u0026thinsp;0.05). The final model is that met the criteria for adequate model fit. Then we compared the models between Global North and Global South.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"5 Conclusion","content":"\u003cp\u003eOur study assesses multiple environmental inequalities between Global South and Global North in over 10,000 urban centers. Three environmental indicators: urban greenness, air pollution (PM\u003csub\u003e2.5\u003c/sub\u003e), CO\u003csub\u003e2\u003c/sub\u003e emissions, representing three differently environmental roles to society and human health (contribution, victmisation and destruction) are used, comparing their situation in 2000 and 2015. We find that the biggest environmental inequality is CO\u003csub\u003e2\u003c/sub\u003e emission (environmental destructions) although both are in the direction of deterioration, the secondary environmental inequality is PM\u003csub\u003e2.5\u003c/sub\u003e (environmental victimisation) and the minimal environmental inequality is greenness (environmental contributions) between Global South and Global North. The socio-economy plays a dominant role in environmental development in Global North while both socio-economic and natural endowments in Global South. The pronounced North-South disparity underscores the urgency of addressing the development dilemma of Global South and breaking the unequal exchange pattern between Global North and Global South.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eW.L. and K.Y. data extraction; Y.W., W.L., L.C. and Z.C., conceptualization; Y.W., L.C., W.L. and Z.C., methodology; W.L. and D.X., processing and empirical analysis; W.L., W.C. and D.X. software; W.L., W.C, K.Y. and Q.Z. visualization; W.L., Y.W. and L.C., writing \u0026ndash; original draft; W.L., Y.W. and L.C., writing \u0026ndash; review \u0026amp; editing; Y.W., Z.C. and M.L. supervision. Wei Li (W.L.); Yongping Wei (Y.W.); Lijuan Chen (L.C.); Zhenjie Chen (Z.C.); Manchun Li (M.L.); Wenqi Chen (W.C.); Kunshu Yang (K.Y.); Diandian Xu (D.X.); Qiqi Zhao (Q.Z.)\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe primary data used in this study are publicly accessible and available for download or use from the following sources: The GHS-UCDB v1.212 is freely available for download via the JRC Open Data Catalogue, the official repository for JRC datasets (https://data.jrc.ec.europa.eu/dataset/53473144-b88c-44bc-b4a3-4583ed1f547e), or GHSL website (https://ghsl.jrc.ec.europa.eu/ghs_stat_ucdb2015mt_r2019a.php).The code that supports the findings of this study is available at: https://github.com/weii20230124/Mutiple-environmental-inequalities.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAkizu-Gardoki, O., Wakiyama, T., Wiedmann, T., Bueno, G., Arto, I., Lenzen, M., \u0026amp; Manuel Lopez-Guede, J. (2021). 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Satellite mapping of urban built-up heights reveals extreme infrastructure gaps and inequalities in the Global South [Article]. \u003cem\u003eProc Natl Acad Sci U S A\u003c/em\u003e,\u003cem\u003e 119\u003c/em\u003e(46), Article e2214813119. https://doi.org/10.1073/pnas.2214813119\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"npj-urban-sustainability","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"npjurbansustain","sideBox":"Learn more about [npj Urban Sustainability](https://www.nature.com/npjurbansustain/)","snPcode":"42949","submissionUrl":"https://submission.springernature.com/new-submission/42949/3","title":"npj Urban Sustainability","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"NPJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Urban centers, Global South and North, Heterogeneity, Inequalities, Climate and environment, Piecewise SEM","lastPublishedDoi":"10.21203/rs.3.rs-6374663/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6374663/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eOur study aims to assess multiple environmental inequalities between Global South and Global North in over 10,000 urban centers. Three environmental indicators: greenness, air-pollution (PM\u003csub\u003e2.5\u003c/sub\u003e), CO\u003csub\u003e2\u003c/sub\u003e emissions are used, representing three differently environmental roles to society and human health (contribution (benefit), victmisation (harm) and destruction (cost)). The relative status and change of these three indicators from 2000 to 2015 are assessed. Our findings indicate that the CO₂ emissions in Global North is more than twice those in the Global South, whereas the mean PM₂.₅ concentration is less than half, reflecting significantly higher environmental destruction (indicated by CO₂ emissions) but lower environmental victimization (indicated by PM₂.₅). Global South and Global North exhibit similar trends in greenness but have different causes with a luxury effect in Global North.\u003cstrong\u003e \u003c/strong\u003eThe socio-economy plays a dominant role in environmental development in Global North while both socio-economic and natural endowments in Global South.\u003c/p\u003e","manuscriptTitle":"Multiple environmental inequalities between Global South and Global North in over 10,000 urban centers","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-05-14 11:41:23","doi":"10.21203/rs.3.rs-6374663/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-07-29T15:42:41+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-07-28T03:49:11+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"45870483717077747707932134538413121033","date":"2025-07-09T06:37:56+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-05-21T09:45:05+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"145609721782975673044350244306500291769","date":"2025-05-14T07:42:22+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-05-09T07:18:51+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-04-30T06:53:45+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-04-08T18:34:21+00:00","index":"","fulltext":""},{"type":"submitted","content":"npj Urban Sustainability","date":"2025-04-04T08:51:30+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"npj-urban-sustainability","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"npjurbansustain","sideBox":"Learn more about [npj Urban Sustainability](https://www.nature.com/npjurbansustain/)","snPcode":"42949","submissionUrl":"https://submission.springernature.com/new-submission/42949/3","title":"npj Urban Sustainability","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"NPJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"ba2786b2-3ca9-4b32-90fb-6580af6ee777","owner":[],"postedDate":"May 14th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":48419431,"name":"Earth and environmental sciences/Environmental sciences/Environmental impact"},{"id":48419432,"name":"Earth and environmental sciences/Environmental social sciences/Sustainability"},{"id":48419433,"name":"Scientific community and society/Geography"}],"tags":[],"updatedAt":"2025-12-01T16:13:51+00:00","versionOfRecord":{"articleIdentity":"rs-6374663","link":"https://doi.org/10.1038/s42949-025-00302-z","journal":{"identity":"npj-urban-sustainability","isVorOnly":false,"title":"npj Urban Sustainability"},"publishedOn":"2025-11-24 15:58:11","publishedOnDateReadable":"November 24th, 2025"},"versionCreatedAt":"2025-05-14 11:41:23","video":"","vorDoi":"10.1038/s42949-025-00302-z","vorDoiUrl":"https://doi.org/10.1038/s42949-025-00302-z","workflowStages":[]},"version":"v1","identity":"rs-6374663","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6374663","identity":"rs-6374663","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}