Nighttime Light Research Articles

Exploring the Dynamic Invasion Pattern of the Black-Headed Fall Webworm in China: Susceptibility to Topography, Vegetation, and Human Activities.

The fall webworm (FWW), H. cunea (Drury) (Lepidoptera: Erebidae: Arctiidae), is an extremely high-risk globally invasive pest. Understanding the invasion dynamics of invasive pests and identifying the critical factors that promote their spread is essential for devising practical and efficient strategies for their control and management. The invasion dynamics of the FWW and its influencing factors were analyzed using standard deviation ellipse and spatial autocorrelation methods. The analysis was based on statistical data on the occurrence of the FWW in China. The dissemination pattern of the FWW between 1979 and 2022 followed a sequence of "invasion-occurrence-transmission-outbreak", spreading progressively from coastal to inland regions. Furthermore, areas with high nighttime light values, abundant ports, and non-forested areas with low vegetation cover at altitudes below 500 m were more likely to be inhabited by the black-headed FWW. The dynamic invasion pattern and the driving factors associated with the fall webworm (FWW) provide critical insights for future FWW management strategies. These strategies serve not only to regulate the dissemination of insects and diminish migratory tendencies but also to guarantee the implementation of efficient early detection systems and prompt response measures.

Spatiotemporal Pattern and Spatial Convergence of Land Use Carbon Emission Efficiency in the Pan-Pearl River Delta: Based on the Difference in Land Use Carbon Budget

Research on land use carbon emission efficiency (LUCEE) in the Pan-Pearl River Delta (PPRD) can aid in formulating regional differentiated carbon reduction strategies. In this work, the inversion of carbon emissions using night-time light (NTL) data and the modified Carnegie Ames Stanford Approach (CASA) model were used to measure the net carbon emissions from land use (NCELU). On this basis, the SBM-undesirable model was used to assess the LUCEE. Additionally, the exploratory spatial data analysis (ESDA), Dagum Gini coefficient, and spatial convergence model were further introduced to analyze the spatial correlation, regional differences, and convergence trend of the LUCEE. Findings indicate that: (1) The NCELU showed an increasing fluctuation. During the period of 2006–2020, the NCELU increased from −168.58 million tons to −724.65 million tons. (2) The LUCEE exhibited a three-phase fluctuating downward trend of “decrease–rise–decrease”. The LUCEE first decreased from 0.612 in 2006 to 0.544 in 2008, then gradually increased to 0.632 in 2016, and finally decreased to 0.488 in 2020. Spatially, the LUCEE manifested a distribution characteristic of “high in the north and south, low in the middle”, with distinct spatial clustering features. (3) The overall Gini coefficient in the study period increased from 0.1819 to 0.2461. The primary contributor to the overall difference over the entire sample period was hypervariable density. (4) The PPRD and its various subregions displayed significant features of absolute and conditional β convergence. The speed of regional convergence from fastest to slowest was central > west > east, with the absolute convergence speeds of 0.0505, 0.0360, and 0.0212, respectively. Finally, policy recommendations are proposed to achieve regional carbon neutrality for the PPRD.

Evaluating urban development in China's resource-based cities: a new perspective using nighttime light data

ABSTRACT Understanding the urbanization and transformation trajectories of resource-based cities (RBCs) is pivotal for China’s sustainable development goals. This study introduces a novel regression-based algorithm for assessing urbanization patterns. We delineated the urban boundaries of 335 cities and employed time-series nighttime light data from 2001 to 2020, shifting the analysis from a pixel-scale to an urban-scale perspective. Our analysis reveals distinct disparities within the 125 RBCs when compared to the national average, leading to their categorization. The key findings include: (1) Within Forest and Coal RBCs, numerous areas have stabilized after experiencing contraction. Geographically, a significant number of RBCs in the Northeast, North, and Northwest regions are experiencing or have experienced substantial urban shrinkage. The developmental status of RBCs exhibits a spatial positive correlation. (2) Although the government categorized RBCs based on their development level in 2013, findings suggest that this classification may no longer accurately reflect the current development status of RBCs in the context of urbanization. (3) Utilizing spectral clustering, we categorized RBCs into five types, identified 10 RBCs undergoing shrinkage and 30 cities trending towards stabilization post-shrinkage. This research offers a refined evaluative method for urbanization, providing insights beneficial for policy-making concerning RBCs’ sustainable growth..

Spatial pattern analysis of the impact of community food environments on foetal macrosomia, preterm births and low birth weight.

Community food environments (CFEs) have a strong impact on child health and nutrition and this impact is currently negative in many areas. In the Republic of Argentina, there is a lack of research evaluating CFEs regionally and comprehensively by tools based on geographic information systems (GIS). This study aimed to characterize the spatial patterns of CFEs, through variables associated with its three dimensions (political, individual and environmental), and their association with the spatial distribution in urban localities in Argentina. CFEs were assessed in 657 localities with ≥5,000 inhabitants. Data on births and CFEs were obtained from nationally available open-source data and through remote sensing. The spatial distribution and presence of clusters were assessed using hotspot analysis, purely spatial analysis (SaTScan), Moran's Index, semivariograms and spatially restrained multivariate clustering. Clusters of low risk for LBW, macrosomia, and preterm births were observed in the central-east part of the country, while high-risk clusters identified in the North, Centre and South. In the central-eastern region, low-risk clusters were found coinciding with hotspots of public policy coverage, high night-time light, social security coverage and complete secondary education of the household head in areas with low risk for negative outcomes of the birth variables studied, with the opposite with regard to households with unsatisfied basic needs and predominant land use classes in peri-urban areas of crops and herbaceous cover. These results show that the exploration of spatial patterns of CFEs is a necessary preliminary step before developing explanatory models and generating novel findings valuable for decision-making.

Multi-year soundscape recordings and automated call detection reveals varied impact of moonlight on calling activity of neotropical forest katydids.

Night-time light can have profound ecological effects, even when the source is natural moonlight. The impacts of light can, however, vary substantially by taxon, habitat and geographical region. We used a custom machine learning model built with the Python package Koogu to investigate the in situ effects of moonlight on the calling activity of neotropical forest katydids over multiple years. We prioritised species with calls that were commonly detected in human annotated data, enabling us to evaluate model performance. We focused on eight species of katydids that the model identified with high precision (generally greater than 0.90) and moderate-to-high recall (minimum 0.35), ensuring that detections were generally correct and that many calls were detected. These results suggest that moonlight has modest effects on the amount of calling, with the magnitude and direction of effect varying by species: half of the species showed positive effects of light and half showed negative. These findings emphasize the importance of understanding natural history for anticipating how biological communities respond to moonlight. The methods applied in this project highlight the emerging opportunities for evaluating large quantities of data with machine learning models to address ecological questions over space and time. This article is part of the theme issue 'Towards a toolkit for global insect biodiversity monitoring'.

China’s urbanisation evolution and metropolitan area expansion, based on the Prolonged Artificial Nighttime-light Dataset (PANDA, 1984–2020)

ABSTRACT China’s massive urbanisation development will undoubtedly serve as a global reference. Due to the uncertainty of statistical data, nighttime light (NTL) data have emerged as alternative data for urbanisation evaluation. Here, the application of the Prolonged Artificial NTL Dataset of China (1984–2020) has been newly expanded. Total-value statistical, complex NTL index (CNLI), NTL concentration degree, rank-size rule, and Markov transfer matrix were used to systematically mine information about urban change from multiple perspectives. China’s urbanisation exhibited rapid growth and outwards expansion. The total percentage increase in NTL brightness and NTL area was 409.38% and 302.58%, respectively. The Yangtze River Delta urban agglomeration of East China had the fastest urbanisation (CNLI Trend = 0.0057/a, p < 0.01). Low NTL areas transitioned to medium NTL areas, and high and extremely-high NTL areas diffused to medium NTL areas. The cities in Eastern China and Southern China typically exhibited extremely-high type NTL areas, whereas other cities primarily exhibited medium NTL areas. The gaps between city sizes decreased over time (q Trend = – 0.0200/a, p < 0.01). Lowest-ranked cities exhibited the highest stability (>95%) in city size type transition. The spatiotemporal changes in NTL obtained were of great significance for monitoring urban expansion patterns, making government decisions, and quantifying China’s sustainable development.

Evaluation of the light pollution in the nature reserves of China based on NPP/VIIRS nighttime light data

ABSTRACT Nature reserves play a key role in the conservation of habitats, species and ecosystems in China. However, the issue of light pollution within these reserves has not received well recognition. Whereas previous studies have investigated light pollution in protected areas by remote sensing, the influence of the atmospheric scattered light from neighborhoods was ignored by using only direct light emissions. This study mapped the nighttime all-sky brightness from NPP/VIIRS data to quantify light pollution over the national nature reserves in China from 2013 to 2022. Based on the remotely sensed sky brightness, the spatio-temporal variations of sky brightness were analyzed. The mean sky brightness across all nature reserves was 0.63 mcd/m2 and the mean trend was 0.0189 mcd/m2/a, indicating obvious and accelerating light pollution. Furthermore, the sky brightness around the nature reserves was estimated and compared with the reserves. In general, the surrounding regions showed higher sky brightness and brightening trends, suggesting a certain but insufficient protective effect on the sky quality of nature reserves. This study provides a comprehensive understanding of the light pollution conditions in the national nature reserves in China and serves as a valuable reference for future assessments of light pollution using remote sensing data.

Characteristics and drivers of vegetation temporal dynamics in Hunan Province of China during 2002-2020

Understanding the influences of climate change and human activities on vegetation change is the foundation for effective ecosystem management. Based on the 250 m MODIS-NDVI data from 2002 to 2020, we employed Theil-Sen Median trend analysis and the Mann-Kendall test to quantify vegetation change in Hunan Province. By combining with meteorological, nighttime light index, land cover and other data, residual analysis and correlation analysis, we examined the impacts of human activities and climate change on vegetation dynamics at both the pixel level and the county level. The results showed that the normalized difference vegetation index (NDVI) in Hunan Province exhibited a spatial pattern of "overall improvement with localized degradation" during 2002-2020. Approximately 64.9% of the study area experienced significant vegetation improvement, mainly occurring in the western and central-southern parts of Hunan Province. 1.4% of the study area experienced significant vegetation degradation, mostly in the newly developed urban areas and the farmland in the Dongting Lake Plain. Human activities and climate change jointly promoted vegetation improvement in 67.9% of the study area. Human activities and climate contributed to 96% and 4% of the NDVI change, respectively. At the county level, human activities contributed to over 80% of the NDVI change in each district or county. The impacts of human activities on vegetation change exhibited significant spatial heterogeneity. Urban expansion led to vegetation degradation in the newly developed areas, while vegetation growth appeared in the old developed urban areas. The ecological restoration projects promoted vegetation restoration in the western part of Hunan Province. This study could help us better understand the spatiotemporal variations of vegetation and their responses to climate change and human activities, which would offer scientific basis for effective ecological restoration policy.

Ecological risk assessment and influencing factors in the Wuhan Metropolitan Area based on supply and demand bundles of ecosystem services

In the context of rapid urbanization, metropolitan areas are facing the risk of supply-demand mismatches among ecosystem services. Investigating the patterns, relationships, and driving factors of multiple supply-demand risks is of great significance to support the efficient management of regional ecological risks. We quantified the single/comprehensive supply-demand risk rates of six ecosystem services in Wuhan Metropolitan Area at the township scale in 2000, 2010, and 2020. By applying the self-organizing feature map network and optimal parameter geo-detector, we identified supply-demand risks bundles of ecosystem services and influencing factors of comprehensive risks. The results showed significant spatial variations in the supply-demand risks of typical ecosystem services from 2000 to 2020. The supply-demand risk associated with grain production, water yield, carbon sequestration, and green space recreation increased, while soil conservation and water purification risks decreased. The comprehensive ecosystem services supply-demand risk increased from 0.41 to 0.45, indicating a 'core area increase and periphery decrease' trend. Throughout the study period, the area exhibited bundles of comprehensive extremely high-risk bundles (B1), comprehensive high-risk bundles (B2), water purification high-risk bundles (B3), and grain production-soil conservation risk bundles (B4). The transition of risk types from B3 to B2 and from B2 to B1 suggested an increase in the combination and intensity of supply-demand risk. Vegetation cover, nighttime light index, and population density were the main driving factors for spatial variations in comprehensive supply-demand risk. Ecologi-cal risk assessment based on ecosystem services supply-demand bundles could provide an effective and reliable way to regulate multiple regional risk issues.

VIIRS Night-Time Light Image in the Spatial Structure Identification of Banyuwangi Regency

A spatial structure is a hierarchical arrangement of settlement centers and a network system of infrastructure and facilities that support the community’s socio-economic activities. Spatial structure is important because the city’s rapid growth requires regional service center planning. In general, the method for determining the spatial structure uses the Centrality Index (IS) with a variable number of facilities, infrastructure, and people to obtain a hierarchical arrangement of regional service centers. However, this method is often constrained by data limitations, so in this study remote sensing technology was used through night light imagery in identifying potential centers. This study used the Visible and Infrared Imaging Radiometer Suite (VIIRS) sensor, one of the sensors carried by the Suomi-NPP (National Polar-orbiting Operational Environmental Satellite System Preparatory Project) satellite. Identification is done by overlaying the night light image with the existing spatial structure to produce potential centers. Based on the analysis results, eight classifications are based on the light emission interval class, where the higher the class, the higher the light emission value, which indicates the potential for a new center. The Pesanggaran sub-district is in class eight, the Tegaldimo sub-district is in class six, and the Banyuwangi sub-district is in class one.

Temporal-spatial evolution of the integrated landscape patterns in the Zhengzhou Metropolitan Area based on nighttime light data

Metropolitan integration development refers to the interconnection between cities and the coordinated development of various aspects such as economy, society, culture, and nature, which is the goal of metropolitan area development. With the Zhengzhou metropolitan area as the research area and based on nighttime light data from 2012 to 2021, we comprehensively used landscape index and landscape morphology spatial pattern analysis methods, systematically evaluated the integration process of the metropolitan area from the perspective of spatial expansion and spatial connection, analyzed the spatiotemporal variations of the landscape pattern of the metropolitan area, and revealed the spatiotemporal expansion and connection patterns of the metropolitan area. The results showed that the area of the Zhengzhou metropolitan area expanded year by year from 2012 to 2021, with the number of landscape patches continuously increasing, showing an agglomeration phenomenon. In the metropolitan area, there was a trend towards stability and multi-directional coordinated growth. The contribution of non-central cities to expansion increased annually, while the expansion patterns of various constituent cities gradually shifted from internal filling to external expansion. The connection scale within the metropolitan area had been expanding annually, with enhanced intercity connections. Intercity connection belts and channels for material and information exchange were emerging, and the integrated network of urban agglomeration connections was gradually forming. Metropolitan integration planning provided positive guidance for the development of metropolitan areas. We should fully leverage the driving effects of metropolitan areas, pay attention to the integration of Zhengzhou-Kaifeng and Zhengzhou-Xuchang, promote the formation of emerging growth poles in Xinxiang and Jiaozuo, as well as regional coordinated development, strengthen the network of policies, economy, transportation, information, etc., and form a diversified and integrated development situation.

Postnatal nighttime light exposure and infant temperament at age 12 months: mediating role of genus Akkermansia.

The gut microbiome has been reported to be associated with nighttime light (NTL) exposure and temperament. However, the specific role of infant gut microbiome plays in NTL exposure and temperament is unclear. This study investigated the potential mediating role of infants' gut microbiome in correlations between NTL exposure and temperament. Demographic information, stool samples, and temperament scores were collected from 40 infants. Temperament was evaluated using the Infants Behavior Questionnaire-Revised (IBQ-R). The gut microbiota was analyzed using 16S rRNA sequencing. Cumulative and lagged effects of NTL exposure were calculated based on residential address (NTLpoint) and a concentric 1km radius buffer zone around the address (NTL1000m), respectively. Mediation models were utilized for assessing the mediating effects of the gut microbiome. The gut microbiome of infants with higher fear scores was characterized by a higher abundance of Akkermansia and Clostridium_sensu_stricto_1 and a lower abundance of Bacteroides. Mediation models indicated Akkermansia played a full mediating role in associations between NTLpoint, NTL1000m and fear in specific time periods. Genus Akkermansia explained 24.46% and 33.50% of associations between fear and cumulative exposure to NTLpoint and NTL1000m, respectively. This study provides evidence for the mediating role of Akkermansia between NTL exposure and fear. However, further experimental is required to elucidate the mechanisms through which the gut microbiome mediates between NTL exposure and temperament in infants.

Spatiotemporal Coupling between Urbanization and Eco-environment based on Google Earth Engine and Nighttime Light Data: A Case Study of the Yellow River Delta

Based on the cloud platform of Google Earth Engine and ArcGIS, this paper uses daytime optical remote sensing and nighttime light (NTL) remote sensing data to extract the comprehensive nighttime light index (CNLI) and remote sensing ecological index (RSEI) respectively, then constructed and quantitatively analyzed the coupling coordination distance model of urbanization and eco-environmental quality in the study area from 2001 to 2020. The results show that the average value of RSEI fluctuated from 0.446 in 2001 to 0.518 in 2020 from 2001 to 2020. The average value of CNLI from 2001 to 2020 continued to rise from 0.101 in 2001 to 0.238 in 2020. From 2001 to 2020, the average value of the coupling coordination degree showed a downward trend, from 1.356 in 2001 to 1.256 in 2020, and the coupling coordination degree gradually developed in a good direction. The research results can provide data support and theoretical suggestions for promoting the sustainable development of the Yellow River Delta.

A spatiotemporal comprehensive constraint interpolation method for missing values in monthly composite Suomi-NPP VIIRS Cloud Mask images

ABSTRACT The Earth’s nighttime light imaging data collected by the Visible Infrared Imaging Radiometer Suite Day/Night Band (VIIRS DNB) are severely lost in summer, hindering the further application of VIIRS DNB data. In this article, a spatiotemporal comprehensive constraint interpolation method (SCIM) that accounts for the relative stationarity of temporal nighttime light, spatial correlations and heterogeneity among pixels is designed. Meanwhile, the method considers the seasonal fluctuations in nighttime light radiation, introduces spatial autocorrelation coefficients and spatial heterogeneity coefficients, and solves the problem of missing VIIRS DNB data. First, 8 widely used temporal interpolation methods (TIM) are selected to perform temporal interpolation for missing image data. Second, the existing VIIRS monthly images are used to construct the constraint relationship between time and space, and the pixels that do not meet the constraint conditions in the TIM simulation image are removed. The inverse distance weight (IDW) method is applied to the removed null pixels to obtain the image simulated by the SCIM. Finally, the accuracy of the TIM and SCIM is evaluated. The results show that the accuracy of the SCIM-simulated image is better than that of the TIM-simulated image. The proposed approach improves the quality of the VIIRS DNB dataset.

Mobility-oriented measurements of people's exposure to outdoor artificial light at night (ALAN) and the uncertain geographic context problem (UGCoP).

Advanced nighttime light (NTL) remote sensing techniques enable the large-scope epidemiological investigations of people's exposure to outdoor artificial light at night (ALAN) and its health effects. However, multiple uncertainties remain in the measurements of people's exposure to outdoor ALAN, including the representations of outdoor ALAN, the contextual settings of exposure measurements, and measurement approaches. Non-exposed but included outdoor ALAN and causally irrelevant outdoor ALAN may manifest as contextual errors, and these uncertain contextual errors may lead to biased measurements and erroneous interpretations when modeling people's health outcomes. In this study, we systematically investigated outdoor ALAN exposure measurements in different geographic contexts using either residence-based or mobility-oriented measurements, different spatial scales, and multiple NTL data sources. Based on the GPS data collected from 208 participants in Hong Kong, outdoor ALAN exposures were measured from NTL imagery at 10 m, 130 m, and 500 m spatial resolutions using in-situ methods or 100 m, 300 m, and 500 m buffer zone averaging. Descriptive analysis, multiple t-tests, and logistic regression were employed to examine the differences between outdoor ALAN exposure measurements using various contextual settings and their effects on modeling people's overall health. Our results confirmed that different contextual settings may lead to significantly different outdoor ALAN exposure measurements. Our results also confirmed that contextual errors may lead to erroneous conclusions when using improper contextual settings to model people's overall health. Consequentially, we suggest measuring people's exposure to outdoor ALAN using the mobility-oriented approach, NTL representation with the high spatial resolution, and a very small buffer zone as a contextual unit to derive outdoor ALAN exposure. This study articulates essential methodological issues induced by uncertainties in outdoor ALAN exposure measurements and can provide essential implications and suggestions for a broad scope of studies that need accurate outdoor ALAN exposure measurements.

Characterizing urban actively populated area growth in the Yangtze River Delta using nighttime light data

Understanding the spatiotemporal evolution of urban actively populated area (UAPA) in urban agglomerations is crucial for sustainable development. This study fills a gap in previous research that overlooked urban population dynamics, leading to inaccurate UAPA characterizations. We utilized nighttime light (NTL) data, which reflects both urban construction status and population dynamics, to characterize UAPA growth pattern in the Yangtze River Delta. We developed a novel search method for extracting UAPA that demonstrated greater accuracy and adaptability than traditional threshold method. We employed expansion rate (ER) and annual growth rate (AGR), and a method for recognizing urban expansion patterns, to illustrate the characteristics and spatial changes in the Yangtze River Delta. We divided the research stage (1992–2019) into six stages based on China’s Five-Year Plan. The results demonstrated that the UAPA size at the end of the sixth stage (2016–2019) was about 6 times greater than that at the end of the first stage (1992–1995). The ER and AGR were approximately 15% during the first stage (1992–1995), and dropped dramatically following the fourth stage (2006–2010). Edge expansion is a typical predominant spatial expansion pattern for UAPA. While the proportion of patches with newly generated expansion showed an initial upward and then a downward trend, the percentage of patches with infilling expansion exhibited a pattern of first decreasing and then increasing. This study aims to provide support for future development planning and policy formulation.

Dim light at night shifts microglia to a pro-inflammatory state after cerebral ischemia, altering stroke outcome in mice

Circadian rhythms are endogenous biological cycles that regulate physiology and behavior and are set to precisely 24-h by light exposure. Light at night (LAN) dysregulates physiology and function including immune response; a critical component that contributes to stroke pathophysiological progression of neuronal injury and may impair recovery from injury. The goal of this study is to explore the effects of dim LAN (dLAN) in a murine model of ischemic stroke to assess how nighttime lighting from hospital settings can affect stroke outcome. Further, this study sought to identify mechanisms underlying pathophysiological changes to immune response after circadian disruption. Male and female adult Swiss Webster (CFW) mice were subjected to transient or permanent focal cerebral ischemia, then were subsequently placed into either dark night conditions (LD) or one night of dLAN (5 lx). 24 h post-stroke, sensorimotor impairments and infarct sizes were quantified. A single night of dLAN following MCAO increased infarct size and sensorimotor deficits across both sexes and reduced survival in males after 24 h. Flow cytometry was performed to assess microglial phenotypes after MCAO, and revealed that dLAN altered the percentage of microglia that express pro-inflammatory markers (MHC II+ and IL-6) and microglia that express CD206 and IL-10 that likely contributed to poor ischemic outcomes. Following these results, microglia were reduced in the brain using Plexxikon 5622 (PLX 5622) a CSFR1 inhibitor, then the mice received an MCAO and were exposed to LD or dLAN conditions for 24 h. Microglial depletion by PLX5622 resulted in infarct sizes that were comparable between lighting conditions. This study provides supporting evidence that environmental lighting exacerbates ischemic injury and post-stroke mortality by a biological mechanism that exposure to dLAN causes a fundamental shift of activated microglial phenotypes from beneficial to detrimental at an early time point after stroke, resulting in irreversible neuronal death.

Towards sustainability: The spatiotemporal patterns and influence mechanism of urban sprawl intensity in the Yangtze River Delta urban agglomeration

Urban sprawl has been a great challenge to current sustainable development goals, especially in urban agglomeration areas. Based on the nighttime light and WorldPop population grid data from 2011 to 2020, this study established a multidimensional evaluation system of urban sprawl intensity in the Yangtze River Delta Urban Agglomeration (YRDUA) with a "scale-density-structure" framework. The Dagum Gini coefficient, spatial Markov chain, and econometric analysis were applied to analyze urban sprawl intensity. The results suggested that: (1) the urban sprawl intensities of the cities in the YRDUA became more imbalanced over the decade; (2) the mild sprawl cities were dominant in the study area, but the moderate and severe sprawl cities were mostly located in the west and the only inland province--Anhui; (3) the spatial distribution of urban sprawl intensity indicated "club convergence" and strong spatial correlation effect, but poor stability; and (4) land resource mismatch and population urbanization were the main explanatory variables for urban sprawl intensity, with large cities more sensitive to the former, and medium cities to the latter. These findings can provide important policy implications to sustainable development and compact growth.

Does urban sprawl lead to carbon emission growth? ——Empirical evidence based on the perspective of local land transfer in China

The rapid expansion of urban areas has caused a series of impacts on the ecological environment. Exploring the effects of urban sprawl on carbon emissions and its mechanisms is crucial for realizing sustainable development patterns and low-carbon city planning. This study integrates used nighttime light data and population grid data to construct an Urban Sprawl Index. It employs a spatial Durbin model to analyze the spatial impact of urban sprawl on carbon emissions, examining the mechanisms from the perspective of local land transfers. The research reveals the following findings: (1) Urban sprawl significantly contributes to a positive spatial spillover effect on carbon emissions, leading to increased emission levels in surrounding cities. (2) The overall local impact of urban sprawl on urban areas is not significant. It suppresses carbon emissions in municipal districts but increases them in counties, with these effects counterbalancing each other. (3) Mechanism analysis indicates that different types of land transfers have varied effects on carbon emissions in districts and counties, and the regulation of the floor area ratio regulation strengthens the impact of land transfers. (4) Heterogeneity analysis shows that the size of urban populations significantly reduces the local effects of sprawl on carbon emissions, while environmental regulations strength attenuate the impact of urban sprawl on carbon emissions in municipal districts. The findings of this study provide practical references and policy implications for formulating reasonable urban development and land use plans, facilitating the achievement of peak carbon and carbon neutrality goals.

TOWARD SPATIAL AND SOCIAL EQUALITY: INVESTIGATING AND MODELING THE LINK BETWEEN OPPORTUNITY AND URBAN INTENSITY IN METRO MANILA, PHILIPPINES

Abstract. This paper employed an interdisciplinary mixed-method approach combining geomatics, geography, and sociology to investigate the link between opportunity and urban intensity across the 32 legislative districts of Metro Manila. Eighteen opportunity indicators, weighted within their opportunity dimensions using Analytic Hierarchy Process (AHP) were used to output Opportunity Scores, while annual VIIRS DNB nighttime light was used as proxy data to approximate the region’s Urban Intensity Score. Two regression models were developed with different sets of explanatory variables: an AHP-based model that used the top two weighted indicators from each opportunity dimension, and a model that used Exploratory Regression Analysis (ERA) to determine the appropriate variable combination. Modeling via Ordinary Least Squares Regression (OLS) produced a properly specified global model, while OLS modeling of the ERA-based model exhibited non-stationarity between variables which necessitated performing Geographically Weighted Regression (GWR). Our quantitative findings, combined with insights from our mini focus group discussion (FGD), provided evidence of the unequal distribution of opportunities across Metro Manila and the spatially varying relationship between urban intensity and opportunity, which have significant implications for urban framework as well as housing choice.