Spatial Regression Models Research Articles

Spatiotemporal Evolution and Influencing Factors of Urban Industry in Modern China (1840–1949): A Case Study of Nanjing

In modern China, industrialization has formed a critical foundation for the transition to modernization. However, the spatiotemporal evolution patterns and driving mechanisms of urban industrial development in Nanjing from 1840 to 1949 remain unclear. Based on textual historical sources, this study examined the spatiotemporal patterns of urban industrial development in Nanjing from 1840 to 1949 by using spatial analysis methods, GeoDetector, regression models and industrial structure indices. The results reveal the following: (1) The overall spatial distribution pattern of the industry in modern Nanjing exhibited a “one main, one secondary” dual-center “ladle-shaped” arrangement. Over time, industry has expanded from the urban center toward the east and north. (2) The modernization level of different industries was uneven, exhibiting a “center-periphery” spatial pattern. (3) At the micro level, transportation and population density were the primary influencing factors for industrial location, whereas at the macro level, government intervention mainly affected the industrialization pattern. (4) The industrial development pattern in modern Nanjing, in alignment with the “pole-axis” spatial system, serves as a microcosm of China’s urban modernization transition. This study represents the application of GIS methods in the humanities and provides valuable insights for urban planning and development.

Factors affecting the demographic situation in the regions of the Far Eastern Federal District

Subject. The article considers the multifactorial regression modeling and evaluation of indicators affecting the improvement of demographic situation in the regions of the Far Eastern Federal District. Objectives. The aim is to identify factors influencing the population growth in the regions of the Far Eastern Federal District. Methods. The study employs theoretical, descriptive, comparative, and statistical methods, demographic analysis, and multivariate regression modeling. Results. We found that the demographic situation in the Central, Northwestern, Southern, North Caucasian, and Ural Federal Districts is generally stabilizing. The situation is extremely negative in the Volga, Siberian, and Far Eastern federal districts. The spatial regression modeling identified 13 out of 47 factors that affect the population growth of the regions of the Far Eastern Federal District. The development of small business contributes to solving socio-economic problems and has a positive impact on living standards. The number of single-industry towns located in the said Federal District has a positive effect on population growth. Conclusions. There is a decrease in the population in the regions of the Far Eastern Federal District. The development of small and medium-sized businesses is good for demographic situation. This is confirmed by positive correlation of the number of small and medium-sized enterprises and their average number with the population of the region. The number of single-industry towns has a positive effect on population growth. Mining and processing enterprises have a negative environmental impact, however, they are not negative indicators that affect the deterioration of the demographic situation.

Spillover impacts of financial development and globalization on environmental quality in ASEAN countries

In the globalization era, the economic policy of a specific country might be influenced by the development of neighboring countries. Thus, this study aims to probe the direct and spillover effects of financial development, economic growth, and globalization on environmental sustainability in ASEAN countries during the period of 1992-2021. By applying three spatial regression models, the results are summarized: (1) There are positive spillover effects of financial development in neighboring countries on ecological footprint in a particular country; (2) Economic growth has a positive impact on ecological deficits in both the host country and neighboring countries in the short-run; (3) The expansion of globalization in neighboring countries has a negative spillover effect on the ecological footprint in a particular country and vice versa. Based on these findings, the study recommends that when a country formulates its economic policies, it is necessary to calculate the impact of that policy on neighboring countries and vice versa. Encouraging economic growth and expanding the money supply ought to go hand in hand with fostering greater integration. This integration is essential to counterbalance the potential adverse effects of these macroeconomic variables on environmental quality and ecological balance.

Exploring farmland price determinants in Northern Italy using a spatial regression analysis

Using spatial regression models, we detect determinants of farmland’s prices in a rural area located in the upper Treviso plain (Veneto region, Italy). Econometric analysis is based on a Spatial linear regression model able to account for spatial lags in the data. Estimates show which intrinsic and extrinsic characteristics have the greatest influence on price, and how buyers and sellers’ profiles also matter on the price determination. Our application fosters spatial regression models in rural real estate market analysis and appraisal, and highlights that in the area under study the farmland’s prices are significantly affected by factors that are rarely considered in the literature, such as sellers and buyers’ profiles, the land use in the context where the sold plot is located matters, the hydraulic risk of the area and the presence of large infrastructures.

Unveiling the Impact of Urbanization on Net Primary Productivity: Insights from the Yangtze River Delta Urban Agglomeration

Urbanization has significantly altered the carbon cycle of the terrestrial environment, particularly in relation to net primary productivity (NPP). Gaining a more comprehensive comprehension of how NPP is affected by urbanization is crucial for obtaining fresh perspectives on sustainable urban landscape design and decision making. While there is a significant body of research examining the geographical and temporal patterns of NPP supply capacity, there are only a few studies that have investigated the spatial relationships between NPP and urbanization, particularly at the grid scale. This research investigated the temporal and geographical features and patterns of NPP and their impact mechanisms. In order to estimate NPP and the level of urbanization in the Yangtze River Delta Urban Agglomeration (YRDUA), we used a combination of different models and datasets. To evaluate the geographical correlations and dependence between NPP and urbanization, we utilized local bivariate autocorrelation methods and spatial regression models to describe and visualize these relationships. The findings revealed that there was a consistent negative relationship between NPP and urbanization on a global scale from 1990 to 2020. However, when examining the local scale, the geographical correlations could be classified into four distinct categories: areas with both low NPP and low urbanization, areas with high NPP and high urbanization, areas with low NPP and high urbanization, and areas with high NPP and low urbanization. Our analysis showed that spatial regression models are more suitable for quantifying the spatial relationship between NPP and urbanization due to their ability to include the impacts of spatial Moran’s I techniques. Due to the growing urbanization, the highest NPP value was recorded in 2005, followed by 2000, 2020, and 2010. Conversely, the smallest association was observed in 2015. Examining the geographical connection between NPP and urbanization offers theoretical and practical insights for urban planning that prioritizes human needs and promotes sustainable development. It also aids in the development of reasonable methods for organizing ecological functional systems.

Impact of transport superiority on ecosystem health in arid regions: A case study of southern Xinjiang, China

The increasingly improved transportation infrastructure network is the prerequisite and foundation for driving regional development. Scientifically investigating the mechanisms by which the transport superiority degree (TSD) in arid regions affects the health of ecosystems holds significant practical value and importance for ecosystem protection and regional sustainable development. This study investigates the impact of the TSD on the Ecosystem Health Levels (EHL) in Southern Xinjiang, China, an area characterized by its arid climate and ecological vulnerability. Arid regions, such as Southern Xinjiang, often face unique ecological challenges, including water scarcity, soil erosion, and biodiversity loss, which can be exacerbated by human activities including transportation infrastructure development. By leveraging multi-source remote sensing data, this research aims to uncover the intricate dynamics between TSD and EHL in such a fragile ecological context. The findings of this study are critical for understanding the balance between infrastructural development and environmental conservation in arid regions and provide valuable insights for sustainable planning and policy-making in similar ecological settings. The research results show the following: (1) Over the study period, county-scale TSD in southern Xinjiang generally increased, while the conditions of EHL deteriorated, with higher EHL in the eastern counties than in the western counties. (2) Bivariate spatial autocorrelation analysis reveals a significant spatial dependency between county-scale TSD and EHL, with primary relationship types being LH (Low TSD − High EHL) and HL (High TSD − Low EHL). (3) Spatial regression model results indicate that an increase in TSD leads to the deterioration of county-scale EHL, and during the study period, it was observed that the interference of TSD on EHL gradually weakened.

Spatial Patterns of Drone Adoption: Insights from Communities in Southern California

Most US cities have seen a rapid proliferation of Civilian drones in the past decade. This study applies spatial regression models to identify the potential spatial clustering of drone adoption and the effects of socioeconomic, Information and communication technology (ICT) infrastructure, and land use characteristics, with a focus on the association between drone adoption and high-speed broadband coverage (which provides access to information and infrastructure for drone technology). Drone adoption exhibited a significant spatial clustering pattern, indicating spatial interaction in innovation adoption. The age, income, occupation, and land use characteristics significantly explained the drone adoption rate, while populations of color and communities with lower broadband coverage had lower drone adoption rates, indicating racial and ethnic disparities in innovation technology adoption. The underlying digital divide amplifies such disparities and requires the development of policy interventions to reduce inequality.

Inequality in environmental risk exposure and procedural justice in the Matanza-Riachuelo River Basin

ABSTRACT Using a capabilities-based perspective on socioenvironmental justice, this paper aims to contribute to the understanding of the interconnection between social equity and care for the earth in the case of the Matanza-Riachuelo – the most industrialized and contaminated river basin in Argentina. First, it uses a spatial regression model to analyse the correlation between multidimensional poverty and potential exposure to three types of environmental risks: environmental hazards of productive establishments, open-air waste dumps, and proximity to contaminated rivers and streams. Second, it analyses inequities in the processes of resettlement of households living in conditions of extreme environmental risk in informal settlements. It shows that higher poverty areas face greater risk of exposure to open-air waste dumps but lower risk of exposure to industrial and commercial environmental hazards. It also demonstrates how enhanced participation of households in decision-making on resettlement contributed to improved outcomes.

Interpretability and spatial efficacy of a deep-learning-based on-site early warning framework using explainable artificial intelligence and geographically weighted random forests

Earthquakes pose significant risks globally, necessitating effective seismic risk mitigation strategies like earthquake early warning (EEW) systems. However, developing and optimizing such systems requires thoroughly understanding their internal procedures and coverage limitations. This study examines a deep-learning-based on-site EEW framework known as ROSERS (Real-time On-Site Estimation of Response Spectra) proposed by the authors, which constructs response spectra from early recorded ground motion waveforms at a target site. This study has three primary goals: (1) evaluating the effectiveness and applicability of ROSERS to subduction seismic sources; (2) providing a detailed interpretation of the trained deep neural network (DNN) and surrogate latent variables (LVs) implemented in ROSERS; and (3) analyzing the spatial efficacy of the framework to assess the coverage area of on-site EEW stations. ROSERS is retrained and tested on a dataset of around 11,000 unprocessed Japanese subduction ground motions. Goodness-of-fit testing shows that the ROSERS framework achieves good performance on this database, especially given the peculiarities of the subduction seismic environment. The trained DNN and LVs are then interpreted using game theory-based Shapley additive explanations to establish cause-effect relationships. Finally, the study explores the coverage area of ROSERS by training a novel spatial regression model that estimates the LVs using geographically weighted random forest and determining the radius of similarity. The results indicate that on-site predictions can be considered reliable within a 2–9 km radius, varying based on the magnitude and distance from the earthquake source. This information can assist end-users in strategically placing sensors, minimizing blind spots, and reducing errors from regional extrapolation.

The rich-poor divide: Unravelling the spatial complexities and determinants of wealth inequality in India

This study presents the first in-depth analysis of spatial differences and factors influencing wealth distribution among households in India. It uses data from the latest National Family Health Survey, covering 707 districts. Techniques like the Lorenz curve, Gini coefficient, Location Quotient, Morans statistics, and Univariate and Bivariate LISA methods explore inequalities, concentration, and clustering patterns of rich-poor households at the district level. Additionally, spatial regression models such as OLS, GWR, and MGWR help to uncover spatial disparities and variability. Our findings demonstrate significant regional disparities, with the affluent household concentration being notably higher in north-western and southern India, while central, eastern, and northeastern regions exhibit greater inequality. Key factors impacting wealth inequality include rurality, low female literacy rates, educational level of household heads and prevalence of Scheduled Castes/Tribes. This study highlights the spatial dimensions of wealth inequality and provides a nuanced understanding of the factors contributing to these patterns. The GWR and MGWR models prove most effective, explaining more than 90% of the variation in wealth distribution factors. This study sheds light on the spatial dynamics and factors behind wealth disparities in India, offering strategic insights for equitable growth initiatives targeting diverse socio-economic sectors.

Spatiotemporal assessment of the nexus between urban sprawl and land surface temperature as microclimatic effect: implications for urban planning.

Rapid urbanisation has led to significant environmental and climatic changes worldwide, especially in urban heat islands where increased land surface temperature (LST) poses a major challenge to sustainable urban living. In the city of Abha in southwestern Saudi Arabia, a region experiencing rapid urban growth, the impact of such expansion on LST and the resulting microclimatic changes are still poorly understood. This study aims to explore the dynamics of urban sprawl and its direct impact on LST to provide important insights for urban planning and climate change mitigation strategies. Using the random forest (RF) algorithm optimised for land use and land cover (LULC) mapping, LULC models were derived that had an overall accuracy of 87.70%, 86.27% and 93.53% for 1990, 2000 and 2020, respectively. The mono-window algorithm facilitated the derivation of LST, while Markovian transition matrices and spatial linear regression models assessed LULC dynamics and LST trends. Notably, built-up areas grew from 69.40 km2 in 1990 to 338.74 km2 in 2020, while LST in urban areas showed a pronounced warming trend, with temperatures increasing from an average of 43.71°C in 1990 to 50.46°C in 2020. Six landscape fragmentation indices were then calculated for urban areas over three decades. The results show that the Largest Patch Index (LPI) increases from 22.78 in 1990 to 65.24 in 2020, and the number of patches (NP) escalates from 2,531 in 1990 to an impressive 10,710 in 2020. Further regression analyses highlighted the morphological changes in the cities and attributed almost 97% of the LST variability to these urban patch dynamics. In addition, water bodies showed a cooling trend with a temperature decrease from 33.76°C in 2000 to 29.69°C in 2020, suggesting an anthropogenic influence. The conclusion emphasises the urgent need for sustainable urban planning to counteract the warming trends associated with urban sprawl and promote climate resilience.

Spatial analysis of American cutaneous leishmaniasis in the state of Amazonas.

To evaluate, using spatial analysis, the occurrence of American Cutaneous Leishmaniasis (ACL) and analyze its association with the municipal human development index (MHDI) and deforestation in the state of Amazonas, Brazil, from 2016 to 2020. This ecological study, carried out from January 2016 to December 2020, included the 62 municipalities of the state of Amazonas. The incidence rate of ACL was determined in space and time. Using Multiple Linear Regression by Ordinary Least Squares (OLS) and Spatial Autoregressive Regression (SAR) models, the relationship between incidence rates and Human Development Index (HDI) and deforestation was analyzed., The high- and low-risk clusters were identified by employing the Getis-Ord Gi* statistic. A total of 7,499 cases of ACL were registered in all 62 municipalities in the state. Most cases were in male (n=5,924; 79.24%), with the greatest frequency in the population aged from 20 to 39 years (n=3,356; 44.7%). The incidence rate in the state of Amazonas was 7.34 cases per 100,000 inhabitants-year, with the municipalities of Rio Preto da Eva and Presidente Figueiredo showing the highest rates (1,377.5 and 817.5 cases per 100,000 population-year, respectively). The ACL cases were clustered into specific areas related to those municipalities with the highest incidence rates. The SAR model revealed a positive relationship between ACL and deforestation. The occurrence of ACL was evident in a variety of patterns in the state of Amazonas; the high incidence rates and persistence of this disease in this state were linked to deforestation. The temporal distribution showed variations in the incidence rates during each year. Our results can help optimize the measures needed to prevent and control this disease in the state.

Identifying local foci of tuberculosis transmission in Moldova using a spatial multinomial logistic regression model

BackgroundMultidrug resistant tuberculosis (MDR-TB) represents a major public health concern in the Republic of Moldova, with an estimated 31% of new and 56% of previously treated TB cases having MDR disease in 2022. A recent genomic epidemiology study of incident TB occurring in 2018 and 2019 found that 92% of MDR-TB was the result of transmission. The MDR phenotype was concentrated among two M. tuberculosis (Mtb) lineages: L2.2.1 (Beijing) and L4.2.1 (Ural). MethodsWe developed and applied a hierarchical Bayesian multinominal logistic regression model to Mtb genomic, spatial, and epidemiological data collected from all individuals with diagnosed TB in Moldova in 2018 and 2019 to identify locations in which specific Mtb strains are being transmitted. We then used a logistic regression model to estimate locality-level factors associated with local transmission. FindingsWe found differences in the spatial distribution and degree of local concentration of disease due to specific strains of Beijing and Ural lineage Mtb. Foci of transmission for four strains of Beijing lineage Mtb, predominantly of the MDR-TB phenotype, were located in several regions, but largely concentrated in Transnistria. In contrast, transmission of Ural lineage Mtb had less marked patterns of spatial aggregation, with a single strain (also of the MDR phenotype) spatially clustered in southern Transnistria. We found a 30% (95% credible interval 2%-80%) increase in odds of a locality being a transmission cluster for each increase of 100 persons per square kilometer, while higher local tuberculosis incidence and poverty were not associated with a locality being a transmission focus. InterpretationOur results identified localities where specific Mtb transmission networks were concentrated and quantified the association between locality-level factors and focal transmission. This analysis revealed Transnistria as the primary area where specific Mtb strains (predominantly of the MDR-TB phenotype) were locally transmitted and suggests that targeted intensified case finding in this region may be an attractive policy option.

Regional comparison of socio-demographic variation in urban E-scooter usage

In recent years we have witnessed explosive growth in the shared, free-floating, electric scooter industry. While still controversial in many North American cities, a number of large e-scooter operators have managed to carve out a piece of the urban transportation landscape. As these vehicles shift from novelty services to increasingly reliable modes of short personal travel, the discussion has turned to investigating who exactly benefits from these micromobility services and who are being left behind. Though population surveys have been administered to identify the socio-demographic characteristics of e-scooter riders in the past, little work has linked these characteristics through trips, or investigated the regional variation in these demographic factors. In this work we explore the variability and similarities in e-scooter rider characteristics across three major U.S. cities. To accomplish this, we apply a Moran’s Eigenvector Spatial Filtering linear regression model and compare our results to more commonly used spatial regression approaches. Our results indicate that the spatial filtering approach outperforms other methods in identifying socio-demographic characteristics of e-scooter users, across multiple regions. We find that many socio-demographics associated with e-scooter usage are regionally variant, despite younger users making up the core user base in all cities. There are variations in usage based on gender, income, and race across cities with Black and Hispanic populations remaining underserved. The implications of these findings are discussed.

Tree abundance, species richness, or species mix? Exploring the relationship between features of urban street trees and pedestrian volume in Jinan, China

Accumulating evidence has confirmed that urban greenery, especially street trees, is beneficial to walking behaviors. Existing street greenery exposure-walking behavior studies have focused on the quantity of greenery, which is often measured by normalized difference vegetation index (NDVI) or green view index (GVI). However, some important qualities of street trees, the dominant component of urban greenery, were often overlooked, due to the labor-intensive and expensive nature of on-site surveys. To address this issue, we proposed a cutting-edge deep learning technique to identify street tree species at the individual tree level. We established a citywide dataset of all street trees with species information via 185,831 Baidu Street View images (BSV) in Jinan, China. Population-level walking intensity, measured by pedestrian volume, was retrieved from BSV images using Baidu AI. We further adopted spatial regression models to investigate the association between pedestrian volume and street tree characteristics, including street tree abundance (number of street trees), species richness (number of unique tree species) and species mix (the degree of diversity of tree species). The built environment and urban greenery covariates were adjusted in the models. The results indicate that the street tree abundance and species mix are positively associated with pedestrian volume. Species richness is not associated with it. Besides, spatial mismatch is identified between abundance and species mix of street trees in the study area. Hence, to facilitate walking behavior and deliver related health benefits, it is necessary to develop fine-grained measures of street greenery features.

Spatial Regression Modeling of Child Survival on the Distribution of Births and Deaths in Kenya Based on the Kenya Demographic and Health Survey (KDHS) 2022

This study used spatial mapping techniques to examine the distribution of births and deaths in Kenya and their relationship with various factors related to child survival, such as maternal age, education, wealth, and access to health services. Data were obtained from the 2022 Kenya Demographic and Health Survey (KDHS). Spatial autocorrelation analyses were conducted to identify clusters of high or low child mortality rates. The results showed significant spatial autocorrelation in child mortality rates, indicating that neighboring areas had similar mortality rates. Factors such as maternal education, wealth, and access to health services were found to be significantly associated with child mortality rates. These findings can inform targeted interventions and policies to reduce child mortality rates in Kenya, particularly in areas with the highest risk of mortality.

Spatio-Temporal analysis of exports of cultural products and their affecting factors for spatial distribution.

Cultural products constitute a significant portion of global trade, and understanding their export patterns can shed light on economic trends, trade dynamics, and market opportunities. This study conducted the spatio-temporal analysis of exports of cultural products, exploring the relationship between various influencing factors and their impact on the spatial distribution of these exports. Leveraging a diverse dataset encompassing 55 BRI countries for the period of 2005-2022, this research employs advanced spatial analysis techniques, including spatial autocorrelation and spatial regression models, to examine the spatial patterns and determinants of exports if cultural product exports. Moreover, this study delves into the multifaceted determinants affecting the spatial distribution of these exports. The findings of this study reveal significant spatio-temporal variations in the exports of cultural products. Spatial autocorrelation analysis indicates the presence of spatial clustering, suggesting that regions with high cultural product exports tend to be geographically close to each other. The spatial regression models further identify several key factors like economic development, productive capacities, cultural tourism, information development and human capital influence the spatial distribution of these exports. The findings of the study reveal that there is strong spatial relationship for exports of cultural products in BRI countries. The findings of this research contribute valuable insights for policymakers, businesses, and stakeholders regarding a deeper comprehension of the driving forces behind the spatial distribution of these cultural products, facilitating informed decision-making processes to optimize strategies for promoting and sustaining the trade of cultural products in an increasingly interconnected world.

Effects of cultural landscape service features in national forest parks on visitors’ sentiments: A nationwide social media-based analysis in China

National Forest Parks (NFPs) represent the highest level of the forest park system in China and have long been considered to cultivate positive experiences among visitors through their rich cultural services. However, there is limited knowledge of the relationship between the cultural dimension of landscape services (CLSs) in NFPs and visitors’ positive experiences. This study represents the first effort to investigate the effects of NFP CLSs on visitors’ expressed sentiments and aims to further identify the relative importance of various CLS features. A total of 267 NFPs across 31 provinces in China were selected as case studies, and approximately 300,000 visitors’ online comments from a widely used travel platform (CTrip) were obtained for sentiment analysis using natural language processing. A novel systematic framework for assessing CLSs was proposed, encompassing five principal features and 19 indicator subcategories. Ordinary least-squares and spatial regression models were used to reveal associations between NFP CLSs and expressed sentiments, whereas the XGBoost model and Shapley Additive exPlanations (SHAP) values were employed to identify the relative importance of NFP CLS features. Three major findings were observed: First, the feature scoring of CLSs in NFPs was unevenly distributed across the country, with scores along the eastern seaboard relatively higher than those inland in the northwest. Second, beneficial associations between the five principal CLS features and visitors’ expressed sentiments were suggested. Third, the influence of landscape aesthetics was particularly prominent in promoting visitors’ positive sentiments, followed by education, physical activities and health, recreation and tourism, and cultural heritage and spiritual practices. These findings provide valuable insights for health-oriented NFP management, policies, and planning.

Integrating traffic pollution dispersion into spatiotemporal NO2 prediction

Accurately predicting ambient NO2 concentrations has great public health importance, as traffic-related air pollution is of major concern in urban areas. In this study, we present a novel approach incorporating traffic contribution to NO2 prediction in a fine-scale spatiotemporal model. We used nationally available traffic estimate dataset in a scalable dispersion model, Research LINE source dispersion model (RLINE). RLINE estimates then served as an additional input for a validated spatiotemporal pollution modeling approach. Our analysis uses measurement data collected by the Multi-Ethnic Study of Atherosclerosis and Air Pollution in the greater Los Angeles area between 2006 and 2009. We predicted road-type-specific annual average daily traffic (AADT) on road segments via national-level spatial regression models with nearest-neighbor Gaussian processes (spNNGP); the spNNGP models were trained based on over half a million point-level traffic volume measurements nationwide. AADT estimates on all highways were combined with meteorological data in RLINE models. We evaluated two strategies to integrate RLINE estimates into spatiotemporal NO2 models: 1) incorporating RLINE estimates as a space-only covariate and, 2) as a spatiotemporal covariate. The results showed that integrating the RLINE estimates as a space-only covariate improved overall cross-validation R2 from 0.83 to 0.84, and root mean squared error (RMSE) from 3.58 to 3.48 ppb. Incorporating the estimates as a spatiotemporal covariate resulted in similar model improvement. The improvement of our spatiotemporal model was more profound in roadside monitors alongside highways, with R2 increasing from 0.56 to 0.66 and RMSE decreasing from 3.52 to 3.11 ppb. The observed improvement indicates that the RLINE estimates enhanced the model's predictive capabilities for roadside NO2 concentration gradients even after considering a comprehensive list of geographic covariates including the distance to roads. Our proposed modeling framework can be generalized to improve high-resolution prediction of NO2 exposure – especially near major roads in the U.S.

Spatial-temporal characteristics and influencing factors of carbon emission in Chengdu-Chongqing area: an urban transportation perspective.

The Chengdu-Chongqing twin-city economic circle is a vital growth pole and a new power source for Chinese high-quality development. Studying the spatial-temporal characteristics of carbon emissions and the role of factors affecting them under the transportation perspective is of great significance for this region to realize the carbon peak and carbon neutrality and to formulate carbon emission reduction policies. We use the exploring spatial data analysis (ESDA) and spatial regression model combined with the STIRPAT model, and research finding: (1) The total carbon emissions in the research area gradually increased from 2014 to 2020, but the growth rate showed a significant decline in 2019. (2) There is significant spatial heterogeneity of carbon emissions in the study area; the hotspot areas of total carbon emissions are in Chongqing and Chengdu, forming a high-low aggregation of carbon emissions. Per capita carbon emissions show a high trend in the southwest and a low in the northeast. (3) From the factors of transportation perspective, highway density and private vehicles have a positive impact on carbon emissions, and urban road areas and public transportation have a very significant inhibition of carbon emissions and a spatial spillover effect. (4) Other factors, such as population size, national economic development, urbanization level, and industrial structure, all have a positive effect on carbon emissions, and disposable income has a negative effect on carbon emissions.