The influence of multidimensional urban form of counties on carbon emissions of residents and its planning implications: Evidence from counties in the Yangtze River Delta, China

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However, as the most basic governmental unit and the new energy consumer in China, counties have rarely been concerned, and the relationship between their urban form and the CER is still limitedly understood.This paper seeks to investigate how urban form influences the CER by taking the 90 counties in the Yangtze River Delta of China as a case study. First, considering the features of the county's urban form and residents' energy consumption, this study focused on analyzing the urban form of the main center and the overall urban form composed of the main center and surrounding towns. Second, based on the needs of sustainable development at county level and the framework of China's territorial spatial planning, this study defined urban form in two dimensions: geometric-aspect urban form and built environment-aspect urban form. Finally, the relationships between urban form and the CER were modelled using partial least squares structural equation model (PLS-SEM). Results reveal that (1) the geometric-aspect urban form variables (scale, compactness, spatial structures, and shape) exert a direct influence on the CER.Controlling the urban development boundary, increasing compactness of themain center, developing in the direction of multiple centers, and minimizing the complexity and narrowness of theshape of the main center positively impact the CER. (2) For the built environment-aspect urban form variables, improving the accessibility of public service facilities and green space coverage is beneficial for counties to reduce the CER, and currently improving road traffic status and energy infrastructurehas a significant contribution to the growth of the CER.(3) The geometric characteristics of urban form indirectly affect the CER by influencing the built environment characteristics of urban form. Road traffic, greening spaces, public service facilities, and energy infrastructureare influential intermediaries. This study identifies the intricate correlation between the county's urban form and the CER, providing a scientific foundation for the formulation of policies aimed at optimizing urban form and achieving “dual carbon” goals. Urban form Carbon emissions of residents County Structural equation model Low carbon cities Figures Figure 1 Figure 2 Figure 3 Introduction The increase in greenhouse gas emissions has given rise to the occurrence of global warming and consequent climate change, leading to a series of serious problems that threaten global natural ecosystems and human development, such as drought, floods, the rise in sea levels, the loss of biodiversity, and economic downturn (Jackson et al. 2017 ). China has experienced a significant acceleration in its industrialization and urbanization processes following the beginning of economic reforms in 1978. However, the swift progress has concurrently led to a significant quantity of carbon emissions. As the top carbon emitter globally today, China has set a target to reduce its GDP per capita carbon emissions by 60–65% by 2030 and has made a commitment to achieve carbon neutral by 2060. Given the present circumstances, it might be argued that China has an enormous challenge in achieving its emission-reduction objectives. Urban areas, the centers of human socioeconomic endeavors, are responsible for more than 70% of the carbon emissions produced by human activities on a global scale. Consequently, they are widely recognized as the primary catalysts for the accelerated pace of climate change (Cai &Zhang 2014 , Pappas et al. 2018 ). In China, this proportion is 85% (Shan et al. 2017 ). Therefore, taking reasonable measures to reduce urban carbon emissions effectively is considered a vital area for implementing climate change adaptation. Researchers and policy-makers are actively exploring measures that can effectively mitigate urban carbon emissions (Ou et al. 2019 ). In recent years, spatial planning for carbon emissions control has gradually been recognized (Seto et al. 2014 , Zhao &Zhang 2018 ). Reducing carbon emissions by changing urban form has become one of the important channels to address climate change. Urban form refers to the arrangement and organization of various urban elements and human activities (Sharifi 2019a ), which can reflect the urban spatial utilization patterns and resource allocation (Wang et al. 2015 ). Once established, it will exert a lasting impact on the functioning of societal, economic, and human conduct (Brian &Haozhi 2016 ) and have a locking effect on urban energy use efficiency and carbon emissions (Sharifi 2019a ). Therefore, it is essential to acquire an in-depth knowledge of the correlation between urban form and carbon emissions in order to shape the sustainable urban form. The issue of carbon reduction in Chinese cities, and the methods by which they can do this through adjustments and optimizations to urban form, has generated significant interest among researchers, urban planners, and decision-makers, given China's status as the largest developing and transitional nation globally. However, previous research has mostly concentrated on megacities and large cities in China, such as municipalities, provincial capital cities, and prefecture-level cities (Ou et al. 2013 , Song et al. 2017 , Wang et al. 2017a , Wang et al. 2017b ). Little research is dedicated to studying the correlation between the county’s urban form and carbon emissions. Counties rank lowest within the Chinese administrative division system, behind provinces and prefecture-level cities. However, as of 2020, the county's population constitutes approximately 73% of the total population, and the GDP accounts for over 60% of the country (Department of Rural Surveys 2019). In recent years, the urban form of the county and the energy consumption of residents have undergone considerable changes as a result of China's promotion of county-based urbanization policies. The average expansion rate of urban built-up areas in counties between 2010 and 2017 was 38.4%, exceeding the national average growth rate of 35.06%. In 2015, the total carbon emissions of counties constituted a proportion above 50% of the national carbon emissions (Wang et al. 2021a ). In 2014, the per capita living gas consumption in the county was 8% higher than that in the city, at 205 m 3 . Focusing on counties for low-carbon research is urgent and necessary. Simultaneously, with the increase in household income and the continuous improvement of lifestyle, the carbon emissions linked to residents' daily activities have progressively emerged as a significant factor in China's overall energy consumption (Qiu 2009 ). In 2021, Chinese residents’ energy consumption constituted approximately 40–50% of the total energy consumption. Moreover, the energy consumption patterns of typical urban families have transitioned from primarily focusing on "eat and wear" to mainly encompassing "residence and travel" (Yin &Shi 2019 ). It is foreseeable that residential buildings and travel-related carbon emissions will also rise rapidly. Therefore, given the significant need to reduce emissions in the current stage, it is imperative to prioritize the urban form of counties and evaluate and analyze the influence of urban form on carbon emissions associated with residents' lives. This work has the potential to offer a more comprehensive perspective on low-carbon spatial planning, thereby aiding China in its achievement of carbon control and emission reduction goals. This study aimed to investigate the influence of the multidimensional urban form of counties on the CER by taking 90 Chinese counties located in the Yangtze River Delta region. To this end, considering the status of the county's urban form and residential energy consumption, we analyzed the urban form of main center and overall urban form. Then, the urban form was defined as geometric-aspect urban form and built environment-aspect urban form. Finally, we adopted a partial least squares structural equation modeling (PLS-SEM) to obtain a more accurate evaluation of the effects of urban form on the CER. This article is expected to complement existing knowledge on the effect of county's urban form on the CER and can be used for low-carbon spatial planning by optimizing urban form to reduce the CER. Literature review Urban form: meanings, scale and factors The term "urban form" usually refers to the spatial structure and form of various urban elements and human activities (Sharifi 2019a , Wolday 2023 ). By synthesizing existing research, it becomes evident that there exist varying interpretations among scholars about the concept of urban form. But overall, urban form can be defined from two perspectives: narrow and broad. The narrow urban form usually refers to the form characteristics displayed by urban physical elements. It primarily emphasizes the attributes of urban physical space, including land use, spatial pattern of urban landscape, streets, communities, buildings, and infrastructure (Shi et al. 2022 ). The broad urban form not only includes the abovementioned physical elements but also involves the forms of nonphysical elements such as society, economy, and population, such as political and social forms, economic and trade forms, and population structure (Clark et al. 2011 , Long &Ye 2019 ). Urban forms that are defined from physical attributes, namely, narrow urban forms, can be further classified into three scale-based categories: micro, meso, and macro (Sharifi 2019a ). The microscale primarily concerns itself with the design and structural features of buildings (i.e., height, orientation, shape coefficient), as well as their position in relation to neighborhood buildings and surrounding open spaces (i.e., density, plot ratio, layout pattern) (Kuhn 2019 , Sharifi 2019c , Wang 2006 ). The meso-scale pertains to the size, spatial structure and layout pattern of communities, blocks, open spaces and streets (i.e., mixed land-use intensity, height-to-width ratio of street) (Sharifi 2019a , Wong et al. 2011 ). Compared to the urban form at the micro- and mesoscale, which pertains to the human scale (Long &Ye 2019 ), macroscale urban form research emphasizes on the overall layout of urban, land use patterns and development modes (Sharifi 2019b ). The land use scale, compactness, spatial structure, layout of public service facilities, road system, green space structure, and infrastructure are all factors of macroscale urban form, reflecting the spatial organization and arrangement of human activities and various urban resources. This study focuses on urban form on a macroscale level. Urban form and carbon emissions of residents The effects of urban form on the carbon emissions of residents are mostly examined through the depiction of various characteristics of urban form (Shi et al. 2022 ). In recent years, macroscale-related research has mainly described urban form from aspects such as urban geometric characteristics and built environment characteristics, which have been proven to be the primary determinants of carbon emissions (Shi et al. 2022 , Zheng et al. 2023 ). Related research can be divided into three categories: (1) how the urban geometric characteristics affect the CER; (2) how the built environment characteristics affect the CER; and (3) how the urban geometric characteristics and the built environment characteristics have impact on the CER together. Meanwhile, as the CER includes travel-related carbon emissions and residential building carbon emissions, the main discussion is around the influence of urban form on these two types of carbon emissions. The urban form variables that reflect geometric characteristics in existing studies include compactness, scale, spatial structure, and urban shape. These various factors could affect energy consumption and relate the carbon emissions of residents through several paths. Compactness urban form means an almost circle-like built-up area, highly dense development and mixed land use (Angel et al. 2010 ). For travel-related carbon emissions, it can increase traffic accessibility and mitigate the reliance on automobile transportation, and decrease the distance of commuting and minimize the duration of automotive trips (Banister 2012 , Chen et al. 2008 , Thinh et al. 2002 ). However, research on large cities has shown the opposite result. Higher compactness may also lead to an increase in traffic volume, traffic jams and travel time, then a rising fuel consumption, and corresponding carbon emissions (Ou et al. 2013 ). The expansion of urban scale is closely related to travel-related carbon emissions, and continuous scale expansion leads to an increase in average commuting distance (Gudipudi et al. 2016 , Mohajeri et al. 2015 ). Furthermore, it has been confirmed by scholars that the spatial structures of single centers and multiple centers have an impact on travel-related carbon emissions. Polycentric urban forms often foster mixed land use and shorter commuting trips, reducing overall travel and traffic congestion (Li et al. 2019 , Veneri 2010 ), and are more conducive to reducing traffic carbon emissions in large cities but have less impact on small cities (Li et al. 2018 ). Urban form complexity can also result in a rise of carbon emissions. The increasingly complex urban form cannot support public transit, which increases the travel distance of passengers and the duration of automobile trips, easily causing traffic congestion and accelerating exhaust emissions (Ou et al. 2013 ). For residential building carbon emissions, previous studies have shown that the heat island effect affecting energy use for cooling is stronger in compact areas (Ewing &Rong 2008 ). This makes residents consume more energy to meet the demands for cooling. An empirical study was carried out on 50 cities in Japan. The findings indicate that compactness is associated with higher per capita residential energy consumption, which may be due to the impact of higher density on sky lighting and natural ventilation (Makido et al. 2012 ). Urban expansion is also considered as an important factor in increasing residential building carbon emissions (Falahatkar &Rezaei 2020 ). The rapid expansion of the urban scale would result in a decrease in vegetation coverage and carbon storage resources, thus contributing to the formation of urban heat islands and affecting the energy consumption level of residential buildings (Ou et al. 2013 , Ou et al. 2019 ). The urban form variables that reflect the built environment characteristics in existing research include transportation systems, public transportation, public service facilities, urban greening and urban infrastructure. For travel-related carbon emissions, the status of the urban road system will have a structural impact on the overall efficiency of transportation (Cao &Yang 2017 ). The convenience of public transportation, the layout and accessibility of public service facilities and urban green spaces affect the travel modes of residents, thereby affecting travel-related carbon emissions (Huang et al. 2019 , Wang et al. 2021b , Wolday 2023 ). The accessibility of public service facilities is significantly lower in newly developed urban areas and remote urban areas, resulting in longer average travel distances, greater use of motor vehicles, and higher transportation carbon emissions (Ma et al. 2018 ). Research on 256 cities in China reveals a significant correlation between the accessibility of green spaces and the mitigation of carbon emissions (Shi et al. 2022 ). For residential building carbon emissions, research shows that urban greening can affect carbon emissions from residential buildings through the urban heat island effect (Schwaab et al. 2021 , Ye et al. 2015 ). Evert and Meijers (2008) found a negative correlation between the density of green space within a 100-foot (approximately 30.5 m) radius and summer cooling electricity consumption. The condition of infrastructure fundamentally determines the energy structure, and its scale and layout will affect energy utilization efficiency, thereby affecting the carbon emissions of residential buildings, which is an important factor in urban energy conservation (Ji et al. 2016 ). Zhang et al. ( 2021a ) use gas pipe network density as an indicator and find that in cities with a population size greater than 500,000, improving the construction level of gas supply systems will have a significant negative driving effect on the carbon emissions of residential buildings. Rong et al. ( 2016 ) find that some residents in old communities and suburban areas use liquefied gas with high carbon emissions coefficients due to the incomplete construction of natural gas pipelines. With the continuous enrichment and deepening of research, scholars have realized the complex connections between the multidimensional variables of urban form. Urban spatial development patterns influence how land use, transport, and infrastructure can be configured, and different urban space development patterns may induce disparate built environmental and carbon emissions consequences (Tsai 2005 ). She et al. ( 2015 ) analyzed the influence of urban size on residents' travel carbon emissions in 25 cities located in the Yangtze River Delta region from 1990 to 2010. The results show that the expansion of urban space leads to an increase in road density, which has a prominent impact on regional carbon emissions. Shen et al. ( 2022 ) explore the influence of the built environment on carbon emissions using the PLS model and find that the urban size and urban sprawl of 19 counties in Taiwan indirectly affect total carbon emission through impact on transportation status (e.g., road network density). Ye et al. ( 2015 ) find in their study on Xiamen, China, that compact cities can reduce the area and accessibility of green spaces and water bodies, reduce the opportunity to regulate high ambient temperatures in densely built-up areas through green infrastructure, and increase household energy use carbon emissions. Some studies have directly explored the impact of urban compactness and complexity on the built environment (Jia &Tang 2019 ). The research results of 146 cities in China with a population of over 1 million show that higher urban compactness is associated with a lower density of the urban pipe network, fewer public transportation facilities and less per capita green space. The more complex the urban form, the higher density of the urban pipe network, the greater investment in public transportation, and the larger per capita green space (Jia &Tang 2019 ). Literature gaps The existing research has not been fully explored in the subsequent aspects. First, although these studies have fully evaluated the correlation between geometric characteristics of urban form and the CER, they tend to explore the sustainability of urban spatial development patterns, which leads to relevant planning suggestions that often remain at the level of urban strategy and long-term low carbon policy and will limit the formulation of more detailed urban low carbon policies. Second, existing studies have greatly enhanced our knowledge of the relationship between built environment characteristics and the CER. However, they mainly focus on a single built environmental factor (i.e., road traffic, urban greening), which may miss out on the potential interactions between different factors, as well as the impact of urban spatial development patterns (geometric characteristics of urban form) on the built environment characteristics. Third, scholars have realized that there may be intricate connections between multidimensional urban form elements that affect the CER. This finding provides ideas for exploring the mechanisms by which urban form affects carbon emissions in a more comprehensive and detailed way. However, limited studies have examined their relationships, and further empirical evidence is needed for confirmation and accumulate. Thus, exploring detailed impact mechanisms of urban form on carbon emissions through combining multidimensional urban form elements is the main contribution being attempted here. This comprehensive perspective is essential for gaining a fully understanding of the impact of urban form on carbon emissions and developing the most sustainable urban form. Methods and data Study areas The counties in the Yangtze River Delta region (YRD) are chosen as the study areas. The YRD is situated in the southeast coast of China, 118°20'E -123°25'E, 28°45' N -33°25'N, with a total area of 358,000 km 2 . This region includes one municipality (Shanghai), three provinces (Anhui, Jiangsu, and Zhejiang), and 152 counties, making it the most economically active and populous region in China. Rapid industrialization and urbanization have led to the rapid expansion of urban scale while stimulating high-intensity energy consumption. The carbon emissions of the YRD increased from 567.52 Mt in 2000 to 1720.82 Mt in 2017 (Shan et al. 2018 ), in which the carbon emissions of counties increased from 445.07 Mt to 1378.01 Mt (Chen et al. 2020 ). The proportion of carbon emissions in counties is relatively high, and there is a trend of further growth. The low-carbon development of counties is the key to achieving the "dual carbon" goal in the YRD. In addition, as a typical rapid urbanization development region in China, the development level of counties in the YRD has already ranked among the top in the counties and is seen as a demonstration area for county development in many aspects. The research results of this region can provide references for the sustainable development of other counties. Therefore, the study selected counties in the YRD as samples for studying low-carbon optimization of the urban form of counties. Due to differences in the statistical caliber of some counties and the lack of statistical data, the study selected 90 counties as the research samples. The study area is shown in Fig. 1 . Research method This study adopted the partial least squares structural equation model (PLS-SEM) to identify the effects and pathways of the urban form of counties on the CER. The structural equation model (SEM) is a multivariate estimation method that uses outer measurement and inner structural models to construct causal networks of various variables, which can effectively test the relationship between observed and latent variables (Zhu et al. 2019 ). The covariance-based estimation approach (CB-SEM) and component-based estimation approach (PLS-SEM) are the two major types of SEM in which PLS-SEM is gaining momentum in various research fields. The reasons for choosing PLS-SEM as the research method in this study are provided follows. First, PLS-SEM is a nonparametric method that can work efficiently with small sample sizes (30–100), unlike CB-SEM, which only obtains robust results when the sample size is 300–500 (Sarstedt et al. 2014 ). The study's sample size is 90, which is a small sample size. Second, PLS-SEM has no requirement for the data to adhere to a normal distribution within the context of PLS-SEM, while CB-SEM requires input data to obey a normal distribution. The data performed a normality test using the Shapiro-Wilk test and it was found that some samples did not strictly follow a normal distribution (see Appendix Table 5 ). Finally, the application of CB-SEM as a predictive research method is not advisable in cases when the direction of the association between variables cannot be determined. Therefore, PLS-SEM was chosen as the analytical model for this study. The factors of urban form are the exogenous latent variable, while the CER are the endogenous latent variables. The study used SmartPLS3.0 software to build a model for path analysis. The process of constructing a PLS-SEM to evaluate the impacts and pathways of the county’s urban form on the CER in this research can be briefly outlined in the following steps. First of all, this study assesses the outer measurement model to verify the reliability and validity of the observed variables, including determine indicator reliability, internal consistency, convergence validity, and discriminative validity verification. Secondly, this study evaluates the inner structural model, according to the testing suggestion proposed by (Hair et al. 2014 , Hair et al. 2019 ), including assessment for collinearity issues, coefficients of determination, and Q 2 stone-Geisser. After passing the above evaluation, a path analysis will be conducted next. By path coefficient and the significance testing of the coefficient to determine the degree of correlation between the exogenous latent variable and the endogenous latent variables and whether the exogenous latent variables would significantly influence the endogenous latent variables. Meanwhile, to determine the existence of the mediation effect, the bootstrap method and variance account for were used for testing (Shrout &Bolger 2002 ). Indicators and data preparation Urban form Based on the summary of existing studies and the framework of China's territorial spatial planning, this study defines the macroscale urban form as composed of geometric-aspect urban form and built environment-aspect urban form. Territorial spatial planning involves two types of planning focuses: space and land. The former focuses on spatial layout and spatial form features, while the latter focuses on land use in relation to human activities (Sun et al. 2022 ). Therefore, this study defines urban form from two aspects: geometric dimension and built environment dimension (Table 1 ). In this study, urban form refers to the form of urban built-up areas. It should be noted that counties usually have the county town as the main center, consisting of several surrounding towns of different levels and sizes. Thus, it is more meaningful to discuss the impact of the main center and the overall composed of the main center and surrounding towns on the CER, respectively. First, as the core area of the county, the county town is a densely populated area for residents' activities and energy consumption and the main body of the county's development. Its urban form changes are more drastic, which will exerting a significant influence on the CER. Second, due to the interconnectivity and interdependence between the county town and other towns in many aspects, when exploring the impact on the CER as a whole, it can not only reveal the impact of the overall urban form of the county on carbon emissions but also reveal the impact of the spatial relationship between the county town and other towns on carbon emissions. This can provide a basis for the reasonable organization and arrangement of urban resources within the county under the low-carbon goal. Therefore, based on the research purpose and scope of indicator application, this article will select urban built-up areas of county town and total built-up areas within the county administrative boundary as the objects of urban form (Fig. 2 ). For the main center of the county, this study refers and duplicates the methodology employed by Zhou et al. ( 2014 ) and Su et al. ( 2021 ) for determination. A 30 m × 30 m moving window method was applied to identify high-density built-up areas of each county (to match the pixel size of land-use data), and then through manual visual comparison and analysis, the border was delineated with a distance of 1 km. Land within the border was considered the main center of the county. The geometric-aspect urban form factors include urban scale, compactness, spatial structures, and urban shape, represented by typical landscape metrics. Landscape metrics have been extensively employed as a reliable approach for quantifying urban form, as they enable the description of the spatial composition and configuration of land cover (Huang et al. 2021 , She et al. 2015 ). The reliability of the metrics selected in this study has been validated in many studies and is easy for urban planners to understand and implement. Compactness measures the degree of agglomeration development in the main center using the agglomeration index (AI) and patch density index (PD). With the development of urbanization, towns have developed rapidly, and the single center spatial structure of counties has begun to change. Thus, to describe the spatial structure of the county, the largest patch index (LPI) is selected to characterize the degree of a single center by measuring the proportion of the county town to the total area of the built-up area. The larger the value is, the larger the scale of the county town, and the higher the degree of a single center. The urban shape focuses on exploring the complexity of the boundary of the main center and the narrowness of the overall shape of the county, selecting the mean perimeter area ratio index (PARA-MN) and the mean-related circumscribing circle distribution index (CIRCLE-MN) to measure, respectively. The calculation of landscape metrics is based on the land-use data. All the landscape metrics, of which the detailed definition and formulas are provided in Appendix Table A6, were computed for each county by using Fragstats4.2 program. The built environment-aspect urban form factors include public service facilities, urban greening, road traffic, public transportation, and urban energy infrastructure. The layout balance and accessibility of public service facilities are represented by the coverage ratio of medical facilities and the coverage ratio of educational facilities. Urban greening is measured by the coverage ratio of green spaces. To measure the impact of transportation status within the county town on travel-related carbon emissions, this study selected road density and cycling lane density as indicators. The higher the values of these two variables, the more developed the road system of the county town. Unlike large city residents who commute mainly within the city, the frequency of travel between towns and the county town is usually higher. This study selects highway density to measure the status of highway traffic, and the larger the value is, the more developed the highway traffic system is. Similarly, to measure the impact of the construction level and convenience of long-distance public transportation and public transportation of the main center around travel-related carbon emissions, this study selects the density of bus routes and the coverage rate of public transport, respectively. Urban energy infrastructure is related to the energy structure. Only 4 counties of the YRD region currently use centralized heating for winter heating. Thus, this study does not include the heating system in the scope of energy infrastructure research and only considers the gas system for residential cooking and self-heating. The gas network density is selected to measure urban energy infrastructure. The quantification of all the above built environment-aspect urban form factors is based on the statistical data and Google Maps API data. Table 1 The exogenous latent variable and observable variable of the PLS-SEM Dimension Latent variable Object Observable variable Adapted from Data sources the geometric-aspect urban form factors Urban scale County Total urban area (ha) (Li et al. 2021 , Ou et al. 2013 ) Land-use data (30×30 m), (Resources and Environmental Sciences and Data Center, Chinese Academy of Sciences, http://www.resdc.cn/ ) Compactness Main center Aggregation index (%) (Fang et al. 2015 , Xia et al. 2017 ) Patch density (%) (He et al. 2020 ) Spatial structures County Largest patch index (%) (Li et al. 2021 , Ou et al. 2019 , Zheng et al. 2023 ) Urban shape Main center Mean perimeter area ratio (None) (Ou et al. 2019 , Shi et al. 2020 , Wang et al. 2019b ) Mean-related circumscribing circle distribution (None) (Guo et al. 2021 , Zhang et al. 2020a ) the built environment-aspect urban form factors Public service facilities County Coverage ratio of medical facilities (with a radius of 500 m) (%) (Zhang et al. 2020b ) Google Maps API data Coverage ratio of educational facilities (with a radius of 500 m) (%) (Zhang et al. 2020b ) Urban greening Main center Coverage ratio of green spaces (%) (Chen 2015 , Tan et al. 2021 ) China County Construction Statistical Yearbook in 2019 Road traffic County Highway density (km/km 2 ) (Lei et al. 2023 , Xu et al. 2022 ) Main center Road density (km/km 2 ) (Ma et al. 2022 , Ou et al. 2013 ) Main center Cycling lane density (km/km 2 ) (Kyung-Hwan &Eun-Jeong 2014 , Wang et al. 2018 ) Public traffic County Density of bus routes (%) (Wei &Quan 2021 ) Google Maps API data Main center Coverage ratio of public transport (with a radius of 300 m) (%) (Chen 2017 , Hou et al. 2019 , Wei &Quan 2021 ) Urban energy infrastructure County Density of gas pipe network (km/km 2 ) (Zhang et al. 2021b) China County Construction Statistical Yearbook in 2019 Carbon emissions of residents The CER refers to the carbon emissions generated by direct energy use of residents, including residential building carbon emissions and travel-related carbon emissions (Zhang et al. 2015 ). Limited by the availability of energy consumption data of the counties in China, this study refers to the method proposed by Yan et al. ( 2022 ), Zhang et al. ( 2020a ), and Wang et al. ( 2021a ) as an alternative approach for estimating the CER. The method has been proven to estimate the CER in counties accurately. The residential building carbon emissions are estimated based on residents' gas consumption data (coal gas, natural gas, liquefied gas, coal), electricity, and heat (steam, hot water). The travel-related carbon emissions are estimated by converting the number of civilian motor vehicles, average driving distance, and average fuel consumption into transportation energy consumption. The energy consumption data of gas, electricity, and heat were obtained from the China County Construction Statistical Yearbook in 2019. The carbon emission coefficients of gas and heat were sourced from the China Energy Statistical Yearbook in 2015, the carbon emissions factors of the grid were derived from China's Regional Grid Baseline Emissions Factor Announcement in 2012. The data on the private car ownership came from the statistical yearbooks of counties in 2019. According to the results of the questionnaire and Guo's research (Guo 2012 ), the annual mileage is established at 15,000 km, while the fuel factor is established at 8.6 L/100 km (Guo 2012 ). Result Assessment of the outer measurement model The outer measurement model was employed to assess the reliability and validity of the observed variables. First, the indicator reliability was examined by the size of the outer loading. As shown in Table 2 , all the loadings were statistically significant (p < 0.01) and surpassed the threshold of 0.7, suggesting that the observed variable effectively represented the latent variable's intended meaning, and the present study satisfied the criteria of indicator reliability. Second, it was necessary to confirm the internal consistency and composite reliability of the PLS model by ensuring that the values of Cronbach's α and composite reliability (CR) above 0.70 (Sarstedt et al. 2023 ). The results show that both the Cronbach's α and CR values surpass the established threshold for reliability. This study meets the criteria of internal consistency. Third, the model was checked for convergent validity using the average variance extracted (AVE). The findings reveal that the AVE values of the latent variables exceed 0.50 indicating that the PLS model has satisfactory convergent validity (Hair et al. 2014 ). Finally, the heterotrait-monotrait (HTMT) ratio was examined to ensure the discriminant validity through The results are shown in Appendix Table 7 . The HTMT values for each dimension are found to be lower than the acceptable threshold of 0.9 (Henseler et al. 2015 ). This study satisfies the criteria of discriminant validity. Overall, the assessment of the outer measurement model is considered acceptable and can be further analyzed. Table 2 Results of model evaluation in the full model Dimension Latent variable Observable variable Loading Cronbach’s α CR AVE the geometric-aspect urban form factors Urban scale Total urban area 1.000 1.000 1.000 1.000 Compactness Aggregation index 0.999 0.795 0.804 0.699 Patch density 0.809 Spatial structures Largest patch index 1.000 1.000 1.000 1.000 Urban shape Mean perimeter area ratio 0.987 0.956 0.977 0.956 Mean-related circumscribing circle distribution 0.968 the built environment-aspect urban form factors Public service facilities Coverage ratio of medical facilities 0.781 0.793 0.864 0.761 Coverage ratio of educational facilities 0.837 Urban greening Coverage ratio of green spaces 1.000 1.000 1.000 1.000 Road traffic Highway density 0.837 0.733 0.760 0.645 Urban road density 0.856 Cycling lane density 0.738 Public traffic Density of bus routes (%) 0.771 0.773 0.869 0.689 Coverage ratio of public transport (%) 0.819 Urban energy infrastructure Density of gas pipe network 1.000 1.000 1.000 1.000 Evaluation of the inner structural model To evaluate the internal structural model, this study employed the measure of VIF to examine whether collinearity existed among the exogenous latent variables. The findings indicate that all VIF values are below the established threshold of 5, suggesting no collinearity issue in the model. Second, this study checked the coefficients of determination (R 2 ), which indicated predictive accuracy or explanatory power for the endogenous construct. R 2 values of 0.25, 0.50, and 0.75 might be characterized as indicating small, moderate, and large, respectively. The results show that the coefficient of determination (R 2 ) is 0.738, suggesting that the PLS model had moderate predictive power for the CER. Third, the predictive relevance was checked with Stone-Geisser's Q2 value. A Q2 value of 0.02, 0.15, or 0.35 signifies the extent to which an exogenous construct possesses a weak, medium, or large capacity to predict a certain endogenous construct. In this study, the Q2 a value that is 0.172 provides confirmation that the inner structural model exhibits a moderate level of predictive relevance. Finally, the acceptable range of the GOF value is 0–1. Within this range, values of 0.1, 0.25, and 0.36 correspond to minimum, medium, and large, respectively. (Akter et al. 2011 ). The results indicate that the GOF value for the inner structural model is 0.56, surpassing the threshold of 0.36. The structural model is considered to have a better prognostic ability. Therefore, the present study met the overall model fit criteria of PLS-SEM and can be employed for further analysis. Pathway analysis This study employed PLS modeling to establish the pathways urban form affects the CER. Path coefficients (standardized beta), significance levels, and t values were calculated to evaluate the associations among the latent variables (Fig. 3). Overall, the path coefficient of the PLS model in this study is significant at least at the level of 0.05, except for the nonsignificant impact of public traffic on the CER. Specifically, regarding whether the geometric-aspect urban form factors will have a direct impact on the CER, the results show that the urban scale, the degree of a single center of spatial structure, and urban shape have a direct and positive impact on the CER. The path coefficients are 0.245 (p < 0.001), 0.171, and 0.270, respectively, indicating that the control of urban scale and the degree of single center, decreasing complexity and shape narrowness positively impact reducing the CER. In contrast, this study finds that the direct effect of compactness on the CER has a significant value of − 0.419 (p < 0.01), and the path coefficient is significantly higher than that of other geometric-aspect urban form factors. This indicates that increasing compactness will significantly reduce the CER. For the relationship between built environment-aspect urban form factors and the CER, road traffic status and energy infrastructure have a direct positive impact on the CER, with path coefficients of 0.570 (p < 0.001) and 0.456, respectively. However, public service facilities and urban greening have opposite effects on the CER, with path coefficients of -0.209 and − 0.270, respectively. This means that transportation status and energy infrastructure have significantly increased the CER, while urban greening and public service facilities have considerably reduced the CER. In addition, regarding the correlation between urban geometric characteristics and built environment characteristics, specifically, the urban scale will affect the proportion of urban green coverage (0.33), the completeness and accessibility of public service facilities (0.433), and the transportation status (0.367). Compactness directly impacts the proportion of urban green coverage (-0.289). The degree of a single center of the spatial structure will impact the transportation status (0.281). Urban shape will mainly impact the transportation status (0.201) and energy infrastructure construction level (0.362). Mediation test The variance accounted for (VAF) approach was used to determine the type of mediation effect. A VAF values more than 80% indicates full mediation, whereas a value less than 20% indicates no mediation effect. The 20% and 80% values indicate partial mediation (Jr 2013 ). As shown in Table 3 , the results show ‘partial mediation’ among public service facilities, traffic status, and the proportion of green coverage between the urban scale and the CER. The indirect effects were 0.090, 0.209, and 0.089, respectively, with VAF values of 0.270, 0.461, and 0.267. Although they all show partial mediation, the mediating effect of traffic status was stronger. Compactness indirectly affects the CER through urban green space, with indirect effects of -0.060 and a VAF value of 0.216. A partial mediational effect of the proportion of green coverage is found between compactness and the CER. The complexity of urban form can indirectly affect the CER through traffic status (VAF = 0.297) and energy infrastructure (VAF = 0.241). The degree of a single center of spatial structure also has significant indirect pathways that affect the CER via traffic status. The indirect effects is 0.160, with VAF values ranging from 0.2 to 0.8, indicating that there were partial mediational effects. Table 3 Results of the mediation test Relationships Indirect effect t-value p-value Direct effect VAF Urban scale → Public service facilities → CER 0.090 2.262 0.013* 0.245 0.270 Urban scale → Road traffic → CER 0.209 2.48 0.031* 0.461 Urban scale → Urban greening → CER 0.089 2.267 0.004** 0.267 Compactness → Urban greening → CER -0.060 2.226 0.021* -0.219 0.216 Space structure → Road traffic → CER 0.160 2.161 0.042* 0.171 0.484 Urban shape → Road traffic → CER 0.115 2.126 0.049* 0.27 0.297 Urban shape → Urban energy infrastructure → CER 0.086 2.003 0.036* 0.241 Note: *, **, and *** indicate significance at 10%, 5%, and 1%, respectively Since public service facilities and road traffic contain multiple observation variables, further analysis is needed to determine the pathways. Based on the findings of the mediation test above, this study constructed five submodels to further analyze the more detailed relationship among urban geometric characteristics, built environment characteristics (public service facilities and road traffic), and the CER (Models 1–3). The results are presented in Table 4 . In Model 1, both medical and educational facilities play a mediating role in the impact of urban scale on the CER. The expansion of the urban scale will significantly affect the coverage ratio of educational facilities, thereby affecting carbon emissions (-0.578***), while the ability to affect the CER through the coverage ratio of medical facilities is limited (-0.135*). As another essential mediating variable of urban scale on the CER, in the dimension of road traffic, urban road density, highway density, and cycling lane density all have a significant impact on the CER with urban scale expansion (0.354**, 0.011*, and − 0.302**, respectively). In Model 2, with respect to how spatial structure affects road traffic and thus affects the CER, this study finds that the degree of a single center only indirectly affects the CER by affecting the highway density (0.341*). In Model 3, regarding how urban shape affects road traffic and thus affects the CER, this study finds that complexity and shape narrowness indirectly affect the CER by affecting highway density and road density (0.187*** and 0.227**, respectively). Table 4 Results of the mediation test of submodels Latent variable Observable variable Model 1 Model 2 Model 3 Public Service Facilities Coverage ratio of medical facilities -0.135* - - Coverage ratio of educational facilities -0.578*** - - Road Traffic Highway density 0.011* 0.341* 0.187*** Road density 0.354** 0.194 0.227** Cycling lane density -0.302** -0.170 -0.156 Note: *, **, and *** indicate significance at 10%, 5%, and 1%, respectively Model 1 includes urban scale, public service facilities, road traffic, and the CER Model 2 includes space structure, road traffic, and the CER Model 3 includes urban shape, road traffic, and the CER Discussions Based on the analysis of the PLS-SEM, this study clarified and quantified the effects and pathways of the urban form (geometric characteristics and built environment characteristics) of counties on the CER. According to the results of pathway analysis, the impact of road traffic, energy infrastructure, and compactness on the CER is significantly higher than that of other urban form factors, making it the decisive factor in reducing the CER. In addition, the study also obtains several meaningful findings. The built environment-aspect urban form factors are direct factors affecting the CER Public service facilities, urban greening, road traffic, and energy infrastructure are direct factors that affect the CER of counties. The completeness and accessibility of public service facilities directly impact travel-related carbon emissions, which is consistent with the research of (Wang et al. 2021b ) and (Yan et al. 2022 ) on counties. High-accessibility public service facilities can provide residents with more equal access to facilities, reducing travel distance and energy consumption. Urban greening has made a positive contribution to the CER. The reasons for this, on the one hand, green spaces have very effective carbon sequestration effects (Shen et al. 2021 ); on the other hand, as important parts of urban green space, parks, and squares are similar to the medical and educational facilities in this study, accessibility also significantly affects travel-related carbon emissions (Ye et al. 2015 ), with a negative correlation between the two. In addition, in this study, according to the survey results, 241 and 76 out of 420 respondents chose walking and cycling (electric bicycles) for their travel options to parks and squares, accounting for 75.5%. Green spaces affect residents' choice of transport modes (Xu et al. 2017 ) and promote active transportation (Xiao et al. 2022 ), which further reduces travel-related carbon emissions. The findings related to road traffic coincide with previous research and are an important driver for carbon emissions (Rasool et al. 2019 , Wang et al. 2019a ). Counties that are experiencing rapid urbanization, high-intensity land development, and road infrastructure construction will significantly increase the frequency of private car use. In addition, public transport systems in counties are typically underdevelopment, resulting in a significant reliance on private cars among local residents for their daily commuting needs (Guo et al. 2022 ), further increasing travel-related carbon emissions. In this study, urban energy infrastructure is another key factor and has a significant positive driving effect on the CER. Studies conducted in large cities demonstrated that improving energy structures could effectively suppress the growth of carbon emissions from residential buildings (Zhang et al. 2021a ). However, for counties that are currently experiencing rapid development, although the condition of energy infrastructure will affect the type of energy used by residents and promote the use of clean energy, in the situation where the demand for domestic energy is not yet stable, the density of gas pipelines is positively correlated with the total energy consumption of residential buildings. The geometric-aspect urban form factors are crucial factors affecting the CER This study finds that the geometric features of urban form (scale, compactness, spatial structures, and shape) can directly affect the CER and indirectly affect the CER through some built-up environmental feature elements. The geometric features of urban form play a crucial role in influencing the CER and are critical factors in mitigating the CER. Urban scale expansion has increased commuting time, distance, and reliance on motor vehicles (Wang et al. 2019a , Wang et al. 2019b ). Meanwhile, urban expansion often accompanies population growth and improving residents' living standards (Song et al. 2020 , Wang et al. 2019a , Zheng et al. 2021 ), which increases energy consumption and corresponding carbon emissions. In addition, although one of the basic configuration methods for public service facilities is to layout them based on city size and administrative level, in this study, as the city size increases, the balance of the layout of medical and educational facilities will also be affected. This is mainly because the construction speed of public service facilities lags behind the speed of urban expansion (Fang et al. 2017 ). The increase in the proportion of green coverage is related to the growth of the urban scale. Although urban development may occupy vegetation and ecological land in suburban areas and convert them into construction land, such as residential and infrastructure (Kong &Nakagoshi 2006 ), urban expansion has the potential to create additional space for green space within the city. This can be achieved through various means, including the implementation of road landscape greening, the reconstruction of open spaces, and the establishment of green spaces within residential areas (Kong &Nakagoshi 2006 , Wang et al. 2023 ). This finding coincides with the empirical research conducted by Wang et al. ( 2023 ) on the Yangtze River Delta region. Urban expansion will increase the density of cycling lanes. This may be due to the insufficient consideration of issues such as traffic operation efficiency and motorization development in road planning during the early stages of counties, resulting in unclear road functions and mixed use of motor and nonmotor vehicles. As urban areas continue to grow, there is a corresponding improvement in the road traffic system and an increase in the density of cycling lanes. The increase in the degree of a single center positively contributes to the CER, which is consistent with the concept of polycentric development advocated in a large city. The increase in the degree of a single center in counties will lead to a prominent phenomenon of uneven resource allocation between the main center (the county town) and the surrounding towns. Generally, the highly qualified educational and medical facilities are mainly concentrated in the main center, and residents in towns need longer travel distances and times to obtain high-quality resources, increasing travel-related carbon emissions (Guo et al. 2022 ). Meanwhile, an increase in the degree of a single center will increase the highway density. Under significantly insufficient employment opportunities and public service facilities provided by townships, residents are more inclined to choose long-distance commuting to and from the main center and residential areas, which will further increase travel-related carbon emissions. The study presented by Chen et al. ( 2011 ) provides an example. They found that many newly built residential areas in Guangzhou are located far from the city center. However, there is a lag in the development of public service facilities in that location. The residents have to undertake extensive long-distance commutes between the city center and where they live. In this study, the negative correlation between the compactness of the main center and the CER corresponds to the perspective of compact development. This is consistent with the research conducted by Ou et al. ( 2019 ) on third- to fifth-tier cities in China. This is mainly because the main center can supply more socioeconomic functions, has less demand for automobile transportation, and has higher accessibility (Thinh et al. 2002 ). In addition, the compact main center will affect the proportion of green coverage and thus affect carbon emissions. Intensive and high-density urban land development may occupy existing green space, leading to a decreased supply of urban green space (Lin et al. 2015 , Yao et al. 2019 ). This is consistent with the observed correlation between green spaces and high-density development in large cities in the 1990s (Jim &Chen 2003 , Kong &Nakagoshi 2006 ). Decreasing the complexity and narrowness of the shape of the main center is also closely related to reducing the CER. The results are consistent with those of large cities (Chen et al. 2011 ). Regular and nonnarrowness land use patterns can avoid unnecessary detours, reduce the travel time and distance of motor vehicles, and lower travel-related carbon emissions. In addition, irregular and complex construction land boundaries can increase the investment in infrastructure construction, forcing relatively inefficient laying methods for energy pipelines (Yeh &Li 2001 ), such as affecting the layout of gas pipelines, pipeline laying length and density, thereby affecting energy transmission losses in buildings and increasing the CER. Limitations and future directions There are some limitations to this study. The first limitation involves the diversity of the research sample. This study only takes the YRD region as an example to discuss the relationship between the urban form of counties and the CER. Future research can conduct more cross-regional studies to avoid overlooking counties with different development trajectories and to fully understand the impact mechanism of urban form of counties on the CER. The second limitation is from the raw data of the CER. The raw data of the CER is limited by the quality of statistical yearbooks of counties. Although this study uses the results of a questionnaire containing data on travel characteristics as a complementary. The results may be inaccurate in residents’ perceptions of their travel characteristics inevitably. Future research can collect more travel characteristics data through multisource data mining to further improve the reliability of the raw data of the CER. In addition, due to the use of cross-sectional data from 2019 for the purpose of examining the correlation between the two, it is impossible to visualize individual differences due to time changes. Therefore, future research may consider examining long-term series data and employing dynamic simultaneous equation models to analyze this relationship further. Conclusions and recommendations This study took 90 counties in the YRD region as the study areas and defined urban form from two dimensions: the geometric aspect and the built environment aspect. Then, the PLS-SEM was employed to explore the direct and indirect effects between urban form and the CER, and critical factors for reducing the CER were identified. The results mainly indicate that (1) the geometric-aspect urban form factors (scale, compactness, structure, and shape) will directly affect the CER. Controlling the urban development boundary, increasing the compactness of the main center, developing in the direction of multiple centers, and minimizing the complexity and narrowness of the shape of the main center have been found to result in important advantages in terms of the CER reduction. (2) The factors that reduce the CER of the built environment-aspect urban form include public service facilities, road traffic, urban greening, and energy infrastructure. Road traffic and energy infrastructure have a considerable negative effect on the CER, while improving the accessibility and completeness of public service facilities, as well as increasing the proportion of green coverage are beneficial for counties to reduce the CER. (3) The findings from the mediation effect analysis suggest the geometric characteristics of urban form will affect the built environment characteristics and thus affect the CER. Public service facilities, road traffic, greening spaces, and energy infrastructure are the influential mediators. (4) There are differences in the impact of urban form on the CER. Road traffic, energy infrastructure, and compactness are decisive factors in reducing the CER. This study confirms through empirical analysis that spatial planning is a crucial measure for counties to address reduce carbon emissions. Simultaneously, this study has found that both the geometric characteristics of urban form and the built environment characteristics have a stable impact on the CER. Nevertheless, there are differences in the effects and pathways. The following suggestions are proposed to optimization of urban form of county and achievement “dual carbon” goals. First, planning strategies and policies should be designed with the objective of improving the compactness of the main center and controlling the expansion of the urban scale. Decision-makers should encourage the redevelopment of underutilized land, brownfields, and spare buildings within the main center to reduce the demand for land expansion. It has not only improved the efficiency of land use but also taken advantage of the centralization of various social and economic functions in the main center to reduce the traffic demand and dependence on vehicles. Meanwhile, urban land can be efficiently used to build small-scale green infrastructure, such as linear street green spaces and corner parks. Implementing vertical greening is another viable approach to alleviate the conflict between high-density urban development and environmental issues, such as green roofs, green walls, and vertical greening systems (Carvalho et al. 2022 , Liu et al. 2018 ). These forms and functions of green spaces improve the overall green space coverage, increase the opportunities for residents to use green space (Fan et al. 2017 ), and offset the negative impacts of compactness development (i.e., heat island effect and air quality) (Haaland &van den Bosch 2015 ). Second, the main center's scale should be considered, while promoting the counties to develop in the direction of multiple centers. The findings of this study demonstrate a significant positive association between the CER and the degree of a single center, and the development of multiple centers can help mitigate carbon emissions. However, unlike most large cities' balanced multicenter development strategy, counties with small urban scales and small populations need to concentrate resources and population to develop their economy and improve production and living efficiency. The effectiveness of urban development may be weakened by emphasizing on the balanced development of multiple centers. Thus, it is imperative to guarantee the scale of the main center. At the same time, public service facilities in each center should be improved to ensure that residents can meet their daily needs and avoid unnecessary long-distance travel. In general, this study provides evidence in favor of the contemporary phenomenon of multicenter development in Chinese cities. Third, policy-makers should minimize the complexity and narrowness of the shape of the main center as the optimization direction for urban form, avoiding the negative impact on the CER. Due to terrain constraints, development suitability, and economic value costs in urban land development, the land's boundary and shape can be adjusted using blue and green space by adding green spaces and water bodies to form a continuous and complete landscape interface. It helps to form a regular road traffic network to avoid travel-related carbon emissions caused by unnecessary detours, congestion, and road twists and turns. In addition to policy recommendations based on the empirical results, although the effectiveness of public transportation in reducing the CER in counties is limited, according to relevant previous research, improving the public transport system is an inevitable choice for sustainable development. In particular, it is essential to build long-distance and interregional public transportation systems according to the practical travel needs among the main center, towns, and villages. Declarations Acknowledgments This work was supported by the National Key Research and Development Project, China (Grant No. 2018YFC0704705). Besides, the authors would like to thank the anonymous reviewers for their valuable comments on this paper. Author contribution Ran Guo and Qing Yuan conceived and designed the methodologies and was the major contributor to writing the manuscript. Qing Yuan and Hong Leng offered funding to support the research and revised the paper. Shiyi Song made the validation and language editing. All the authors have read and approved the final manuscript. Funding This work was supported by the National Key Research and Development Project, China (Grant No. 2018YFC0704705). Data availability Not applicable. Ethics approval Not applicable. This article does not contain any studies with human participants or animals performed by any of the authors. Consent to participate Not applicable. This article does not contain any studies with human participants or animals performed by any of the authors. Consent for publication Not applicable. This article does not contain any individual person’s data in any form. Competing interests The authors declare no competing interests. References Akter S, D'Ambra J, Ray P (2011): Trustworthiness in mHealth Information Services: An Assessment of a Hierarchical Model with Mediating and Moderating Effects Using Partial Least Squares (PLS). 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-3672227","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":262875899,"identity":"a2c8d2ac-7825-4d6c-bc36-0fbdd0d75d39","order_by":0,"name":"Ran 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Institute of Industry: Harbin Institute of Technology","correspondingAuthor":false,"prefix":"","firstName":"Qing","middleName":"","lastName":"Yuan","suffix":""},{"id":262875902,"identity":"7322d518-b7e8-428b-b507-41aaeeba909a","order_by":3,"name":"Shiyi Song","email":"","orcid":"","institution":"Xi'an University of Architecture and Technology","correspondingAuthor":false,"prefix":"","firstName":"Shiyi","middleName":"","lastName":"Song","suffix":""}],"badges":[],"createdAt":"2023-11-27 12:48:02","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3672227/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3672227/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s11356-024-34836-z","type":"published","date":"2024-09-13T15:57:46+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":49063133,"identity":"8e43a4c1-5684-4ccd-83be-2038cf10fd9e","added_by":"auto","created_at":"2024-01-02 14:32:47","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":96247,"visible":true,"origin":"","legend":"\u003cp\u003eThe location of study areas in the Yangtze River Delta region\u003c/p\u003e","description":"","filename":"1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3672227/v1/a7bbb79b8183f31d4a2efcff.jpg"},{"id":49062067,"identity":"d3fd07a7-b0e1-49bf-ae18-1f33de9a18ef","added_by":"auto","created_at":"2024-01-02 14:24:47","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":92640,"visible":true,"origin":"","legend":"\u003cp\u003eThe delineation of the main center of the county, an example of Changxing\u003c/p\u003e","description":"","filename":"2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3672227/v1/e77139d62beb03eee11b6457.jpg"},{"id":49063134,"identity":"c7c88eb3-a506-4ec1-bcad-b528a33cd5ee","added_by":"auto","created_at":"2024-01-02 14:32:47","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":97881,"visible":true,"origin":"","legend":"\u003cp\u003eSummary of pathway analysis results of urban form and the CER\u003c/p\u003e\n\u003cp\u003eNote: *, **, and *** indicate significance at 10%, 5%, and 1%, respectively\u003c/p\u003e","description":"","filename":"3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3672227/v1/22e6f9fe10ea135d3992a85e.jpg"},{"id":64619154,"identity":"cd35215a-709e-43f2-8728-20829c023fc5","added_by":"auto","created_at":"2024-09-16 16:11:59","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1141447,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3672227/v1/adfe7513-19f7-42fd-9c55-f5a54865db20.pdf"},{"id":49062064,"identity":"f01d1aa8-40c1-454b-abee-5b9896e462e3","added_by":"auto","created_at":"2024-01-02 14:24:47","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":19814,"visible":true,"origin":"","legend":"","description":"","filename":"Appendix.docx","url":"https://assets-eu.researchsquare.com/files/rs-3672227/v1/3a7a689b5f321065c75c8ec8.docx"}],"financialInterests":"","formattedTitle":"The influence of multidimensional urban form of counties on carbon emissions of residents and its planning implications: Evidence from counties in the Yangtze River Delta, China","fulltext":[{"header":"Introduction","content":"\u003cp\u003eThe increase in greenhouse gas emissions has given rise to the occurrence of global warming and consequent climate change, leading to a series of serious problems that threaten global natural ecosystems and human development, such as drought, floods, the rise in sea levels, the loss of biodiversity, and economic downturn (Jackson et al. \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). China has experienced a significant acceleration in its industrialization and urbanization processes following the beginning of economic reforms in 1978. However, the swift progress has concurrently led to a significant quantity of carbon emissions. As the top carbon emitter globally today, China has set a target to reduce its GDP per capita carbon emissions by 60\u0026ndash;65% by 2030 and has made a commitment to achieve carbon neutral by 2060. Given the present circumstances, it might be argued that China has an enormous challenge in achieving its emission-reduction objectives.\u003c/p\u003e \u003cp\u003eUrban areas, the centers of human socioeconomic endeavors, are responsible for more than 70% of the carbon emissions produced by human activities on a global scale. Consequently, they are widely recognized as the primary catalysts for the accelerated pace of climate change (Cai \u0026amp;Zhang \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2014\u003c/span\u003e, Pappas et al. \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). In China, this proportion is 85% (Shan et al. \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Therefore, taking reasonable measures to reduce urban carbon emissions effectively is considered a vital area for implementing climate change adaptation. Researchers and policy-makers are actively exploring measures that can effectively mitigate urban carbon emissions (Ou et al. \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). In recent years, spatial planning for carbon emissions control has gradually been recognized (Seto et al. \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2014\u003c/span\u003e, Zhao \u0026amp;Zhang \u003cspan citationid=\"CR110\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Reducing carbon emissions by changing urban form has become one of the important channels to address climate change. Urban form refers to the arrangement and organization of various urban elements and human activities (Sharifi \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2019a\u003c/span\u003e), which can reflect the urban spatial utilization patterns and resource allocation (Wang et al. \u003cspan citationid=\"CR88\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Once established, it will exert a lasting impact on the functioning of societal, economic, and human conduct (Brian \u0026amp;Haozhi \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) and have a locking effect on urban energy use efficiency and carbon emissions (Sharifi \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2019a\u003c/span\u003e). Therefore, it is essential to acquire an in-depth knowledge of the correlation between urban form and carbon emissions in order to shape the sustainable urban form.\u003c/p\u003e \u003cp\u003eThe issue of carbon reduction in Chinese cities, and the methods by which they can do this through adjustments and optimizations to urban form, has generated significant interest among researchers, urban planners, and decision-makers, given China's status as the largest developing and transitional nation globally. However, previous research has mostly concentrated on megacities and large cities in China, such as municipalities, provincial capital cities, and prefecture-level cities (Ou et al. \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2013\u003c/span\u003e, Song et al. \u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e2017\u003c/span\u003e, Wang et al. \u003cspan citationid=\"CR86\" class=\"CitationRef\"\u003e2017a\u003c/span\u003e, Wang et al. \u003cspan citationid=\"CR89\" class=\"CitationRef\"\u003e2017b\u003c/span\u003e). Little research is dedicated to studying the correlation between the county\u0026rsquo;s urban form and carbon emissions. Counties rank lowest within the Chinese administrative division system, behind provinces and prefecture-level cities. However, as of 2020, the county's population constitutes approximately 73% of the total population, and the GDP accounts for over 60% of the country (Department of Rural Surveys 2019). In recent years, the urban form of the county and the energy consumption of residents have undergone considerable changes as a result of China's promotion of county-based urbanization policies. The average expansion rate of urban built-up areas in counties between 2010 and 2017 was 38.4%, exceeding the national average growth rate of 35.06%. In 2015, the total carbon emissions of counties constituted a proportion above 50% of the national carbon emissions (Wang et al. \u003cspan citationid=\"CR87\" class=\"CitationRef\"\u003e2021a\u003c/span\u003e). In 2014, the per capita living gas consumption in the county was 8% higher than that in the city, at 205 m\u003csup\u003e3\u003c/sup\u003e. Focusing on counties for low-carbon research is urgent and necessary. Simultaneously, with the increase in household income and the continuous improvement of lifestyle, the carbon emissions linked to residents' daily activities have progressively emerged as a significant factor in China's overall energy consumption (Qiu \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). In 2021, Chinese residents\u0026rsquo; energy consumption constituted approximately 40\u0026ndash;50% of the total energy consumption. Moreover, the energy consumption patterns of typical urban families have transitioned from primarily focusing on \"eat and wear\" to mainly encompassing \"residence and travel\" (Yin \u0026amp;Shi \u003cspan citationid=\"CR105\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). It is foreseeable that residential buildings and travel-related carbon emissions will also rise rapidly. Therefore, given the significant need to reduce emissions in the current stage, it is imperative to prioritize the urban form of counties and evaluate and analyze the influence of urban form on carbon emissions associated with residents' lives. This work has the potential to offer a more comprehensive perspective on low-carbon spatial planning, thereby aiding China in its achievement of carbon control and emission reduction goals.\u003c/p\u003e \u003cp\u003eThis study aimed to investigate the influence of the multidimensional urban form of counties on the CER by taking 90 Chinese counties located in the Yangtze River Delta region. To this end, considering the status of the county's urban form and residential energy consumption, we analyzed the urban form of main center and overall urban form. Then, the urban form was defined as geometric-aspect urban form and built environment-aspect urban form. Finally, we adopted a partial least squares structural equation modeling (PLS-SEM) to obtain a more accurate evaluation of the effects of urban form on the CER. This article is expected to complement existing knowledge on the effect of county's urban form on the CER and can be used for low-carbon spatial planning by optimizing urban form to reduce the CER.\u003c/p\u003e"},{"header":"Literature review","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eUrban form: meanings, scale and factors\u003c/h2\u003e \u003cp\u003eThe term \"urban form\" usually refers to the spatial structure and form of various urban elements and human activities (Sharifi \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2019a\u003c/span\u003e, Wolday \u003cspan citationid=\"CR95\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). By synthesizing existing research, it becomes evident that there exist varying interpretations among scholars about the concept of urban form. But overall, urban form can be defined from two perspectives: narrow and broad. The narrow urban form usually refers to the form characteristics displayed by urban physical elements. It primarily emphasizes the attributes of urban physical space, including land use, spatial pattern of urban landscape, streets, communities, buildings, and infrastructure (Shi et al. \u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). The broad urban form not only includes the abovementioned physical elements but also involves the forms of nonphysical elements such as society, economy, and population, such as political and social forms, economic and trade forms, and population structure (Clark et al. \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2011\u003c/span\u003e, Long \u0026amp;Ye \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eUrban forms that are defined from physical attributes, namely, narrow urban forms, can be further classified into three scale-based categories: micro, meso, and macro (Sharifi \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2019a\u003c/span\u003e). The microscale primarily concerns itself with the design and structural features of buildings (i.e., height, orientation, shape coefficient), as well as their position in relation to neighborhood buildings and surrounding open spaces (i.e., density, plot ratio, layout pattern) (Kuhn \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2019\u003c/span\u003e, Sharifi \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2019c\u003c/span\u003e, Wang \u003cspan citationid=\"CR83\" class=\"CitationRef\"\u003e2006\u003c/span\u003e). The meso-scale pertains to the size, spatial structure and layout pattern of communities, blocks, open spaces and streets (i.e., mixed land-use intensity, height-to-width ratio of street) (Sharifi \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2019a\u003c/span\u003e, Wong et al. \u003cspan citationid=\"CR96\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). Compared to the urban form at the micro- and mesoscale, which pertains to the human scale (Long \u0026amp;Ye \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), macroscale urban form research emphasizes on the overall layout of urban, land use patterns and development modes (Sharifi \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e2019b\u003c/span\u003e). The land use scale, compactness, spatial structure, layout of public service facilities, road system, green space structure, and infrastructure are all factors of macroscale urban form, reflecting the spatial organization and arrangement of human activities and various urban resources. This study focuses on urban form on a macroscale level.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eUrban form and carbon emissions of residents\u003c/h2\u003e \u003cp\u003eThe effects of urban form on the carbon emissions of residents are mostly examined through the depiction of various characteristics of urban form (Shi et al. \u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). In recent years, macroscale-related research has mainly described urban form from aspects such as urban geometric characteristics and built environment characteristics, which have been proven to be the primary determinants of carbon emissions (Shi et al. \u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e2022\u003c/span\u003e, Zheng et al. \u003cspan citationid=\"CR112\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Related research can be divided into three categories: (1) how the urban geometric characteristics affect the CER; (2) how the built environment characteristics affect the CER; and (3) how the urban geometric characteristics and the built environment characteristics have impact on the CER together. Meanwhile, as the CER includes travel-related carbon emissions and residential building carbon emissions, the main discussion is around the influence of urban form on these two types of carbon emissions.\u003c/p\u003e \u003cp\u003eThe urban form variables that reflect geometric characteristics in existing studies include compactness, scale, spatial structure, and urban shape. These various factors could affect energy consumption and relate the carbon emissions of residents through several paths. Compactness urban form means an almost circle-like built-up area, highly dense development and mixed land use (Angel et al. \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). For travel-related carbon emissions, it can increase traffic accessibility and mitigate the reliance on automobile transportation, and decrease the distance of commuting and minimize the duration of automotive trips (Banister \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2012\u003c/span\u003e, Chen et al. \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2008\u003c/span\u003e, Thinh et al. \u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e2002\u003c/span\u003e). However, research on large cities has shown the opposite result. Higher compactness may also lead to an increase in traffic volume, traffic jams and travel time, then a rising fuel consumption, and corresponding carbon emissions (Ou et al. \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). The expansion of urban scale is closely related to travel-related carbon emissions, and continuous scale expansion leads to an increase in average commuting distance (Gudipudi et al. \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2016\u003c/span\u003e, Mohajeri et al. \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Furthermore, it has been confirmed by scholars that the spatial structures of single centers and multiple centers have an impact on travel-related carbon emissions. Polycentric urban forms often foster mixed land use and shorter commuting trips, reducing overall travel and traffic congestion (Li et al. \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2019\u003c/span\u003e, Veneri \u003cspan citationid=\"CR82\" class=\"CitationRef\"\u003e2010\u003c/span\u003e), and are more conducive to reducing traffic carbon emissions in large cities but have less impact on small cities (Li et al. \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Urban form complexity can also result in a rise of carbon emissions. The increasingly complex urban form cannot support public transit, which increases the travel distance of passengers and the duration of automobile trips, easily causing traffic congestion and accelerating exhaust emissions (Ou et al. \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). For residential building carbon emissions, previous studies have shown that the heat island effect affecting energy use for cooling is stronger in compact areas (Ewing \u0026amp;Rong \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). This makes residents consume more energy to meet the demands for cooling. An empirical study was carried out on 50 cities in Japan. The findings indicate that compactness is associated with higher per capita residential energy consumption, which may be due to the impact of higher density on sky lighting and natural ventilation (Makido et al. \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). Urban expansion is also considered as an important factor in increasing residential building carbon emissions (Falahatkar \u0026amp;Rezaei \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). The rapid expansion of the urban scale would result in a decrease in vegetation coverage and carbon storage resources, thus contributing to the formation of urban heat islands and affecting the energy consumption level of residential buildings (Ou et al. \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2013\u003c/span\u003e, Ou et al. \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe urban form variables that reflect the built environment characteristics in existing research include transportation systems, public transportation, public service facilities, urban greening and urban infrastructure. For travel-related carbon emissions, the status of the urban road system will have a structural impact on the overall efficiency of transportation (Cao \u0026amp;Yang \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). The convenience of public transportation, the layout and accessibility of public service facilities and urban green spaces affect the travel modes of residents, thereby affecting travel-related carbon emissions (Huang et al. \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2019\u003c/span\u003e, Wang et al. \u003cspan citationid=\"CR92\" class=\"CitationRef\"\u003e2021b\u003c/span\u003e, Wolday \u003cspan citationid=\"CR95\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). The accessibility of public service facilities is significantly lower in newly developed urban areas and remote urban areas, resulting in longer average travel distances, greater use of motor vehicles, and higher transportation carbon emissions (Ma et al. \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Research on 256 cities in China reveals a significant correlation between the accessibility of green spaces and the mitigation of carbon emissions (Shi et al. \u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). For residential building carbon emissions, research shows that urban greening can affect carbon emissions from residential buildings through the urban heat island effect (Schwaab et al. \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2021\u003c/span\u003e, Ye et al. \u003cspan citationid=\"CR103\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Evert and Meijers (2008) found a negative correlation between the density of green space within a 100-foot (approximately 30.5 m) radius and summer cooling electricity consumption. The condition of infrastructure fundamentally determines the energy structure, and its scale and layout will affect energy utilization efficiency, thereby affecting the carbon emissions of residential buildings, which is an important factor in urban energy conservation (Ji et al. \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Zhang et al. (\u003cspan citationid=\"CR108\" class=\"CitationRef\"\u003e2021a\u003c/span\u003e) use gas pipe network density as an indicator and find that in cities with a population size greater than 500,000, improving the construction level of gas supply systems will have a significant negative driving effect on the carbon emissions of residential buildings. Rong et al. (\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) find that some residents in old communities and suburban areas use liquefied gas with high carbon emissions coefficients due to the incomplete construction of natural gas pipelines.\u003c/p\u003e \u003cp\u003eWith the continuous enrichment and deepening of research, scholars have realized the complex connections between the multidimensional variables of urban form. Urban spatial development patterns influence how land use, transport, and infrastructure can be configured, and different urban space development patterns may induce disparate built environmental and carbon emissions consequences (Tsai \u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e2005\u003c/span\u003e). She et al. (\u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e2015\u003c/span\u003e) analyzed the influence of urban size on residents' travel carbon emissions in 25 cities located in the Yangtze River Delta region from 1990 to 2010. The results show that the expansion of urban space leads to an increase in road density, which has a prominent impact on regional carbon emissions. Shen et al. (\u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) explore the influence of the built environment on carbon emissions using the PLS model and find that the urban size and urban sprawl of 19 counties in Taiwan indirectly affect total carbon emission through impact on transportation status (e.g., road network density). Ye et al. (\u003cspan citationid=\"CR103\" class=\"CitationRef\"\u003e2015\u003c/span\u003e) find in their study on Xiamen, China, that compact cities can reduce the area and accessibility of green spaces and water bodies, reduce the opportunity to regulate high ambient temperatures in densely built-up areas through green infrastructure, and increase household energy use carbon emissions. Some studies have directly explored the impact of urban compactness and complexity on the built environment (Jia \u0026amp;Tang \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). The research results of 146 cities in China with a population of over 1\u0026nbsp;million show that higher urban compactness is associated with a lower density of the urban pipe network, fewer public transportation facilities and less per capita green space. The more complex the urban form, the higher density of the urban pipe network, the greater investment in public transportation, and the larger per capita green space (Jia \u0026amp;Tang \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e \u003cdiv id=\"Sec5\" class=\"Section3\"\u003e \u003ch2\u003eLiterature gaps\u003c/h2\u003e \u003cp\u003eThe existing research has not been fully explored in the subsequent aspects. First, although these studies have fully evaluated the correlation between geometric characteristics of urban form and the CER, they tend to explore the sustainability of urban spatial development patterns, which leads to relevant planning suggestions that often remain at the level of urban strategy and long-term low carbon policy and will limit the formulation of more detailed urban low carbon policies. Second, existing studies have greatly enhanced our knowledge of the relationship between built environment characteristics and the CER. However, they mainly focus on a single built environmental factor (i.e., road traffic, urban greening), which may miss out on the potential interactions between different factors, as well as the impact of urban spatial development patterns (geometric characteristics of urban form) on the built environment characteristics. Third, scholars have realized that there may be intricate connections between multidimensional urban form elements that affect the CER. This finding provides ideas for exploring the mechanisms by which urban form affects carbon emissions in a more comprehensive and detailed way. However, limited studies have examined their relationships, and further empirical evidence is needed for confirmation and accumulate. Thus, exploring detailed impact mechanisms of urban form on carbon emissions through combining multidimensional urban form elements is the main contribution being attempted here. This comprehensive perspective is essential for gaining a fully understanding of the impact of urban form on carbon emissions and developing the most sustainable urban form.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section3\"\u003e \u003cdiv id=\"Sec7\" class=\"Section4\"\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e "},{"header":"Methods and data","content":"\u003ch2\u003eStudy areas\u003c/h2\u003e\u003cp\u003eThe counties in the Yangtze River Delta region (YRD) are chosen as the study areas. The YRD is situated in the southeast coast of China, 118°20'E -123°25'E, 28°45' N -33°25'N, with a total area of 358,000 km\u003csup\u003e2\u003c/sup\u003e. This region includes one municipality (Shanghai), three provinces (Anhui, Jiangsu, and Zhejiang), and 152 counties, making it the most economically active and populous region in China. Rapid industrialization and urbanization have led to the rapid expansion of urban scale while stimulating high-intensity energy consumption. The carbon emissions of the YRD increased from 567.52 Mt in 2000 to 1720.82 Mt in 2017 (Shan et al. \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), in which the carbon emissions of counties increased from 445.07 Mt to 1378.01 Mt (Chen et al. \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). The proportion of carbon emissions in counties is relatively high, and there is a trend of further growth. The low-carbon development of counties is the key to achieving the \"dual carbon\" goal in the YRD. In addition, as a typical rapid urbanization development region in China, the development level of counties in the YRD has already ranked among the top in the counties and is seen as a demonstration area for county development in many aspects. The research results of this region can provide references for the sustainable development of other counties. Therefore, the study selected counties in the YRD as samples for studying low-carbon optimization of the urban form of counties. Due to differences in the statistical caliber of some counties and the lack of statistical data, the study selected 90 counties as the research samples. The study area is shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e\u003ch2\u003eResearch method\u003c/h2\u003e\u003cp\u003eThis study adopted the partial least squares structural equation model (PLS-SEM) to identify the effects and pathways of the urban form of counties on the CER. The structural equation model (SEM) is a multivariate estimation method that uses outer measurement and inner structural models to construct causal networks of various variables, which can effectively test the relationship between observed and latent variables (Zhu et al. \u003cspan citationid=\"CR114\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). The covariance-based estimation approach (CB-SEM) and component-based estimation approach (PLS-SEM) are the two major types of SEM in which PLS-SEM is gaining momentum in various research fields.\u003c/p\u003e\u003cp\u003eThe reasons for choosing PLS-SEM as the research method in this study are provided follows. First, PLS-SEM is a nonparametric method that can work efficiently with small sample sizes (30–100), unlike CB-SEM, which only obtains robust results when the sample size is 300–500 (Sarstedt et al. \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). The study's sample size is 90, which is a small sample size. Second, PLS-SEM has no requirement for the data to adhere to a normal distribution within the context of PLS-SEM, while CB-SEM requires input data to obey a normal distribution. The data performed a normality test using the Shapiro-Wilk test and it was found that some samples did not strictly follow a normal distribution (see \u003cspan refid=\"Sec22\" class=\"InternalRef\"\u003eAppendix\u003c/span\u003e Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). Finally, the application of CB-SEM as a predictive research method is not advisable in cases when the direction of the association between variables cannot be determined. Therefore, PLS-SEM was chosen as the analytical model for this study. The factors of urban form are the exogenous latent variable, while the CER are the endogenous latent variables. The study used SmartPLS3.0 software to build a model for path analysis.\u003c/p\u003e\u003cp\u003eThe process of constructing a PLS-SEM to evaluate the impacts and pathways of the county’s urban form on the CER in this research can be briefly outlined in the following steps. First of all, this study assesses the outer measurement model to verify the reliability and validity of the observed variables, including determine indicator reliability, internal consistency, convergence validity, and discriminative validity verification. Secondly, this study evaluates the inner structural model, according to the testing suggestion proposed by (Hair et al. \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2014\u003c/span\u003e, Hair et al. \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), including assessment for collinearity issues, coefficients of determination, and Q\u003csup\u003e2\u003c/sup\u003e stone-Geisser. After passing the above evaluation, a path analysis will be conducted next. By path coefficient and the significance testing of the coefficient to determine the degree of correlation between the exogenous latent variable and the endogenous latent variables and whether the exogenous latent variables would significantly influence the endogenous latent variables. Meanwhile, to determine the existence of the mediation effect, the bootstrap method and variance account for were used for testing (Shrout \u0026amp;Bolger \u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e2002\u003c/span\u003e).\u003c/p\u003e\n\u003ch3\u003eIndicators and data preparation\u003c/h3\u003e\n\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eUrban form\u003c/h2\u003e \u003cp\u003eBased on the summary of existing studies and the framework of China's territorial spatial planning, this study defines the macroscale urban form as composed of geometric-aspect urban form and built environment-aspect urban form. Territorial spatial planning involves two types of planning focuses: space and land. The former focuses on spatial layout and spatial form features, while the latter focuses on land use in relation to human activities (Sun et al. \u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Therefore, this study defines urban form from two aspects: geometric dimension and built environment dimension (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn this study, urban form refers to the form of urban built-up areas. It should be noted that counties usually have the county town as the main center, consisting of several surrounding towns of different levels and sizes. Thus, it is more meaningful to discuss the impact of the main center and the overall composed of the main center and surrounding towns on the CER, respectively. First, as the core area of the county, the county town is a densely populated area for residents' activities and energy consumption and the main body of the county's development. Its urban form changes are more drastic, which will exerting a significant influence on the CER. Second, due to the interconnectivity and interdependence between the county town and other towns in many aspects, when exploring the impact on the CER as a whole, it can not only reveal the impact of the overall urban form of the county on carbon emissions but also reveal the impact of the spatial relationship between the county town and other towns on carbon emissions. This can provide a basis for the reasonable organization and arrangement of urban resources within the county under the low-carbon goal. Therefore, based on the research purpose and scope of indicator application, this article will select urban built-up areas of county town and total built-up areas within the county administrative boundary as the objects of urban form (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). For the main center of the county, this study refers and duplicates the methodology employed by Zhou et al. (\u003cspan citationid=\"CR113\" class=\"CitationRef\"\u003e2014\u003c/span\u003e) and Su et al. (\u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) for determination. A 30 m × 30 m moving window method was applied to identify high-density built-up areas of each county (to match the pixel size of land-use data), and then through manual visual comparison and analysis, the border was delineated with a distance of 1 km. Land within the border was considered the main center of the county.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe geometric-aspect urban form factors include urban scale, compactness, spatial structures, and urban shape, represented by typical landscape metrics. Landscape metrics have been extensively employed as a reliable approach for quantifying urban form, as they enable the description of the spatial composition and configuration of land cover (Huang et al. \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2021\u003c/span\u003e, She et al. \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). The reliability of the metrics selected in this study has been validated in many studies and is easy for urban planners to understand and implement. Compactness measures the degree of agglomeration development in the main center using the agglomeration index (AI) and patch density index (PD). With the development of urbanization, towns have developed rapidly, and the single center spatial structure of counties has begun to change. Thus, to describe the spatial structure of the county, the largest patch index (LPI) is selected to characterize the degree of a single center by measuring the proportion of the county town to the total area of the built-up area. The larger the value is, the larger the scale of the county town, and the higher the degree of a single center. The urban shape focuses on exploring the complexity of the boundary of the main center and the narrowness of the overall shape of the county, selecting the mean perimeter area ratio index (PARA-MN) and the mean-related circumscribing circle distribution index (CIRCLE-MN) to measure, respectively. The calculation of landscape metrics is based on the land-use data. All the landscape metrics, of which the detailed definition and formulas are provided in \u003cspan refid=\"Sec22\" class=\"InternalRef\"\u003eAppendix\u003c/span\u003e Table A6, were computed for each county by using Fragstats4.2 program.\u003c/p\u003e \u003cp\u003eThe built environment-aspect urban form factors include public service facilities, urban greening, road traffic, public transportation, and urban energy infrastructure. The layout balance and accessibility of public service facilities are represented by the coverage ratio of medical facilities and the coverage ratio of educational facilities. Urban greening is measured by the coverage ratio of green spaces. To measure the impact of transportation status within the county town on travel-related carbon emissions, this study selected road density and cycling lane density as indicators. The higher the values of these two variables, the more developed the road system of the county town. Unlike large city residents who commute mainly within the city, the frequency of travel between towns and the county town is usually higher. This study selects highway density to measure the status of highway traffic, and the larger the value is, the more developed the highway traffic system is. Similarly, to measure the impact of the construction level and convenience of long-distance public transportation and public transportation of the main center around travel-related carbon emissions, this study selects the density of bus routes and the coverage rate of public transport, respectively. Urban energy infrastructure is related to the energy structure. Only 4 counties of the YRD region currently use centralized heating for winter heating. Thus, this study does not include the heating system in the scope of energy infrastructure research and only considers the gas system for residential cooking and self-heating. The gas network density is selected to measure urban energy infrastructure. The quantification of all the above built environment-aspect urban form factors is based on the statistical data and Google Maps API data.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e\u003cdiv class=\"gridtable\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eThe exogenous latent variable and observable variable of the PLS-SEM\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDimension\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLatent variable\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eObject\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eObservable variable\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAdapted from\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eData sources\u003c/p\u003e \u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"5\" rowspan=\"6\"\u003e \u003cp\u003ethe geometric-aspect urban form factors\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUrban scale\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCounty\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTotal urban area (ha)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(Li et al. \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2021\u003c/span\u003e, Ou et al. \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2013\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\" morerows=\"5\" rowspan=\"6\"\u003e \u003cp\u003eLand-use data (30×30 m), (Resources and Environmental\u003c/p\u003e \u003cp\u003eSciences and Data Center, Chinese Academy of Sciences, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.resdc.cn/\u003c/span\u003e\u003cspan address=\"http://www.resdc.cn/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eCompactness\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eMain center\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAggregation index (%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(Fang et al. \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2015\u003c/span\u003e, Xia et al. \u003cspan citationid=\"CR97\" class=\"CitationRef\"\u003e2017\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePatch density (%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(He et al. \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2020\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSpatial structures\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCounty\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLargest patch index (%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(Li et al. \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2021\u003c/span\u003e, Ou et al. \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2019\u003c/span\u003e, Zheng et al. \u003cspan citationid=\"CR112\" class=\"CitationRef\"\u003e2023\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eUrban shape\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eMain center\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMean perimeter area ratio (None)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(Ou et al. \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2019\u003c/span\u003e, Shi et al. \u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e2020\u003c/span\u003e, Wang et al. \u003cspan citationid=\"CR91\" class=\"CitationRef\"\u003e2019b\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMean-related circumscribing circle distribution (None)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(Guo et al. \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2021\u003c/span\u003e, Zhang et al. \u003cspan citationid=\"CR106\" class=\"CitationRef\"\u003e2020a\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"8\" rowspan=\"9\"\u003e \u003cp\u003ethe built environment-aspect urban form factors\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003ePublic service facilities\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eCounty\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCoverage ratio of medical facilities (with a radius of 500 m) (%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(Zhang et al. \u003cspan citationid=\"CR107\" class=\"CitationRef\"\u003e2020b\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eGoogle Maps API data\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCoverage ratio of educational facilities (with a radius of 500 m) (%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(Zhang et al. \u003cspan citationid=\"CR107\" class=\"CitationRef\"\u003e2020b\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUrban greening\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMain center\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCoverage ratio of green spaces (%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(Chen \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2015\u003c/span\u003e, Tan et al. \u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e2021\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eChina County Construction Statistical Yearbook in 2019\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eRoad traffic\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCounty\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHighway density (km/km\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(Lei et al. \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2023\u003c/span\u003e, Xu et al. \u003cspan citationid=\"CR99\" class=\"CitationRef\"\u003e2022\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMain center\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRoad density (km/km\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(Ma et al. \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2022\u003c/span\u003e, Ou et al. \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2013\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMain center\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCycling lane density (km/km\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(Kyung-Hwan \u0026amp;Eun-Jeong \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2014\u003c/span\u003e, Wang et al. \u003cspan citationid=\"CR93\" class=\"CitationRef\"\u003e2018\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003ePublic traffic\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCounty\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDensity of bus routes (%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(Wei \u0026amp;Quan \u003cspan citationid=\"CR94\" class=\"CitationRef\"\u003e2021\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eGoogle Maps API data\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMain center\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCoverage ratio of public transport (with a radius of 300 m) (%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(Chen \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2017\u003c/span\u003e, Hou et al. \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2019\u003c/span\u003e, Wei \u0026amp;Quan \u003cspan citationid=\"CR94\" class=\"CitationRef\"\u003e2021\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUrban energy infrastructure\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCounty\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDensity of gas pipe network (km/km\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(Zhang et al. 2021b)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eChina County Construction Statistical Yearbook in 2019\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/table\u003e\u003c/div\u003e \u003cp\u003e\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eCarbon emissions of residents\u003c/h2\u003e \u003cp\u003eThe CER refers to the carbon emissions generated by direct energy use of residents, including residential building carbon emissions and travel-related carbon emissions (Zhang et al. \u003cspan citationid=\"CR109\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Limited by the availability of energy consumption data of the counties in China, this study refers to the method proposed by Yan et al. (\u003cspan citationid=\"CR101\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), Zhang et al. (\u003cspan citationid=\"CR106\" class=\"CitationRef\"\u003e2020a\u003c/span\u003e), and Wang et al. (\u003cspan citationid=\"CR87\" class=\"CitationRef\"\u003e2021a\u003c/span\u003e) as an alternative approach for estimating the CER. The method has been proven to estimate the CER in counties accurately. The residential building carbon emissions are estimated based on residents' gas consumption data (coal gas, natural gas, liquefied gas, coal), electricity, and heat (steam, hot water). The travel-related carbon emissions are estimated by converting the number of civilian motor vehicles, average driving distance, and average fuel consumption into transportation energy consumption. The energy consumption data of gas, electricity, and heat were obtained from the China County Construction Statistical Yearbook in 2019. The carbon emission coefficients of gas and heat were sourced from the China Energy Statistical Yearbook in 2015, the carbon emissions factors of the grid were derived from China's Regional Grid Baseline Emissions Factor Announcement in 2012. The data on the private car ownership came from the statistical yearbooks of counties in 2019. According to the results of the questionnaire and Guo's research (Guo \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2012\u003c/span\u003e), the annual mileage is established at 15,000 km, while the fuel factor is established at 8.6 L/100 km (Guo \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2012\u003c/span\u003e).\u003c/p\u003e "},{"header":"Result","content":"\u003ch2\u003eAssessment of the outer measurement model\u003c/h2\u003e\u003cp\u003eThe outer measurement model was employed to assess the reliability and validity of the observed variables. First, the indicator reliability was examined by the size of the outer loading. As shown in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, all the loadings were statistically significant (p \u0026lt; 0.01) and surpassed the threshold of 0.7, suggesting that the observed variable effectively represented the latent variable's intended meaning, and the present study satisfied the criteria of indicator reliability. Second, it was necessary to confirm the internal consistency and composite reliability of the PLS model by ensuring that the values of Cronbach's α and composite reliability (CR) above 0.70 (Sarstedt et al. \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). The results show that both the Cronbach's α and CR values surpass the established threshold for reliability. This study meets the criteria of internal consistency. Third, the model was checked for convergent validity using the average variance extracted (AVE). The findings reveal that the AVE values of the latent variables exceed 0.50 indicating that the PLS model has satisfactory convergent validity (Hair et al. \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Finally, the heterotrait-monotrait (HTMT) ratio was examined to ensure the discriminant validity through The results are shown in \u003cspan refid=\"Sec22\" class=\"InternalRef\"\u003eAppendix\u003c/span\u003e Table\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e. The HTMT values for each dimension are found to be lower than the acceptable threshold of 0.9 (Henseler et al. \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). This study satisfies the criteria of discriminant validity. Overall, the assessment of the outer measurement model is considered acceptable and can be further analyzed.\u003c/p\u003e\u003cp\u003e \u003c/p\u003e\u003cdiv class=\"gridtable\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eResults of model evaluation in the full model\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e\u003ccolgroup cols=\"7\"\u003e\u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDimension\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLatent variable\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eObservable variable\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLoading\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCronbach’s α\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eCR\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eAVE\u003c/p\u003e \u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"5\" rowspan=\"6\"\u003e \u003cp\u003ethe geometric-aspect urban form factors\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUrban scale\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTotal urban area\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eCompactness\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAggregation index\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.999\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.795\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.804\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.699\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePatch density\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.809\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSpatial structures\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLargest patch index\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eUrban shape\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMean perimeter area ratio\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.987\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.956\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.977\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.956\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMean-related circumscribing circle distribution\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.968\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"8\" rowspan=\"9\"\u003e \u003cp\u003ethe built environment-aspect urban form factors\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003ePublic service facilities\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCoverage ratio of medical facilities\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.781\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.793\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.864\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.761\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCoverage ratio of educational facilities\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.837\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUrban greening\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCoverage ratio of green spaces\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eRoad traffic\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHighway density\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.837\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e0.733\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e0.760\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e0.645\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eUrban road density\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.856\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCycling lane density\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.738\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003ePublic traffic\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDensity of bus routes (%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.771\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.773\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.869\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.689\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCoverage ratio of public transport (%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.819\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUrban energy infrastructure\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDensity of gas pipe network\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/table\u003e\u003c/div\u003e\u003cp\u003e\u003c/p\u003e\u003ch2\u003eEvaluation of the inner structural model\u003c/h2\u003e\u003cp\u003eTo evaluate the internal structural model, this study employed the measure of VIF to examine whether collinearity existed among the exogenous latent variables. The findings indicate that all VIF values are below the established threshold of 5, suggesting no collinearity issue in the model. Second, this study checked the coefficients of determination (R\u003csup\u003e2\u003c/sup\u003e), which indicated predictive accuracy or explanatory power for the endogenous construct. R\u003csup\u003e2\u003c/sup\u003e values of 0.25, 0.50, and 0.75 might be characterized as indicating small, moderate, and large, respectively. The results show that the coefficient of determination (R\u003csup\u003e2\u003c/sup\u003e) is 0.738, suggesting that the PLS model had moderate predictive power for the CER. Third, the predictive relevance was checked with Stone-Geisser's Q2 value. A Q2 value of 0.02, 0.15, or 0.35 signifies the extent to which an exogenous construct possesses a weak, medium, or large capacity to predict a certain endogenous construct. In this study, the Q2 a value that is 0.172 provides confirmation that the inner structural model exhibits a moderate level of predictive relevance. Finally, the acceptable range of the GOF value is 0–1. Within this range, values of 0.1, 0.25, and 0.36 correspond to minimum, medium, and large, respectively. (Akter et al. \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). The results indicate that the GOF value for the inner structural model is 0.56, surpassing the threshold of 0.36. The structural model is considered to have a better prognostic ability. Therefore, the present study met the overall model fit criteria of PLS-SEM and can be employed for further analysis.\u003c/p\u003e\u003ch2\u003ePathway analysis\u003c/h2\u003e\u003cp\u003eThis study employed PLS modeling to establish the pathways urban form affects the CER. Path coefficients (standardized beta), significance levels, and t values were calculated to evaluate the associations among the latent variables (Fig.\u0026nbsp;3). Overall, the path coefficient of the PLS model in this study is significant at least at the level of 0.05, except for the nonsignificant impact of public traffic on the CER. Specifically, regarding whether the geometric-aspect urban form factors will have a direct impact on the CER, the results show that the urban scale, the degree of a single center of spatial structure, and urban shape have a direct and positive impact on the CER. The path coefficients are 0.245 (p \u0026lt; 0.001), 0.171, and 0.270, respectively, indicating that the control of urban scale and the degree of single center, decreasing complexity and shape narrowness positively impact reducing the CER. In contrast, this study finds that the direct effect of compactness on the CER has a significant value of − 0.419 (p \u0026lt; 0.01), and the path coefficient is significantly higher than that of other geometric-aspect urban form factors. This indicates that increasing compactness will significantly reduce the CER. For the relationship between built environment-aspect urban form factors and the CER, road traffic status and energy infrastructure have a direct positive impact on the CER, with path coefficients of 0.570 (p \u0026lt; 0.001) and 0.456, respectively. However, public service facilities and urban greening have opposite effects on the CER, with path coefficients of -0.209 and − 0.270, respectively. This means that transportation status and energy infrastructure have significantly increased the CER, while urban greening and public service facilities have considerably reduced the CER.\u003c/p\u003e\u003cp\u003eIn addition, regarding the correlation between urban geometric characteristics and built environment characteristics, specifically, the urban scale will affect the proportion of urban green coverage (0.33), the completeness and accessibility of public service facilities (0.433), and the transportation status (0.367). Compactness directly impacts the proportion of urban green coverage (-0.289). The degree of a single center of the spatial structure will impact the transportation status (0.281). Urban shape will mainly impact the transportation status (0.201) and energy infrastructure construction level (0.362).\u003c/p\u003e\u003ch2\u003eMediation test\u003c/h2\u003e\u003cp\u003eThe variance accounted for (VAF) approach was used to determine the type of mediation effect. A VAF values more than 80% indicates full mediation, whereas a value less than 20% indicates no mediation effect. The 20% and 80% values indicate partial mediation (Jr \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). As shown in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, the results show ‘partial mediation’ among public service facilities, traffic status, and the proportion of green coverage between the urban scale and the CER. The indirect effects were 0.090, 0.209, and 0.089, respectively, with VAF values of 0.270, 0.461, and 0.267. Although they all show partial mediation, the mediating effect of traffic status was stronger. Compactness indirectly affects the CER through urban green space, with indirect effects of -0.060 and a VAF value of 0.216. A partial mediational effect of the proportion of green coverage is found between compactness and the CER. The complexity of urban form can indirectly affect the CER through traffic status (VAF = 0.297) and energy infrastructure (VAF = 0.241). The degree of a single center of spatial structure also has significant indirect pathways that affect the CER via traffic status. The indirect effects is 0.160, with VAF values ranging from 0.2 to 0.8, indicating that there were partial mediational effects.\u003c/p\u003e\u003cp\u003e \u003c/p\u003e\u003cdiv class=\"gridtable\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eResults of the mediation test\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRelationships\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIndirect effect\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003et-value\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eDirect effect\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eVAF\u003c/p\u003e \u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUrban scale\u003cb\u003e→\u003c/b\u003e Public service facilities\u003cb\u003e→\u003c/b\u003e CER\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.090\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.262\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.013*\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e0.245\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.270\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUrban scale\u003cb\u003e→\u003c/b\u003e Road traffic\u003cb\u003e→\u003c/b\u003e CER\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.209\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.48\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.031*\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.461\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUrban scale\u003cb\u003e→\u003c/b\u003e Urban greening\u003cb\u003e→\u003c/b\u003e CER\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.089\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.267\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.004**\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.267\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCompactness\u003cb\u003e→\u003c/b\u003e Urban greening\u003cb\u003e→\u003c/b\u003e CER\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.060\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.226\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.021*\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.219\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.216\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSpace structure\u003cb\u003e→\u003c/b\u003e Road traffic\u003cb\u003e→\u003c/b\u003e CER\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.160\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.161\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.042*\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.171\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.484\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUrban shape\u003cb\u003e→\u003c/b\u003e Road traffic\u003cb\u003e→\u003c/b\u003e CER\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.115\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.126\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.049*\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.27\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.297\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUrban shape\u003cb\u003e→\u003c/b\u003e Urban energy infrastructure\u003cb\u003e→\u003c/b\u003e CER\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.086\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.003\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.036*\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.241\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"6\"\u003eNote: *, **, and *** indicate significance at 10%, 5%, and 1%, respectively\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003cp\u003eSince public service facilities and road traffic contain multiple observation variables, further analysis is needed to determine the pathways. Based on the findings of the mediation test above, this study constructed five submodels to further analyze the more detailed relationship among urban geometric characteristics, built environment characteristics (public service facilities and road traffic), and the CER (Models 1–3). The results are presented in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e. In Model 1, both medical and educational facilities play a mediating role in the impact of urban scale on the CER. The expansion of the urban scale will significantly affect the coverage ratio of educational facilities, thereby affecting carbon emissions (-0.578***), while the ability to affect the CER through the coverage ratio of medical facilities is limited (-0.135*). As another essential mediating variable of urban scale on the CER, in the dimension of road traffic, urban road density, highway density, and cycling lane density all have a significant impact on the CER with urban scale expansion (0.354**, 0.011*, and − 0.302**, respectively). In Model 2, with respect to how spatial structure affects road traffic and thus affects the CER, this study finds that the degree of a single center only indirectly affects the CER by affecting the highway density (0.341*). In Model 3, regarding how urban shape affects road traffic and thus affects the CER, this study finds that complexity and shape narrowness indirectly affect the CER by affecting highway density and road density (0.187*** and 0.227**, respectively).\u003c/p\u003e\u003cp\u003e \u003c/p\u003e\u003cdiv class=\"gridtable\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eResults of the mediation test of submodels\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLatent variable\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eObservable variable\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eModel 1\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eModel 2\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eModel 3\u003c/p\u003e \u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003ePublic Service Facilities\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCoverage ratio of medical facilities\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.135*\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCoverage ratio of educational facilities\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.578***\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eRoad Traffic\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHighway density\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.011*\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.341*\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.187***\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRoad density\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.354**\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.194\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.227**\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCycling lane density\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.302**\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.170\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.156\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"5\"\u003eNote: *, **, and *** indicate significance at 10%, 5%, and 1%, respectively\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003cp\u003eModel 1 includes urban scale, public service facilities, road traffic, and the CER\u003c/p\u003e\u003cp\u003eModel 2 includes space structure, road traffic, and the CER\u003c/p\u003e\u003cp\u003eModel 3 includes urban shape, road traffic, and the CER\u003c/p\u003e"},{"header":"Discussions","content":"\u003cp\u003eBased on the analysis of the PLS-SEM, this study clarified and quantified the effects and pathways of the urban form (geometric characteristics and built environment characteristics) of counties on the CER. According to the results of pathway analysis, the impact of road traffic, energy infrastructure, and compactness on the CER is significantly higher than that of other urban form factors, making it the decisive factor in reducing the CER. In addition, the study also obtains several meaningful findings.\u003c/p\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eThe built environment-aspect urban form factors are direct factors affecting the CER\u003c/h2\u003e \u003cp\u003ePublic service facilities, urban greening, road traffic, and energy infrastructure are direct factors that affect the CER of counties. The completeness and accessibility of public service facilities directly impact travel-related carbon emissions, which is consistent with the research of (Wang et al. \u003cspan citationid=\"CR92\" class=\"CitationRef\"\u003e2021b\u003c/span\u003e) and (Yan et al. \u003cspan citationid=\"CR101\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) on counties. High-accessibility public service facilities can provide residents with more equal access to facilities, reducing travel distance and energy consumption. Urban greening has made a positive contribution to the CER. The reasons for this, on the one hand, green spaces have very effective carbon sequestration effects (Shen et al. \u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e2021\u003c/span\u003e); on the other hand, as important parts of urban green space, parks, and squares are similar to the medical and educational facilities in this study, accessibility also significantly affects travel-related carbon emissions (Ye et al. \u003cspan citationid=\"CR103\" class=\"CitationRef\"\u003e2015\u003c/span\u003e), with a negative correlation between the two. In addition, in this study, according to the survey results, 241 and 76 out of 420 respondents chose walking and cycling (electric bicycles) for their travel options to parks and squares, accounting for 75.5%. Green spaces affect residents' choice of transport modes (Xu et al. \u003cspan citationid=\"CR100\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) and promote active transportation (Xiao et al. \u003cspan citationid=\"CR98\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), which further reduces travel-related carbon emissions.\u003c/p\u003e \u003cp\u003eThe findings related to road traffic coincide with previous research and are an important driver for carbon emissions (Rasool et al. \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2019\u003c/span\u003e, Wang et al. \u003cspan citationid=\"CR90\" class=\"CitationRef\"\u003e2019a\u003c/span\u003e). Counties that are experiencing rapid urbanization, high-intensity land development, and road infrastructure construction will significantly increase the frequency of private car use. In addition, public transport systems in counties are typically underdevelopment, resulting in a significant reliance on private cars among local residents for their daily commuting needs (Guo et al. \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), further increasing travel-related carbon emissions. In this study, urban energy infrastructure is another key factor and has a significant positive driving effect on the CER. Studies conducted in large cities demonstrated that improving energy structures could effectively suppress the growth of carbon emissions from residential buildings (Zhang et al. \u003cspan citationid=\"CR108\" class=\"CitationRef\"\u003e2021a\u003c/span\u003e). However, for counties that are currently experiencing rapid development, although the condition of energy infrastructure will affect the type of energy used by residents and promote the use of clean energy, in the situation where the demand for domestic energy is not yet stable, the density of gas pipelines is positively correlated with the total energy consumption of residential buildings.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003eThe geometric-aspect urban form factors are crucial factors affecting the CER\u003c/h2\u003e \u003cp\u003eThis study finds that the geometric features of urban form (scale, compactness, spatial structures, and shape) can directly affect the CER and indirectly affect the CER through some built-up environmental feature elements. The geometric features of urban form play a crucial role in influencing the CER and are critical factors in mitigating the CER.\u003c/p\u003e \u003cp\u003eUrban scale expansion has increased commuting time, distance, and reliance on motor vehicles (Wang et al. \u003cspan citationid=\"CR90\" class=\"CitationRef\"\u003e2019a\u003c/span\u003e, Wang et al. \u003cspan citationid=\"CR91\" class=\"CitationRef\"\u003e2019b\u003c/span\u003e). Meanwhile, urban expansion often accompanies population growth and improving residents' living standards (Song et al. \u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e2020\u003c/span\u003e, Wang et al. \u003cspan citationid=\"CR90\" class=\"CitationRef\"\u003e2019a\u003c/span\u003e, Zheng et al. \u003cspan citationid=\"CR111\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), which increases energy consumption and corresponding carbon emissions. In addition, although one of the basic configuration methods for public service facilities is to layout them based on city size and administrative level, in this study, as the city size increases, the balance of the layout of medical and educational facilities will also be affected. This is mainly because the construction speed of public service facilities lags behind the speed of urban expansion (Fang et al. \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). The increase in the proportion of green coverage is related to the growth of the urban scale. Although urban development may occupy vegetation and ecological land in suburban areas and convert them into construction land, such as residential and infrastructure (Kong \u0026amp;Nakagoshi \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2006\u003c/span\u003e), urban expansion has the potential to create additional space for green space within the city. This can be achieved through various means, including the implementation of road landscape greening, the reconstruction of open spaces, and the establishment of green spaces within residential areas (Kong \u0026amp;Nakagoshi \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2006\u003c/span\u003e, Wang et al. \u003cspan citationid=\"CR85\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). This finding coincides with the empirical research conducted by Wang et al. (\u003cspan citationid=\"CR85\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) on the Yangtze River Delta region. Urban expansion will increase the density of cycling lanes. This may be due to the insufficient consideration of issues such as traffic operation efficiency and motorization development in road planning during the early stages of counties, resulting in unclear road functions and mixed use of motor and nonmotor vehicles. As urban areas continue to grow, there is a corresponding improvement in the road traffic system and an increase in the density of cycling lanes.\u003c/p\u003e \u003cp\u003eThe increase in the degree of a single center positively contributes to the CER, which is consistent with the concept of polycentric development advocated in a large city. The increase in the degree of a single center in counties will lead to a prominent phenomenon of uneven resource allocation between the main center (the county town) and the surrounding towns. Generally, the highly qualified educational and medical facilities are mainly concentrated in the main center, and residents in towns need longer travel distances and times to obtain high-quality resources, increasing travel-related carbon emissions (Guo et al. \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Meanwhile, an increase in the degree of a single center will increase the highway density. Under significantly insufficient employment opportunities and public service facilities provided by townships, residents are more inclined to choose long-distance commuting to and from the main center and residential areas, which will further increase travel-related carbon emissions. The study presented by Chen et al. (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2011\u003c/span\u003e) provides an example. They found that many newly built residential areas in Guangzhou are located far from the city center. However, there is a lag in the development of public service facilities in that location. The residents have to undertake extensive long-distance commutes between the city center and where they live.\u003c/p\u003e \u003cp\u003eIn this study, the negative correlation between the compactness of the main center and the CER corresponds to the perspective of compact development. This is consistent with the research conducted by Ou et al. (\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) on third- to fifth-tier cities in China. This is mainly because the main center can supply more socioeconomic functions, has less demand for automobile transportation, and has higher accessibility (Thinh et al. \u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e2002\u003c/span\u003e). In addition, the compact main center will affect the proportion of green coverage and thus affect carbon emissions. Intensive and high-density urban land development may occupy existing green space, leading to a decreased supply of urban green space (Lin et al. \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2015\u003c/span\u003e, Yao et al. \u003cspan citationid=\"CR102\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). This is consistent with the observed correlation between green spaces and high-density development in large cities in the 1990s (Jim \u0026amp;Chen \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2003\u003c/span\u003e, Kong \u0026amp;Nakagoshi \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2006\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eDecreasing the complexity and narrowness of the shape of the main center is also closely related to reducing the CER. The results are consistent with those of large cities (Chen et al. \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). Regular and nonnarrowness land use patterns can avoid unnecessary detours, reduce the travel time and distance of motor vehicles, and lower travel-related carbon emissions. In addition, irregular and complex construction land boundaries can increase the investment in infrastructure construction, forcing relatively inefficient laying methods for energy pipelines (Yeh \u0026amp;Li \u003cspan citationid=\"CR104\" class=\"CitationRef\"\u003e2001\u003c/span\u003e), such as affecting the layout of gas pipelines, pipeline laying length and density, thereby affecting energy transmission losses in buildings and increasing the CER.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003eLimitations and future directions\u003c/h2\u003e \u003cp\u003eThere are some limitations to this study. The first limitation involves the diversity of the research sample. This study only takes the YRD region as an example to discuss the relationship between the urban form of counties and the CER. Future research can conduct more cross-regional studies to avoid overlooking counties with different development trajectories and to fully understand the impact mechanism of urban form of counties on the CER. The second limitation is from the raw data of the CER. The raw data of the CER is limited by the quality of statistical yearbooks of counties. Although this study uses the results of a questionnaire containing data on travel characteristics as a complementary. The results may be inaccurate in residents’ perceptions of their travel characteristics inevitably. Future research can collect more travel characteristics data through multisource data mining to further improve the reliability of the raw data of the CER. In addition, due to the use of cross-sectional data from 2019 for the purpose of examining the correlation between the two, it is impossible to visualize individual differences due to time changes. Therefore, future research may consider examining long-term series data and employing dynamic simultaneous equation models to analyze this relationship further.\u003c/p\u003e \u003c/div\u003e "},{"header":"Conclusions and recommendations","content":"\u003cp\u003eThis study took 90 counties in the YRD region as the study areas and defined urban form from two dimensions: the geometric aspect and the built environment aspect. Then, the PLS-SEM was employed to explore the direct and indirect effects between urban form and the CER, and critical factors for reducing the CER were identified. The results mainly indicate that (1) the geometric-aspect urban form factors (scale, compactness, structure, and shape) will directly affect the CER. Controlling the urban development boundary, increasing the compactness of the main center, developing in the direction of multiple centers, and minimizing the complexity and narrowness of the shape of the main center have been found to result in important advantages in terms of the CER reduction. (2) The factors that reduce the CER of the built environment-aspect urban form include public service facilities, road traffic, urban greening, and energy infrastructure. Road traffic and energy infrastructure have a considerable negative effect on the CER, while improving the accessibility and completeness of public service facilities, as well as increasing the proportion of green coverage are beneficial for counties to reduce the CER. (3) The findings from the mediation effect analysis suggest the geometric characteristics of urban form will affect the built environment characteristics and thus affect the CER. Public service facilities, road traffic, greening spaces, and energy infrastructure are the influential mediators. (4) There are differences in the impact of urban form on the CER. Road traffic, energy infrastructure, and compactness are decisive factors in reducing the CER.\u003c/p\u003e\u003cp\u003eThis study confirms through empirical analysis that spatial planning is a crucial measure for counties to address reduce carbon emissions. Simultaneously, this study has found that both the geometric characteristics of urban form and the built environment characteristics have a stable impact on the CER. Nevertheless, there are differences in the effects and pathways. The following suggestions are proposed to optimization of urban form of county and achievement “dual carbon” goals.\u003c/p\u003e\u003cp\u003eFirst, planning strategies and policies should be designed with the objective of improving the compactness of the main center and controlling the expansion of the urban scale. Decision-makers should encourage the redevelopment of underutilized land, brownfields, and spare buildings within the main center to reduce the demand for land expansion. It has not only improved the efficiency of land use but also taken advantage of the centralization of various social and economic functions in the main center to reduce the traffic demand and dependence on vehicles. Meanwhile, urban land can be efficiently used to build small-scale green infrastructure, such as linear street green spaces and corner parks. Implementing vertical greening is another viable approach to alleviate the conflict between high-density urban development and environmental issues, such as green roofs, green walls, and vertical greening systems (Carvalho et al. \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2022\u003c/span\u003e, Liu et al. \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). These forms and functions of green spaces improve the overall green space coverage, increase the opportunities for residents to use green space (Fan et al. \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), and offset the negative impacts of compactness development (i.e., heat island effect and air quality) (Haaland \u0026amp;van den Bosch \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2015\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eSecond, the main center's scale should be considered, while promoting the counties to develop in the direction of multiple centers. The findings of this study demonstrate a significant positive association between the CER and the degree of a single center, and the development of multiple centers can help mitigate carbon emissions. However, unlike most large cities' balanced multicenter development strategy, counties with small urban scales and small populations need to concentrate resources and population to develop their economy and improve production and living efficiency. The effectiveness of urban development may be weakened by emphasizing on the balanced development of multiple centers. Thus, it is imperative to guarantee the scale of the main center. At the same time, public service facilities in each center should be improved to ensure that residents can meet their daily needs and avoid unnecessary long-distance travel. In general, this study provides evidence in favor of the contemporary phenomenon of multicenter development in Chinese cities.\u003c/p\u003e\u003cp\u003eThird, policy-makers should minimize the complexity and narrowness of the shape of the main center as the optimization direction for urban form, avoiding the negative impact on the CER. Due to terrain constraints, development suitability, and economic value costs in urban land development, the land's boundary and shape can be adjusted using blue and green space by adding green spaces and water bodies to form a continuous and complete landscape interface. It helps to form a regular road traffic network to avoid travel-related carbon emissions caused by unnecessary detours, congestion, and road twists and turns.\u003c/p\u003e\u003cp\u003eIn addition to policy recommendations based on the empirical results, although the effectiveness of public transportation in reducing the CER in counties is limited, according to relevant previous research, improving the public transport system is an inevitable choice for sustainable development. In particular, it is essential to build long-distance and interregional public transportation systems according to the practical travel needs among the main center, towns, and villages.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgments\u0026nbsp;\u003c/strong\u003eThis work was supported by the National Key Research and Development Project, China (Grant No. 2018YFC0704705). Besides, the authors would like to thank the anonymous reviewers for their valuable comments on this paper.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contribution\u0026nbsp;\u003c/strong\u003eRan Guo and Qing Yuan conceived and designed the methodologies and was the major contributor to writing the manuscript. Qing Yuan and Hong Leng offered funding to support the research and revised the paper. Shiyi Song made the validation and language editing. All the authors have read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eThis work was supported by the National Key Research and Development Project, China (Grant No. 2018YFC0704705).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e Not applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eNot applicable. This article does not contain any studies with human participants or animals performed by any of the authors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to participate\u003c/strong\u003e Not applicable. This article does not contain any studies with human participants or animals performed by any of the authors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e Not applicable. 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Sustainable Cities and Society 50. https://doi.org/10.1016/j.scs.2019.101646 \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":true,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"environmental-science-and-pollution-research","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"espr","sideBox":"Learn more about [Environmental Science and Pollution Research](https://www.springer.com/journal/11356)","snPcode":"11356","submissionUrl":"https://submission.nature.com/new-submission/11356/3","title":"Environmental Science and Pollution Research","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Urban form, Carbon emissions of residents, County, Structural equation model, Low carbon cities","lastPublishedDoi":"10.21203/rs.3.rs-3672227/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3672227/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eAccurate estimation of the impact of urban form on carbon emissions of residents (CER) is a crucial prerequisite for China to adopt effective low-carbon spatial planning strategies and achieve the carbon peak and neutrality goals (hereafter “dual carbon” goals). However, as the most basic governmental unit and the new energy consumer in China, counties have rarely been concerned, and the relationship between their urban form and the CER is still limitedly understood.This paper seeks to investigate how urban form influences the CER by taking the 90 counties in the Yangtze River Delta of China as a case study. First, considering the features of the county's urban form and residents' energy consumption, this study focused on analyzing the urban form of the main center and the overall urban form composed of the main center and surrounding towns. Second, based on the needs of sustainable development at county level and the framework of China's territorial spatial planning, this study defined urban form in two dimensions: geometric-aspect urban form and built environment-aspect urban form. Finally, the relationships between urban form and the CER were modelled using partial least squares structural equation model (PLS-SEM). Results reveal that (1) the geometric-aspect urban form variables (scale, compactness, spatial structures, and shape) exert a direct influence on the CER.Controlling the urban development boundary, increasing compactness of themain center, developing in the direction of multiple centers, and minimizing the complexity and narrowness of theshape of the main center positively impact the CER. (2) For the built environment-aspect urban form variables, improving the accessibility of public service facilities and green space coverage is beneficial for counties to reduce the CER, and currently improving road traffic status and energy infrastructurehas a significant contribution to the growth of the CER.(3) The geometric characteristics of urban form indirectly affect the CER by influencing the built environment characteristics of urban form. Road traffic, greening spaces, public service facilities, and energy infrastructureare influential intermediaries. This study identifies the intricate correlation between the county's urban form and the CER, providing a scientific foundation for the formulation of policies aimed at optimizing urban form and achieving “dual carbon” goals.\u003c/p\u003e","manuscriptTitle":"The influence of multidimensional urban form of counties on carbon emissions of residents and its planning implications: Evidence from counties in the Yangtze River Delta, China","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-01-02 14:24:43","doi":"10.21203/rs.3.rs-3672227/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Major Revision","date":"2024-05-13T05:43:55+00:00","index":"","fulltext":""},{"type":"reviewerAgreed","content":"","date":"2024-01-07T11:08:51+00:00","index":0,"fulltext":""},{"type":"reviewersInvited","content":"","date":"2023-12-23T11:25:52+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"Environmental Science and Pollution Research","date":"2023-12-15T17:26:58+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2023-12-04T04:52:24+00:00","index":"","fulltext":""},{"type":"submitted","content":"Environmental Science and Pollution Research","date":"2023-11-27T01:27:00+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"environmental-science-and-pollution-research","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"espr","sideBox":"Learn more about [Environmental Science and Pollution Research](https://www.springer.com/journal/11356)","snPcode":"11356","submissionUrl":"https://submission.nature.com/new-submission/11356/3","title":"Environmental Science and Pollution Research","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"bb39bfe5-13f2-4284-84a4-994b2a9d55fb","owner":[],"postedDate":"January 2nd, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2024-09-16T16:03:35+00:00","versionOfRecord":{"articleIdentity":"rs-3672227","link":"https://doi.org/10.1007/s11356-024-34836-z","journal":{"identity":"environmental-science-and-pollution-research","isVorOnly":false,"title":"Environmental Science and Pollution Research"},"publishedOn":"2024-09-13 15:57:46","publishedOnDateReadable":"September 13th, 2024"},"versionCreatedAt":"2024-01-02 14:24:43","video":"","vorDoi":"10.1007/s11356-024-34836-z","vorDoiUrl":"https://doi.org/10.1007/s11356-024-34836-z","workflowStages":[]},"version":"v1","identity":"rs-3672227","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3672227","identity":"rs-3672227","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}