Full Text

Turn on search term navigation

1. Introduction

Since the end of the 20th century, carbon emissions have received significant attention in academic circles due to global warming and climate change. Recently, carbon emissions were emphasized in the 2021 Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6), which listed “Human influence warms the atmosphere, oceans, and land” as the first title. The report pointed out that the concentration of carbon dioxide in the atmosphere (410 ppm) is the highest it has been in 2 million years. The global average temperature will rise by at least 1.5 °C by the middle of the 21st century due to anthropogenic emissions [1], which poses a significant threat to the survival of human beings and the sustainable development of the environment. In addition, 55% of the world’s population lived in cities as of 2018, and the global urbanization rate is expected to reach 68% by 2050 [2]. Cities consume approximately 75% of the world’s resources and generate more than 70% of greenhouse gases [3]; thus, cities are the main places where carbon emissions are generated. Urban form can be defined as a process with temporal and spatial boundaries, including the spatial organization and arrangement of human activities, and, at the same time, can affect the development and expansion of the city and the efficiency of resource allocation, land use, transportation, and infrastructure, and better promote city development [4,5]. Diverse research results on urban form and carbon emissions have been obtained by the authors from different disciplinary backgrounds and different countries. Most authors believe that urban forms play an essential role in improving urban land-use efficiency [6], compactness [7], population density [8], and energy efficiency [9]. Among these, land use [10], energy efficiency [11,12], and other factors are closely related to changes in carbon emissions. Although there are many studies on the correlation between urban form and carbon emissions, the previous review literature expounds the concept of urban form and the correlation between energy consumption, but few studies have discussed the influencing factors of urban form and carbon emissions [13], and some reviews have only discussed the impact of urban form on transportation carbon emissions [14]. This paper not only considers the carbon emissions of transportation, but also the impact of urban morphological factors such as the built environment and three-dimensional urban structure on the carbon emissions of buildings. It also summarizes some research methods commonly used. Thus, it is more systematic and comprehensive than previous reviews. In addition, some difficulties in previous studies have not been solved effectively: (1) Firstly, due to the diversity of indicators, the quantitative research on the impact of urban form on carbon emissions still needs to be further improved. (2) In addition, the impact of urban form on carbon emissions at different stages of development and different types of cities is unclear. (3) Finally, low-carbon urban development strategies still need to be enriched (e.g., the optimization of the land use, built environment, traffic network, and development pattern). These problems need to be solved urgently to improve the urban environment and achieve sustainable urban development, meanwhile, it will clarify the correlation between carbon emissions and urban form. Therefore, it is important to review previous studies and carry out a systematic bibliometric analysis to understand the future development directions.

2. Materials and Methods

2.1. Data

This study used the Web of Science (WOS) database as the literature data source to represent the global research situation. The WOS was selected, and the literature retrieved covered from 2002 to 2021 (data as of December 2021). The topics of the search were input as “#1:TS = urban spatial structure, urban structure, and urban form” and “#2:TS = carbon emissions and carbon neutral.” Article and review literature types were selected to obtain a total of 2439 retrieved results. Finally, the obtained literature data were exported in txt format.

2.2. Methods

Visualization tools can enable domain analysis for science and technology management by visually presenting data in graphs [15]. Moreover, they can provide multivariate and dynamic visualization analysis of data. Among these tools, Citespace and VOSviewer (VOS) are used widely [16,17]. To date, these tools have been used in the visual analysis of various disciplines, such as infectious disease prediction [18], medical data mining [19], global food security [20], urban resilience assessment [21], the development history of smart cities [22], and geographic information system (GIS) knowledge fields [23]. In this study, VOS and Citespace were the primary tools to analyze and visualize the core contents with their powerful literature analysis functions. Furthermore, based on bibliometric analysis, a reading of highly cited literature was carried out to assess and summarize the main research content and trends related to the current global urban form and carbon emissions.

3. Results

3.1. Overall Results

Figure 1 shows the evolution of published papers on urban form and carbon emissions during the 20 years of 2002–2021. Over time, the growing tendency indicates that more and more scientific research on urban form and carbon emissions has been conducted, and two stages can be identified as follows:

3.1.1. Development Phase (2002–2012)

During this stage, global researchers produced books, reports, and many empirical types of research on urban form and carbon emissions. Each year, 5 to 10 additional articles were published, indicating that urban form became an essential factor affecting carbon emissions [24,25,26,27]. In addition, the Kyoto Protocol came into force in 2005, and greenhouse gas emissions were regulated for the first time in human history, contributing to the growth in research output.

3.1.2. Rapid Development Phase (2013–2021)

Since 2013, the number of articles on urban form and emissions increased promptly, and more than 1959 articles published during this period. The World Climate Change Conference organized in Doha in 2012 emphasized the importance of implementing the Kyoto Protocol II commitments. Moreover, scholars from all over the world began to pay attention to the influence mechanism of urban form on carbon emissions at different scales and the possibility of using the characteristics of urban form to reduce carbon emissions [28,29,30,31,32].

Figure 2 and Figure 3 show that the developed countries in Europe and the United States had the highest number of papers published in the early stage. In recent years, the research of emerging economies such as China and India has increased rapidly. A comparative analysis shows that developed countries in Europe and the United States have a relatively high level of urbanization and have made progress in estimating and influencing carbon emissions related to urban form. However, the urbanization levels of countries in Asia, South America, and Africa are increasing rapidly, and the contradiction between the development of urban form and carbon emissions is becoming increasingly prominent.

The distribution of journals shows that the Journal of Cleaner Production has the highest number of articles (160), followed by Atmospheric Environment and Sustainability (109), and the other journals account for less than 100 articles. In addition, when classifying the subjects of the WOS database (Table 1), it was found that the research is concentrated in the fields of ecological and environmental science (81.87%), energy and fuel (42.63%), meteorology and atmospheric science (41.41%), and engineering (38.68%); because a document may belong to multiple categories, the total is more than 100%. The distribution of subject categories suggests the field’s ecological, geographical, atmospheric, and environmental issues were prioritized. Moreover, urban form and carbon emission research became more interdisciplinary over time.

3.2. Keywords Co-Occurrence Network

In this study, VOSviewer was used to analyze author keywords in the data, and the occurrence frequency threshold was set to 5. In addition, the result generated a total of 220 keywords and the keyword cluster atlas was drawn (Figure 4). The keywords are divided into 12 clusters, and we mainly consider those associated with the carbon emissions of urban settlement areas, because urban settlements make a large contribution to carbon emissions. The results in Figure 4 and Figure 5 suggest that there are five core research topics that are closely relevant: carbon emission, urban form, sustainable, land use, and energy efficiency.

Citespace was used for keyword emergence analysis, which found that the keywords with a high burst value related to the topic of this paper are carbon element, city, and particle. Furthermore, their emergence values are all over 10, indicating that these keywords have become research hotspots over a certain period (Figure 6). Overall, three stages of emergence from 2002 to 2021 were identified. The nascent stage appeared around 2002. In this stage, carbon element and atmospheric environment were the main research topics [33,34]. The development stage appeared around 2004, during which city became an essential direction of carbon emission research, and research on urban form and carbon emission continued to flourish. The mature stage appeared around 2019. The main keywords in this stage were Carbon dioxide (CO₂) emissions, empirical analysis, and optimization, indicating that the research on urban form and carbon emissions gradually matured, and focused on the influence factors such as built environment, traffic, and land use.

3.3. Document Co-Citation Network

The Figure 7 shows the document co-citation network (DCN), which consists of 1041 cited references and 4742 co-citation links between 2002 and 2021. The silhouette scores of the most crucial 10 clusters are all above 0.8, which mean the result of the figure are credible (Table 2). The largest cluster—#0 Secondary organic aerosol formation and #1 Urban form—contains 133 member references, and is thus slightly more extensive than the others. In conclusion, with the change of the atmospheric environment, urban form and city size have become the main focus in the research field of urban carbon emissions.

Table 3 shows the ten papers with a total cited number of more than 46. Among these ten papers, nine belong to clusters #1, #2, and #4, indicating that the research perspective in these papers mainly focuses on the impact of urban form on carbon emissions. Panel data analysis is a popular method used to explore the relationship between urban form and carbon emissions. According to the statistics of authors with high co-citations, Dhakal ranks first with 73 co-citations. He analyzed urban carbon emissions at the national, meso-, and micro-scales in 2009 and found that cities accounted for 84% of China’s commercial energy use. Thirty-five significant cities in China account for 18% of the total population; however, they account for 40% of China’s energy consumption and CO₂ emissions, which provides a scientific basis for future research on urban carbon emissions [35].

3.4. Research Methods

3.4.1. Accounting Methods of Carbon Emission

In the process of reading the literature in this paper, we found that some scholars believe that the accounting boundary of carbon emissions is mainly classified according to the production side, the consumption side, and the “emission raising” [36]. Other scholars have proposed more specific accounting boundaries, namely, the geographic boundary, the temporal boundary, the activity boundary, and the life cycle boundary [37]. These boundary theories are the basis of the carbon emission accounting method, in which we take into account the differences in the scale of urban form research, and the urban carbon emission accounting methods are divided into the direct method, indirect method, and measurement method.

The direct method is suitable for accounting for carbon emissions in large cities or cities with a high degree of development. The principle of the direct method is based on the emission inventory provided by IPCC [38]. The carbon emission factors and conversion coefficients of different types of energy are multiplied by a city’s energy consumption to obtain the corresponding carbon emissions. The use of the direct method depends on the availability and integrity of energy data. At the same time, large or highly developed cities have relatively complete data statistical systems and high data openness, which facilitates the use of the direct method [39,40,41,42,43].

The indirect method is generally used in small and medium-sized cities or less-developed regions. The indirect method is mainly based on proxy data (e.g., nighttime light (NTL) and land use), transforming carbon emission data into large spatial units and distributing them onto a spatial grid for estimation [44]. With the development of GIS and remote sensing (RS) in recent years, NTL data have become the primary proxy data in regional carbon emission accounting [45,46]. The Defense Meteorological Satellite Program’s Operational Line-scan System (DMSP-OLS) introduced in 1992 provides the most widely used NTL data [47,48,49]. Studies have shown that economic development positively correlates with carbon emissions.

In contrast, economic development positively correlates with the level of urbanization. Further, the level of urbanization is positively correlated with a city’s stable light intensity, so that NTL can well represent a city’s carbon emissions level. At the same time, the availability of energy consumption data in small and medium-sized cities is low, and the direct method is challenging to be used. Therefore, as an important source of proxy data of the indirect method, NTL is suitable for accounting for carbon emissions in small and medium-sized cities.

It is suitable to adopt an actual measurement method when conducting small-scale carbon emission accounting, such as in communities or parts of cities. The measurement method mainly allocates emissions to designated spatial units by collecting and organizing data at the point or sector level [50,51]. Researchers in the UK used low-cost portable CO₂ sensors to monitor and estimate CO₂ dispersion in the old city of Edinburgh [52]. Zhang et al. (2018) attempted to link greenhouse gas emissions measured at the sectoral level with spatial land use, enabling spatial visualization at different scales such as districts or streets [53]. Compared with the previous two methods, the carbon emission data obtained by the measurement method is more accurate. However, it requires more workforce and material resources, so selecting the method should align with practical needs.

According to the current status of the research literature, it is obvious that these three methods provide particular convenience for accounting for urban-space carbon emissions. However, there are still some problems with the current accounting methods. Although the direct method is convenient for accounting, it has a limited scope of application and a high degree of dependence on energy consumption data; it needs to be implemented with a corresponding and accurate statistical system, and there is still controversy over the fairness of carbon accounting for energy-producing cities. Notwithstanding the fact that the indirect method improves the calculation efficiency, it is limited by low spatial resolution and oversaturation of solid light in urban areas. Furthermore, although the estimation results of the measured method are more accurate than those of the first two methods, this accuracy is dependent on local relevant data and the cost of data collection is high [54]. Therefore, improving the applicability and optimization of accounting methods is the main goal for the future.

3.4.2. Methods to Explore the Correlation between Urban Form and Carbon Emissions

The current research methods of urban form and carbon emissions can be mainly divided into spatial methods and non-spatial methods. Spatial methods mainly use geographic information or spatial distribution data to explore the spatial differentiation and spillover effect or the impact mechanism of carbon emissions, and include geographic weighting models, spatial autocorrelation models, and GIS spatial analysis [55,56,57]. For example, Wang et al. used a spatial autocorrelation model to explore the spatial spillover effect and driving force of China’s urban carbon emissions [58]. Furthermore, non-spatial types of data (e.g., economic status, population density, and energy consumption) is frequently used to identify the impact of different factors of urban form on carbon emissions in the non-spatial methods, which include linear or the nonlinear correlation models, panel data models, and index decomposition methods (e.g., the Kaya identity model and STIRPAT model) [53,54,59,60]. For example, Yang et al. found that China’s urban form and traffic factors have a significant impact on per capita carbon dioxide emissions through a panel data model [61]. Van et al. used a multiple linear regression model to study the impact of the built environment on residents’ travel carbon emissions, and found that the average travel time was negatively correlated with rail transit density [62].

With the improvement in data acquisition capabilities and the development of technology, new methods are constantly emerging. To avoid the negative consequences of multicollinearity between explanatory variables and improve the accuracy of the results, the random forest regression model based on machine learning is widely applied in research. Lin et al. found through a random forest regression model that the degree of spatial crowding plays a dominant role in the variation of CO₂ emissions [63]. In short, there are many types of research methods for examining the correlation between urban form and carbon emissions, which need to be appropriately selected according to different needs to ensure the validity of the results.

3.5. Impacts of Urban Form on Carbon Emissions

As a social and technological system, urban form significantly influences CO₂ emissions [64,65]. This paper finds that the study of urban form and carbon emissions can be roughly divided into two types of literature: (1) analyses of the influencing factors of CO₂ emissions through quantitative research on land- use change, built environment, transportation, and a development model of the urban form; and (2) proposing new methods and strategies to accelerate the development of low-carbon cities by exploring the mechanism of different influencing factors of urban form.

3.5.1. Land Use

As the spatial carrier of urban activities, the land is an essential manifestation of urban spatial structure and form. Land-use research mainly considers type, scale, efficiency, structure, and management. According to land-use patterns and industrial types, land-use types are divided into agricultural land, industrial land, commercial and residential land, transportation land, and other land [66]. Once the land-use type changes, it directly affects carbon sequestration in terrestrial ecosystems and indirectly affects anthropogenic carbon emissions [67,68,69]. It is generally believed that the carbon sink will decrease with the transformation of agricultural land (forest land, grassland, farmland) to construction land (industrial land, commercial land, residential land, transportation land) [70,71]. Furthermore, urban expansion will lead to fragmentation and irregularity of various land-use patches in cities, which will make the urban form highly complex, and economic activities will be scattered into different pieces, resulting in more carbon emissions [72,73,74,75,76]. In addition, the level of land-use efficiency, the benefits and drawbacks of structure, and the implementation of land-management policies will also have varying degrees of impact on urban carbon emissions [77,78,79]. Huang et al. (2013) explored the scale and spatial distribution of various types of land by building a regression model and found that the concentrated distribution of industrial land may reduce the carbon emission intensity [80]. However, the concentrated distribution of the other four land-use types will promote carbon emissions.

With the deepening of research, land-use structure and land-management policies are considered the main land-use influencing factors affecting carbon emissions [81]. Consequently, the emission reduction strategy of land use should optimize the land-use structure and land management. There are differences in the ability of different types of land to generate and absorb CO₂. Their functional attributes and structures significantly affect urban residents’ travel mode and energy consumption. In order to optimize the land-use structure, the first step is to promote high-carbon source land to high-carbon land change. Researchers have explored the carbon sinks of urban parks in different countries and found that urban parks can offset 2.3 to 3.6% of their annual greenhouse gas emissions [82,83,84]. Therefore, increasing the proportion of carbon sinks such as parks and green spaces in cities will help reduce the pressure of emissions there. The next step is to strengthen the mixed use of land. Studies have suggested that mixed-use land can reduce the travel distance and shorten the travel time of residents, thereby changing the way residents travel to achieve the effect of emission reduction [85,86,87,88,89]. The mixed use of land functions includes horizontal and vertical two-dimensional mixing. Horizontal mixing refers to low-density areas with residential, commercial, and green spaces, which are usually used on a small scale to create a comfortable small environment, while vertical mixing refers to the vertical space of high-rise buildings. As an example of a mixed-use high-density area, the lower level of a building could be the commercial area, the middle level an office area, and the upper level a living area. Additionally, it is also crucial to formulate a sound land-management system, improve the development efficiency of other types of land, spatially achieve sustainable and balanced development, and avoid the imbalance of land-use structure to reduce the burden of carbon emissions. In summary, in-depth quantitative research should be continued in the future to explore better solutions for land use and to build urban land-use patterns and urban forms that are comfortable and sustainable for living.

3.5.2. Built Environment

In terms of the built environment, numerous indicators have been used by different researchers in recent years. The 3D indicators (Density, Diversity, and Design) proposed by Ewing and Kockelman are widely accepted [90]. Subsequently, other indicators have emerged, such as communities, public transport service levels, and management needs. In this paper, the three elements of density (building, population), function (land property, mixing degree, management needs), and form (three-dimensional height, body-shape coefficient, orientation) are used to assess travel behavior, car ownership, and building energy consumption to explore the relationship between the built environment and carbon emissions. Density factors include building density and population density, which are generally believed to be negatively correlated with carbon emissions [79,91,92]. In a city’s central area, the density of buildings and population is large, which, combined with many employment opportunities and developed infrastructure and public transportation, reduces the scope of activities for residents to meet their work and living needs. Therefore, they will adopt a lower-carbon travel mode to reduce per capita energy consumption and emissions [93,94,95]. The functional elements include the nature of land use, land mixing degree, and managing demand. The functions of construction land are different. The corresponding building energy demand of buildings with different functions is different due to different use contents and intensity. The energy consumption of industrial land is generally higher than that of commercial and residential land [96,97]. In addition, the higher the mixing degree of buildings with different functions, the lower the carbon emissions [98]. Management needs usually involve the distance from the residence to the parking lot, the number of parking lots, and the level of parking services. Providing low-cost parking lots in the community will promote increased car ownership, increasing residents’ dependence on cars and travel carbon emissions [99,100]. The morphological elements mainly include the three-dimensional urban structure and height, shape coefficient, and orientation of the building.

In recent years, urban buildings have continued to develop vertically. Although the compact distribution of buildings on the two-dimensional scale helps reduce CO₂ emissions, it may lead to more CO₂ emissions due to the dense and crowded patterns of urban structures in three-dimensional space, because high-density and high-rise urban three-dimensional structures reduce the air flow, sunlight, and ventilation capacity [101,102,103], leading to aggravation of the urban heat island phenomenon [104]. Furthermore, the power consumption of lighting and air conditioning will also increase significantly. Therefore, Lin et al. promote high-rise and low-density urban three-dimensional structures to reduce CO₂ emissions [63]. Furthermore, although high-rise buildings are conducive to increasing living density and reducing per capita carbon emissions, buildings of different heights change the morphological heterogeneity and vertical roughness of urban landscapes, leading to increased energy consumption and CO₂ emissions [105,106,107,108]. The building-shape coefficient refers to building surface area to volume. Cho (2002) found that the building-shape coefficient was positively correlated with carbon emissions when other conditions did not change [109]. Some researchers have also discussed the orientation of buildings. Different orientations mean different sunlight conditions, which directly affect the absorption or reflection of solar radiation on the building surface. Krüger et al. (2010) conducted a simulation study on the impact of building orientation on the carbon emissions of construction land in Israel’s arid climate and found that the north–south orientation of the plots can produce shading effects between buildings, thereby reducing carbon emissions [110]. Therefore, the built environment’s impact on carbon emissions is not a single factor but should be comprehensively considered based on multiple factors.

By considering the factors affecting the carbon emissions of the built environment, studies have shown that workplace density plays a more significant role in reducing CO₂ emissions than that of the home [111]. Therefore, reasonably increasing the building density of workplaces is one of the measures that contributes to low-carbon development. Secondly, based on the self-selection effect, residents could choose a built environment suitable for themselves according to their preferences [112,113], allowing residents who like to walk to choose walkable neighborhoods. Increasing walking frequency [114] allows residents with sustainable consumption concepts to live in communities close to public transportation and to reduce car ownership. In addition, it is necessary to pay attention to the reasonable layout and planning of parking lots, reduce unnecessary waste of land resources, and reduce the frequency of residents’ private car use. Finally, the driving effect of policy factors on various functional facilities of the urban built environment should also attract attention to reduce the built environment’s impact on cities’ carbon emissions from multiple perspectives and accelerate the construction of a low-carbon city.

3.5.3. Transportation Networks

Examination of transportation networks mainly considers accessibility (destination accessibility, bus station accessibility), connectivity, road network density, and road design and hierarchy. Accessibility, connectivity, and road network density are related to whether a city has a developed road traffic network and a complete public transportation system. These factors affect transportation carbon emissions by changing the urban traffic environment (vehicle speed, congestion, air quality) and residents’ travel behavior (travel range, car ownership, and use) [115,116,117]. The accessibility of the destination generally includes the distance from the residence to the central business district (CBD) and work accessibility. The closer distance to the CBD or the workplace, and the lower driving mileage reduce the number of family vehicles [118,119,120,121].

Similarly, the distance between households and public transportation stations will also affect car ownership. Potoglou et al. (2008) found that, if there is a public transportation station within walking distance of residences [122], household car ownership will decrease. One study showed that a 10% increase in public transport accessibility can reduce travel-related greenhouse gas emissions by 5.8% [123]. Furthermore, some scholars have proposed that the accessibility of green spaces and water spaces can also reduce CO₂ emissions related to residential energy consumption [124]. It is generally believed that connectivity and road network density negatively correlate with carbon emissions. Studies have shown that street connectivity positively correlates with low-carbon travel modes such as walking and cycling. Therefore, higher connectivity means fewer carbon emissions.

Furthermore, many studies on road network density have been carried out. Hong et al. (2014) found that if the intersection density in urban areas increases by 100%, the carbon emissions of transportation can decrease by 31.2–34.4% [125]. Wang et al. (2017) described the coupling relationship between urban spatial structure and traffic organization through road network density and the traffic coupling factor [126]; they found that a higher road network density can enhance the accessibility and connectivity of urban traffic and improve the efficiency of urban transportation energy consumption by increasing the speed of vehicles and reducing traffic congestion. In addition, the design and structure of roads also affect carbon emissions. Studies have shown that designing streets suitable for walking or cycling for community residents will help them to adopt more “low-carbon” travel modes [127]. Agarwal et al. (2018) proposed a bicycle-only highway scheme in Patna, India. After implementing this infrastructure, the share of bicycle trips increased from 32 to 48%, effectively reducing transportation carbon emissions [128].

3.5.4. Development Patterns

Sprawl development and compact development are two key development modes in urbanization. Sprawl development reflects the one-time development of cities with high dispersion and low density. In contrast, compact development reflects the spatial density of cities (population, housing, employment) and the concentration of infrastructure [129], which indirectly affect the level of carbon emissions in cities and residents’ daily lives. The general view is that guiding the transformation of cities from disorderly sprawl to high-density and compact development is conducive to carbon emission reduction [74,130]. Sprawl development is a kind of incremental development. The scale of a city continues to increase with the development of economic and travel tools. Such extensive expansion will cause harm to the environment and the development of the city. Further, the expansion will occupy a large amount of natural green space, reduce natural carbon sinks, and result in longer commuting distances and greater commuting flow, thereby increasing urban energy consumption [131,132]. At the same time, urban sprawl will lead to longer infrastructure service lines, resulting in massive demand for infrastructure investment. As a kind of stock development, compact development aims to slow the trend of urban sprawl and carry out intensive planning of urban form. It is generally believed that compact development is negatively related to carbon emissions [72,73,133] because a compact urban form helps reduce a city’s erosion of the surrounding ecological environment and dependence on private cars. Improving the compactness of urban functions and forms, promoting the sharing of resources, services, and infrastructure, reducing land occupation by repeated construction, and reducing the energy and resource costs of urban operation are conducive to the sustainable development of a city. However, some scholars hold the opposite view, arguing that the benefits of compact development are not enough to make up for the adverse effects caused by it [108,134], such as road congestion, air pollution, low greening rate, and the heat island effect. Rong (2006) proposed a U-shaped relationship between compact development and energy consumption, with benefits increasing as density increases and decreasing as the urban heat island effect stimulates energy demand [135]. Although there is still controversy over the concept of compact development, this paper believes that the future direction is not only developing compact cities but how to develop more affordable and efficient compact cities

Due to the rigid needs of different types of urban development, sprawl and expansion are sometimes inevitable, but compact development is still the main direction. The two modes work together to form the complex urban form (single-center, multi-center), and proper planning of the development model is crucial for low-carbon development. Firstly, the development of compact cities is necessary. However, in the development process, attention should be paid to ensuring that the functions and forms of cities are compact, which is conducive to the complementarity between different functional units of urban space and reduces the commuting volume of urban residents [136]. Secondly, it is necessary to comprehensively consider the problems existing in single-center and multi-center development models [74,75,137] such as traffic congestion, poor greening, and high housing prices caused by single-center development [72,73]. Furthermore, multi-center development weakens the benefits created by public transportation, increasing infrastructure investment [138]. Based on these issues, we should explore a combination of the single-center development and multi-center development in the future. i.e., a balanced state to improve residents’ living experience while reducing energy consumption. In addition, the impact of urban development models in cities at different development stages varies. In order to promote low-carbon development, it is necessary to pay attention to adopting appropriate development models according to the constraints of cities at different development stages [139]. In a mega-city with a relatively high level of development, the development environment is the main restrictive factor (e.g., greenery coverage). It is necessary to focus on constructing various green spaces in compact development. Living consumption is the main limiting factor for coastal cities with a medium level of low-carbon development. Therefore, in a compact design, it is necessary to fully regulate residents’ consumption and cultivate a low-carbon lifestyle. For inland cities with a low level of low-carbon development, there are constraints on economic development and social progress, so further optimization of industrial structure and improvement of energy utilization efficiency must be considered in urban development planning [140,141,142].

To summarize, the urban form includes main four aspects: land use, built environment, transportation network, and development pattern, which have direct or indirect effects on CO₂ emissions. However, the following problems also exist. Firstly, due to the complexity of influencing factors and the difference in the selection (e.g., land-use policy and development mode), a comprehensive systematic analysis framework and standardized index system have not been formed. Secondly, the current empirical research scales are concentrated in the urban dimension or the regional category, and there are few studies at the micro-scale (e.g., the community scale). Empirical research in different dimensions needs to be enriched. At the same time, although measures such as improving energy utilization efficiency and optimizing land-use structure have had positive effects on carbon emission reduction, they cannot fundamentally solve the problem of massive greenhouse gas emissions. Therefore, it is of great value to continuously reveal the potential impact factors of CO₂ emissions while comprehensively considering the urban spatial form, improving traffic efficiency, predicting the spatial distribution trend of CO₂, and examining other optimization aspects. The built environment is a city’s backbone, and the transportation network represents its veins. Therefore, based on reasonable land use and the development model, we should first focus on building a comprehensive standardized index system. Next, carbon emission reduction strategies should be promoted while optimizing planning decisions to avoid weakening or offsetting benefits. Finally, all of the suggestions in this paper are considered to be of great significance to the realization of healthy and low-carbon development of cities.

4. Conclusions and Discussion

4.1. Conclusions of the Current Situation

This paper presents a visualization analysis of knowledge map of the correlation research between urban form and carbon emissions using the bibliometric method. It summarizes the characteristics of published papers, keywords, and co-citations. In addition, this paper examines the trends of popular research and provides an overview of current research. The conclusions of the paper include the following aspects:

(1). The research on the correlation between urban form and carbon emissions shows, overall, continuous growth in the total amount of literature, which can be divided into two development stages: the “development phase” and the “rapid development phase.” In addition, carbon emissions are both a global and a regional issue. The process of urbanization occurred earlier in developed countries, and the United States and Europe paid earlier attention to the urban ecological environment. With the deepening of economic globalization, developing countries begin to pay increasing attention to the urban environment.
(2). Through the visual analysis of keywords, it was found that those with high popularity are carbon emission, urban form, sustainable, land use, and energy efficiency. In addition, the research can be divided into three stages of “budding-development-maturity” according to the degree of emergence.
(3). The impact of urban form on carbon emissions is a complex process of multiple factors. First, land-use structure, land-use efficiency, and land-management policies are the main land factors affecting carbon emissions. Second, the built environment affects carbon emissions mainly through the combined action of three elements: density, function, and form. Furthermore, traffic accessibility, connectivity, and road network density can also affect transportation carbon emissions by changing the urban traffic environment and residents’ travel behavior. Furthermore, urban development models have different impacts on carbon emissions of cities at different development stages, but compact urban development models are the main development direction.

4.2. Discussion

Based on the current research and practice on the correlation between urban form and carbon emissions, many studies have analyzed the correlation between their different factors., but lack a systematic research framework, an evaluation system, and micro-analysis. The urbanization process of most developing countries is still advancing rapidly. Determining how to optimize the urban form, promote energy conservation and emission reduction, and accelerate the development of low-carbon cities still faces severe challenges.

4.2.1. Build a Systematic Research Framework and Evaluation System

With the continuous increase in the urban population and the rapid development of the urban form, the methods to study the impact of urban form and carbon emissions have become complex and diverse. Some researchers have analyzed them through indicators such as accessibility and spot coverage. In contrast, others have used indicators such as production specialization and mobility, all of which have obtained credible results. However, the lack of systematic analysis, poor standardization of evaluation indicators, and the low availability of open data are related to the complexity of factors and the privacy of required data. Therefore, considering the requirement of low-carbon city construction in the future, it is necessary to build a systematic research framework to analyze the correlation between urban form and carbon emissions. Furthermore, it is also necessary to standardize the indicators and data to improve the research on urban carbon emissions, and the theory will be more comprehensive [143,144,145].

4.2.2. Microscopic Research

It can be found in the existing literature that the research scale of most works on the impact of urban form and carbon emissions is concentrated at the provincial level or the level of multiple urban agglomerations. However, few literature studies have examined the micro-dimension of cities, such as the street level or the community level. Although related research (zero-carbon community construction) has received attention in recent years, it is still necessary to explore the impact mechanism of urban form and carbon emissions at the micro-level. The joint development of macro-scale and micro-scale research reveals the impact mechanism more scientifically and effectively.

4.2.3. Method Optimization

With the development of RS and GIS, carbon satellites have gradually become a more applicable monitoring and accounting method. Although there are still deficiencies in the current technology, with the continuous development of the Global Carbon Assimilation System, assimilating satellite monitoring data and ground measurement data to reduce data errors can promote the rapid development of research on urban carbon emissions in the future. In addition, it is necessary to explore the combinations of new methods in the computation field, such as machine-learning algorithms, deep learning, and big data, with traditional analysis methods, to improve the efficiency and accuracy of analyses and accelerate the construction of sustainable urban forms that will meet various challenges in the future and promote the low-carbon development of cities.

Author Contributions

Conceptualization, C.S. and S.W.; methodology, Y.Z. and W.M.; software, Y.Z.; validation, Y.Z.; formal analysis, Y.Z.; investigation, C.S.; resources, C.S.; data curation, Y.Z.; writing—original draft preparation, Y.Z.; supervision, S.W. and R.W.; project administration, S.W.; funding acquisition, C.S. and R.W. All authors have read and agreed to the published version of the manuscript.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

Data is contained within the article.

Conflicts of Interest

The authors declare no conflict of interest.

Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Figures and Tables

Figure 1. The number of global publications on urban form and carbon emissions (2002–2021).

Figure 2. Stacked area charts of global publications (Top 10).

Figure 3. Spatial distribution of global publications.

Figure 4. Keywords co-occurrence network.

Figure 5. Keywords hotspot map.

Figure 6. The top 25 keywords with the strongest citation burst words for the WOS. The data for this figure come from Citespace.

View Image - Figure 7. The document co-citation network. The '#' and number indicate the serial number of the clusters with the largest to the smallest number.

Figure 7. The document co-citation network. The '#' and number indicate the serial number of the clusters with the largest to the smallest number.

Table 1

Subject classification of the Web of Science.

Subject Classification	Frequency	Proportion
Environmental Sciences Ecology	1888	81.87%
Energy Fuels	983	42.63%
Meteorology Atmospheric Sciences	955	41.41%
Engineering	892	38.68%
Business Economics	657	28.49%
Science Technology Other Topics	650	28.18%
Geography	611	26.49%
Public Environmental Occupational Health	575	24.94%
Physical Sciences Other Topics	475	20.59%
Mathematics	293	12.71%

Table 2

Summary of the largest 10 clusters.

Cluster ID	Size	Silhouette Score	Label (LLR)	Mean(Cite Year)
0	133	0.925	Secondary organic aerosol formation	2006
1	133	0.893	Urban form	2007
2	125	0.902	Panel data analysis	2011
3	109	0.875	Urban site	2002
4	97	0.89	Urban carbon metabolism	2009
5	83	0.919	Polycyclic aromatic hydrocarbon	1996
6	68	0.92	Urban household	2010
7	35	0.965	Chemical evolution	2001
8	34	0.97	Houston, Texas	2001
9	22	0.982	Utah, USA	2002

Table 3

The top 10 co-citation frequencies of the papers.

Citation Counts	References	Cluster ID
73	Dhakal S,2009, ENERG POLICY	4
67	Glaeser EL,2010, J.ECOL.	1
52	Ewing R,2008, HOUS POLICY DEBATE	1
51	York R,2003, ECOL ECON	2
51	Poumanyvong P,2010, ECOL ECON	2
51	Fang CL,2015, APPL ENERG	2
49	Ewing R,2010, J Am Plann Assoc	1
48	Wang SJ,2017, APPL ENERG	2
47	Mi ZF,2016, APPL ENERG	4
46	Ang BW,2004, ENERG POLICY	6

References

1. Masson-Delmotte, V.; Zhai, P.; Pirani, A.; Connors, S.L.; Péan, C.; Berger, S.; Caud, N.; Chen, Y.; Goldfarb, L.; Gomis, M.I. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change. Climate Change 2021: The Physical Science Basis; Cambridge University Press: Cambridge, UK, New York, NY, USA, 2021; [DOI: https://dx.doi.org/10.1017/9781009157896]

2. Ritchie, H.; Roser, M. Our World in Data, Urbanization. 2018; Available online: https://ourworldindata.org/urbanization (accessed on 1 August 2022).

3. Madlener, R.; Sunak, Y. Impacts of urbanization on urban structures and energy demand: What can we learn for urban energy planning and urbanization management?. Sustain. Cities Soc.; 2011; 1, pp. 45-53. [DOI: https://dx.doi.org/10.1016/j.scs.2010.08.006]

4. Georgescu-Roegen, N. The Entropy Law and the Economic Process; Harvard University Press: Cambridge, MA, USA, 1971.

5. Chen, Y.; Li, X.; Zheng, Y.; Guan, Y.; Liu, X. Estimating the relationship between urban forms and energy consumption: A case study in the Pearl River Delta, 2005–2008. Landsc. Urban Plan.; 2011; 201, pp. 33-42. [DOI: https://dx.doi.org/10.1016/j.landurbplan.2011.03.007]

6. He, S.; Yu, S.; Li, G.; Zhang, J. Exploring the influence of urban form on land-use efficiency from a spatiotemporal heterogeneity perspective: Evidence from 336 Chinese cities. Land Use Policy; 2020; 95, 104576. [DOI: https://dx.doi.org/10.1016/j.landusepol.2020.104576]

7. Lee, J.; Kurisu, K.; An, K.; Hanaki, K. Development of the compact city index and its application to Japanese cities. Urban Stud.; 2008; 52, pp. 1054-1070. [DOI: https://dx.doi.org/10.1177/0042098014536786]

8. Guo, C.; Schwarz, N.; Buchmann, C.M. Exploring the added value of population distribution indicators for studies of European urban form. Appl. Spat. Anal. Policy; 2018; 11, pp. 439-463. [DOI: https://dx.doi.org/10.1007/s12061-017-9225-7]

9. Kaza, N. Urban form and transportation energy consumption. Energy Policy; 2020; 136, 111049. [DOI: https://dx.doi.org/10.1016/j.enpol.2019.111049]

10. Zhang, W.; Huang, B.; Luo, D. Effects of land use and transportation on carbon sources and carbon sinks: A case study in Shenzhen, China. Landsc. Urban Plan.; 2014; 122, pp. 175-185. [DOI: https://dx.doi.org/10.1016/j.landurbplan.2013.09.014]

11. Wang, S.; Li, G.; Fang, C. Urbanization, economic growth, energy consumption, and CO₂ emissions: Empirical evidence from countries with different income levels. Renew. Sustain. Energy Rev.; 2018; 81, pp. 2144-2159. [DOI: https://dx.doi.org/10.1016/j.rser.2017.06.025]

12. Wang, S.; Wang, J.; Fang, C.; Li, S. Estimating the impacts of urban form on CO₂ emission efficiency in the Pearl River Delta, China. Cities; 2019; 85, pp. 117-129. [DOI: https://dx.doi.org/10.1016/j.cities.2018.08.009]

13. Anderson, W.; Kanaroglou, P.; Miller, E. Urban form, energy and the environment: A review of issues, evidence and policy. Urban Stud.; 1996; 33, pp. 7-35. [DOI: https://dx.doi.org/10.1080/00420989650012095]

14. Tian, X.; An, C.; Chen, Z. Assessing the impact of urban form on the greenhouse gas emissions from household vehicles: A review. J. Environ. Inform. Lett.; 2020; 3, pp. 70-85. [DOI: https://dx.doi.org/10.3808/jeil.202000029]

15. Boyack, K.W.; Wylie, B.N.; Davidson, G.S. Information visualization, human-computer interaction, and cognitive psychology: Domain visualizations. Visual Interfaces to Digital Libraries; Springer: Berlin/Heidelberg, Germany, 2002; pp. 145-158.

16. Chen, C.; Song, M. Visualizing a field of research: A methodology of systematic scientometric reviews. PLoS ONE; 2009; 14, e0223994. [DOI: https://dx.doi.org/10.1371/journal.pone.0223994]

17. Van Eck, N.J.; Waltman, L.; Dekker, R.; Van Den Berg, J. A comparison of two techniques for bibliometric mapping: Multidimensional scaling and VOS. J. Am. Soc. Inf. Sci. Technol.; 2010; 61, pp. 2405-2416. [DOI: https://dx.doi.org/10.1002/asi.21421]

18. Yang, W.; Zhang, J.; Ma, R. The prediction of infectious diseases: A bibliometric analysis. Int. J. Environ. Res. Public Health; 2020; 17, 6218. [DOI: https://dx.doi.org/10.3390/ijerph17176218] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/32867133]

19. Hu, Y.; Yu, Z.; Cheng, X.; Luo, Y.; Wen, C. A bibliometric analysis and visualization of medical data mining research. Medicine; 2020; 99, e20338. [DOI: https://dx.doi.org/10.1097/MD.0000000000020338]

20. Xie, H.; Wen, Y.; Choi, Y.; Zhang, X. Global Trends on Food Security Research: A Bibliometric Analysis. Land; 2008; 10, 119. [DOI: https://dx.doi.org/10.3390/land10020119]

21. Sharifi, A. Urban resilience assessment: Mapping knowledge structure and trends. Sustainability; 2020; 12, 5918. [DOI: https://dx.doi.org/10.3390/su12155918]

22. Sharifi, A.; Allam, Z.; Feizizadeh, B.; Ghamari, H. Three decades of research on smart cities: Mapping knowledge structure and trends. Sustainability; 2021; 13, 7140. [DOI: https://dx.doi.org/10.3390/su13137140]

23. Wei, F.; Grubesic, T.H.; Bishop, B.W. Exploring the GIS knowledge domain using CiteSpace. Prof. Geogr.; 2015; 67, pp. 374-384. [DOI: https://dx.doi.org/10.1080/00330124.2014.983588]

24. Lebel, L.; Garden, P.; Banaticla, M.R.N.; Lasco, R.D.; Contreras, A.; Mitra, A.P.; Sari, A. Integrating carbon management into the development strategies of urbanizing regions in Asia—Implications of urban function, form, and role. J. Ind. Ecol.; 2008; 11, pp. 61-81. [DOI: https://dx.doi.org/10.1162/jie.2007.1185]

25. Hankey, S.; Marshall, J.D. Impacts of urban form on future US passenger-vehicle greenhouse gas emissions. Energy Policy; 2010; 38, pp. 4880-4887. [DOI: https://dx.doi.org/10.1016/j.enpol.2009.07.005]

26. Croci, E.; Melandri, S.; Molteni, T. Determinants of cities’ GHG emissions: A comparison of seven global cities. Int. J. Clim. Change Strateg. Manag.; 2011; 3, pp. 275-301. [DOI: https://dx.doi.org/10.1108/17568691111153429]

27. Liu, X.; Sweeney, J. Modelling the impact of urban form on household energy demand and related CO₂ emissions in the Greater Dublin Region. Energy Policy; 2012; 46, pp. 359-369. [DOI: https://dx.doi.org/10.1016/j.enpol.2012.03.070]

28. Bereitschaft, B.; Debbage, K. Urban form, air pollution, and CO₂ emissions in large US metropolitan areas. Prof. Geogr.; 2013; 65, pp. 612-635. [DOI: https://dx.doi.org/10.1080/00330124.2013.799991]

29. Gurney, K.R. Recent research quantifying anthropogenic CO₂ emissions at the street scale within the urban domain. Carbon Manag.; 2014; 5, pp. 309-320. [DOI: https://dx.doi.org/10.1080/17583004.2014.986849]

30. Baiocchi, G.; Creutzig, F.; Minx, J.; Pichler, P.P. A spatial typology of human settlements and their CO₂ emissions in England. Glob. Environ. Chang.; 2015; 34, pp. 13-21. [DOI: https://dx.doi.org/10.1016/j.gloenvcha.2015.06.001]

31. Marchi, M.; Pulselli, R.M.; Marchettini, N.; Pulselli, F.M.; Bastianoni, S. Carbon dioxide sequestration model of a vertical greenery system. Ecol. Model.; 2015; 306, pp. 46-56. [DOI: https://dx.doi.org/10.1016/j.ecolmodel.2014.08.013]

32. Ma, M.; Rozema, J.; Gianoli, A.; Zhang, W. The Impacts of City Size and Density on CO₂ Emissions: Evidence from the Yangtze River Delta Urban Agglomeration. Appl. Spat. Anal. Policy; 2021; 15, pp. 529-555. [DOI: https://dx.doi.org/10.1007/s12061-021-09406-2]

33. Fernandes, M.; Skjemstad, J.; Johnson, B.; Wells, J.; Brooks, P. Characterization of carbonaceous combustion residues. I. Morphological, elemental and spectroscopic features. Chemosphere; 2003; 51, pp. 785-795. [DOI: https://dx.doi.org/10.1016/S0045-6535(03)00098-5]

34. Arnfield, A. Two decades of urban climate research: A review of turbulence, exchanges of energy and water, and the urban heat island. Int. J. Climatol.; 2003; 23, pp. 1-26. [DOI: https://dx.doi.org/10.1002/joc.859]

35. Dhakal, S. Urban energy use and carbon emissions from cities in China and policy implications. Energy Policy; 2009; 37, pp. 4208-4219. [DOI: https://dx.doi.org/10.1016/j.enpol.2009.05.020]

36. Davis, S.; Peters, G.; Caldeira, K. The Supply Chain of CO₂ Emissions. Proc. Natl. Acad. Sci. USA; 2011; 108, pp. 18554-18559. [DOI: https://dx.doi.org/10.1073/pnas.1107409108] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/22006314]

37. Kennedy, S.; Sgouridis, S. Rigorous Classification and Carbon Accounting Principles for Low and Zero Carbon Cities. Energy Policy; 2011; 39, pp. 5259-5268. [DOI: https://dx.doi.org/10.1016/j.enpol.2011.05.038]

38. Solomon, S.; Qin, D.; Manning, Z.; Chen, M.; Marquis, K.; Averyt, M.; Tignor and Miller, H. Contribution of Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change. Climate Change 2007: The Physical Science Basis; Cambridge University Press: Cambridge, UK, New York, NY, USA, 2007; Volume 996, pp. 113-119.

39. Dodman, D. Blaming cities for climate change? An analysis of urban greenhouse gas emissions inventories. Environ. Urban.; 2009; 21, pp. 185-201. [DOI: https://dx.doi.org/10.1177/0956247809103016]

40. Kennedy, C.; Steinberger, J.; Gasson, B.; Hansen, Y.; Hillman, T.; Havránek, M.; Mendez, G.V. Methodology for inventorying greenhouse gas emissions from global cities. Energy Policy; 2010; 38, pp. 4828-4837. [DOI: https://dx.doi.org/10.1016/j.enpol.2009.08.050]

41. Kennedy, C.A.; Ramaswami, A.; Carney, S.; Dhakal, S. Greenhouse gas emission baselines for global cities and metropolitan regions. Cities and Climate Change: Responding to an Urgent Agenda; World Bank Publications: Washington, DC, USA, 2011; pp. 15-54.

42. Shan, Y.; Liu, J.; Liu, Z.; Xu, X.; Shao, S.; Wang, P.; Guan, D. New provincial CO₂ emission inventories in China based on apparent energy consumption data and updated emission factors. Appl. Energy; 2016; 184, pp. 742-750. [DOI: https://dx.doi.org/10.1016/j.apenergy.2016.03.073]

43. Cai, M.; Shi, Y.; Ren, C. Developing a high-resolution emission inventory tool for low-carbon city management using hybrid method–A pilot test in high-density Hong Kong. Energy Build.; 2020; 226, 110376. [DOI: https://dx.doi.org/10.1016/j.enbuild.2020.110376]

44. Chuai, X.; Feng, J. High resolution carbon emissions simulation and spatial heterogeneity analysis based on big data in Nanjing City, China. Sci. Total Environ.; 2019; 686, pp. 828-837. [DOI: https://dx.doi.org/10.1016/j.scitotenv.2019.05.138]

45. Elvidge, C.D.; Baugh, K.E.; Zhizhin, M.; Hsu, F.C. Why VIIRS data are superior to DMSP for mapping nighttime lights. Proc. Asia-Pac. Adv. Netw.; 2013; 35, 62. [DOI: https://dx.doi.org/10.7125/APAN.35.7]

46. Small, C.; Pozzi, F.; Elvidge, C.D. Spatial analysis of global urban extent from DMSP-OLS night lights. Remote Sens. Environ.; 2005; 96, pp. 277-291. [DOI: https://dx.doi.org/10.1016/j.rse.2005.02.002]

47. Lu, H.; Liu, G.; Miao, C.; Zhang, C.; Cui, Y.; Zhao, J. Spatial pattern of residential carbon dioxide emissions in a rapidly urbanizing Chinese city and its mismatch effect. Sustainability; 2018; 10, 827. [DOI: https://dx.doi.org/10.3390/su10030827]

48. Doll, C.N.; Muller, J.P.; Elvidge, C.D. Night-time imagery as a tool for global mapping of socioeconomic parameters and greenhouse gas emissions. AMBIO J. Hum. Environ; 2000; 29, pp. 157-162. [DOI: https://dx.doi.org/10.1579/0044-7447-29.3.157]

49. Elvidge, C.D.; Keith, D.M.; Tuttle, B.T.; Baugh, K.E. Spectral identification of lighting type and character. Sensors; 2010; 10, pp. 3961-3988. [DOI: https://dx.doi.org/10.3390/s100403961] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/22319336]

50. Cai, B.; Liang, S.; Zhou, J.; Wang, J.; Cao, L.; Qu, S.; Yang, Z. China high resolution emission database (CHRED) with point emission sources, gridded emission data, and supplementary socioeconomic data. Resour. Conserv. Recycl.; 2018; 129, pp. 232-239. [DOI: https://dx.doi.org/10.1016/j.resconrec.2017.10.036]

51. Yamagata, Y.; Murakami, D.; Yoshida, T. Urban carbon mapping with spatial BigData. Energy Procedia; 2017; 142, pp. 2461-2466. [DOI: https://dx.doi.org/10.1016/j.egypro.2017.12.183]

52. Boyd, J.; Budinov, D.; Robinson, I.; Jack, J. The measurement of carbon dioxide levels in a city canyon. Proceedings of the SPIE Remote Sensing Technologies and Applications in Urban Environments; Edinburgh, UK, 26–29 September 2016; Volume 10008, pp. 78-87.

53. Zhang, G.; Ge, R.; Lin, T.; Ye, H.; Li, X.; Huang, N. Spatial apportionment of urban greenhouse gas emission inventory and its implications for urban planning: A case study of Xiamen, China. Ecol. Indic.; 2018; 85, pp. 644-656.

54. Cai, M.; Shi, Y.; Ren, C.; Yoshida, T.; Yamagata, Y.; Ding, C.; Zhou, N. The need for urban form data in spatial modeling of urban carbon emissions in China: A critical review. J. Clean. Prod.; 2021; 139, 128792. [DOI: https://dx.doi.org/10.1016/j.jclepro.2021.128792]

55. Wang, S.; Shi, C.; Fang, C.; Feng, K. Examining the spatial variations of determinants of energy-related CO₂ emissions in China at the city level using Geographically Weighted Regression Model. Appl. Energy; 2019; 235, pp. 95-105. [DOI: https://dx.doi.org/10.1016/j.apenergy.2018.10.083]

56. Wang, Y.; Zhao, M.; Chen, W. Spatial effect of factors affecting household CO₂ emissions at the provincial level in China: A geographically weighted regression model. Carbon Manag.; 2018; 9, pp. 187-200. [DOI: https://dx.doi.org/10.1080/17583004.2018.1451964]

57. Hou, Q.; Zhang, X.; Li, B.; Zhang, X.; Wang, W. Identification of low-carbon travel block based on GIS hotspot analysis using spatial distribution learning algorithm. Neural Comput. Appl.; 2019; 31, pp. 4703-4713. [DOI: https://dx.doi.org/10.1007/s00521-018-3447-8]

58. Wang, S.; Huang, Y.; Zhou, Y. Spatial spillover effect and driving forces of carbon emission intensity at the city level in China. J. Geogr. Sci.; 2019; 29, pp. 231-252. [DOI: https://dx.doi.org/10.1007/s11442-019-1594-1]

59. Wu, Y.; Shen, J.; Zhang, X.; Skitmore, M.; Lu, W. The impact of urbanization on carbon emissions in developing countries: A Chinese study based on the U-Kaya method. J. Clean. Prod.; 2017; 163, pp. S284-S298. [DOI: https://dx.doi.org/10.1016/j.jclepro.2017.05.144]

60. Wang, P.; Wu, W.; Zhu, B.; Wei, Y. Examining the impact factors of energy-related CO₂ emissions using the STIRPAT model in Guangdong Province, China. Appl. Energy; 2013; 106, pp. 65-71. [DOI: https://dx.doi.org/10.1016/j.apenergy.2013.01.036]

61. Yang, W.; Tao, L.; Cao, X. Examining the impacts of socio-economic factors, urban form and transportation development on CO₂ emissions from transportation in China: A panel data analysis of China’s provinces. Habitat Int.; 2015; 49, pp. 212-220. [DOI: https://dx.doi.org/10.1016/j.habitatint.2015.05.030]

62. van de Coevering, P.; Schwanen, T. Re-evaluating the impact of urban form on travel patterns in Europe and North-America. Transp. Policy; 2006; 13, pp. 229-239. [DOI: https://dx.doi.org/10.1016/j.tranpol.2005.10.001]

63. Lin, J.; Lu, S.; He, X.; Wang, F. Analyzing the impact of three-dimensional building structure on CO₂ emissions based on random forest regression. Energy; 2021; 236, 121502. [DOI: https://dx.doi.org/10.1016/j.energy.2021.121502]

64. Ewing, R.; Cervero, R. Travel and the built environment: A meta-analysis. J. Am. Plan. Assoc.; 2010; 76, pp. 265-294. [DOI: https://dx.doi.org/10.1080/01944361003766766]

65. Fong, W.K.; Matsumoto, H.; Ho, C.S.; Lun, Y.F. Energy consumption and carbon dioxide emission considerations in the urban planning process in Malaysia. Plan. Malays.; 2008; 6, [DOI: https://dx.doi.org/10.21837/pm.v6i1.68]

66. Khan, F.; Pinter, L. Scaling indicator and planning plane: An indicator and a visual tool for exploring the relationship between urban form, energy efficiency and carbon emissions. Ecol. Indic.; 2016; 67, pp. 183-192. [DOI: https://dx.doi.org/10.1016/j.ecolind.2016.02.046]

67. Pan, Y.; Birdsey, R.A.; Fang, J.; Houghton, R.; Kauppi, P.E.; Kurz, W.A.; Hayes, D. A large and persistent carbon sink in the world’s forests. Science; 2011; 333, pp. 988-993. [DOI: https://dx.doi.org/10.1126/science.1201609]

68. Seto, K.C.; Güneralp, B.; Hutyra, L.R. Global forecasts of urban expansion to 2030 and direct impacts on biodiversity and carbon pools. Proc. Natl. Acad. Sci. USA; 2012; 109, pp. 16083-16088. [DOI: https://dx.doi.org/10.1073/pnas.1211658109]

69. Bateman, I.J.; Harwood, A.R.; Mace, G.M.; Watson, R.T.; Abson, D.J.; Andrews, B.; Termansen, M. Bringing ecosystem services into economic decision-making: Land use in the United Kingdom. Science; 2013; 341, pp. 45-50. [DOI: https://dx.doi.org/10.1126/science.1234379] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/23828934]

70. Xu, Q.; Yang, R.; Dong, Y.X.; Liu, Y.X.; Qiu, L.R. The influence of rapid urbanization and land use changes on terrestrial carbon sources/sinks in Guangzhou, China. Ecol. Indic.; 2016; 70, pp. 304-316. [DOI: https://dx.doi.org/10.1016/j.ecolind.2016.05.052]

71. Liu, J.; Kuang, W.; Zhang, Z.; Xu, X.; Qin, Y.; Ning, J.; Chi, W. Spatiotemporal characteristics, patterns, and causes of land-use changes in China since the late 1980s. J. Geogr. Sci.; 2014; 24, pp. 195-210. [DOI: https://dx.doi.org/10.1007/s11442-014-1082-6]

72. Ou, J.; Liu, X.; Li, X.; Chen, Y. Quantifying the relationship between urban forms and carbon emissions using panel data analysis. Landsc. Ecol.; 2013; 28, pp. 1889-1907. [DOI: https://dx.doi.org/10.1007/s10980-013-9943-4]

73. Liu, X.; Wang, M.; Qiang, W.; Wu, K.; Wang, X. Urban form, shrinking cities, and residential carbon emissions: Evidence from Chinese city-regions. Appl. Energy; 2020; 261, 114409. [DOI: https://dx.doi.org/10.1016/j.apenergy.2019.114409]

74. Shi, K.; Xu, T.; Li, Y.; Chen, Z.; Gong, W.; Wu, J.; Yu, B. Effects of urban forms on CO₂ emissions in China from a multi-perspective analysis. J. Environ. Manag.; 2020; 262, 110300. [DOI: https://dx.doi.org/10.1016/j.jenvman.2020.110300] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/32250786]

75. Zhao, P.; Diao, J.; Li, S. The influence of urban structure on individual transport energy consumption in China’s growing cities. Habitat Int.; 2017; 66, pp. 95-105. [DOI: https://dx.doi.org/10.1016/j.habitatint.2017.06.001]

76. Muñiz, I.; Dominguez, A. The impact of urban form and spatial structure on per capita carbon footprint in US larger metropolitan areas. Sustainability; 2020; 12, 389. [DOI: https://dx.doi.org/10.3390/su12010389]

77. Lu, J.; Guldmann, J.M. Landscape ecology, land-use structure, and population density: Case study of the Columbus Metropolitan Area. Landsc. Urban Plan.; 2012; 105, pp. 74-85. [DOI: https://dx.doi.org/10.1016/j.landurbplan.2011.11.024]

78. Chuai, X.; Huang, X.; Wang, W.; Zhao, R.; Zhang, M.; Wu, C. Land use, total carbon emissions change and low carbon land management in Coastal Jiangsu, China. J. Clean. Prod.; 2015; 103, pp. 77-86. [DOI: https://dx.doi.org/10.1016/j.jclepro.2014.03.046]

79. Glaeser, E.L.; Kahn, M.E. The greenness of cities: Carbon dioxide emissions and urban development. J. Urban Econ.; 2010; 67, pp. 404-418. [DOI: https://dx.doi.org/10.1016/j.jue.2009.11.006]

80. Huang, Y.; Xia, B.; Yang, L. Relationship study on land use spatial distribution structure and energy-related carbon emission intensity in different land use types of Guangdong, China, 1996–2008. Sci. World J.; 2013; 2013, 309680. [DOI: https://dx.doi.org/10.1155/2013/309680] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/23476128]

81. Wu, C.; Li, G.; Yue, W.; Lu, R.; Lu, Z.; You, H. Effects of endogenous factors on regional land-use carbon emissions based on the grossman decomposition model: A case study of Zhejiang province, China. Environ. Manag.; 2015; 55, pp. 467-478.

82. Gratani, L.; Varone, L.; Bonito, A. Carbon sequestration of four urban parks in Rome. Urban For. Urban Green.; 2016; 19, pp. 184-193. [DOI: https://dx.doi.org/10.1016/j.ufug.2016.07.007]

83. Jo, H.K.; Kim, J.Y.; Park, H.M. Carbon reduction and planning strategies for urban parks in Seoul. Urban For. Urban Green.; 2019; 41, pp. 48-54. [DOI: https://dx.doi.org/10.1016/j.ufug.2019.03.009]

84. Wang, Y.; Chang, Q.; Li, X. Promoting sustainable carbon sequestration of plants in urban greenspace by planting design: A case study in parks of Beijing. Urban For. Urban Green.; 2021; 64, 127291. [DOI: https://dx.doi.org/10.1016/j.ufug.2021.127291]

85. Lv, F.; Qi, X.C. The Urban Planning Strategies Research of Decreasing Traffic Carbon Emissions. Advanced Materials Research; Trans Tech Publications, Ltd.: Bach, Switzerland, 2013; Volume 671, pp. 2402-2405.

86. Zhang, R.; Matsushima, K.; Kobayashi, K. Can land use planning help mitigate transport-related carbon emissions? A case of Changzhou. Land Use Policy; 2018; 74, pp. 32-40. [DOI: https://dx.doi.org/10.1016/j.landusepol.2017.04.025]

87. Grimm, N.B.; Faeth, S.H.; Golubiewski, N.E.; Redman, C.L.; Wu, J.; Bai, X.; Briggs, J.M. Global change and the ecology of cities. Science; 2008; 319, pp. 756-760. [DOI: https://dx.doi.org/10.1126/science.1150195]

88. Hutyra, L.R.; Yoon, B.; Hepinstall-Cymerman, J.; Alberti, M. Carbon consequences of land cover change and expansion of urban lands: A case study in the Seattle metropolitan region. Landsc. Urban Plan.; 2011; 103, pp. 83-93. [DOI: https://dx.doi.org/10.1016/j.landurbplan.2011.06.004]

89. Pickett, S.T.; Cadenasso, M.L.; Grove, J.M.; Boone, C.G.; Groffman, P.M.; Irwin, E.; Warren, P. Urban ecological systems: Scientific foundations and a decade of progress. J. Environ. Manag.; 2011; 92, pp. 331-362. [DOI: https://dx.doi.org/10.1016/j.jenvman.2010.08.022] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/20965643]

90. Cervero, R.; Kockelman, K. Travel demand and the 3Ds: Density, diversity, and design. Transp. Res. Part D Transp. Environ.; 1997; 2, pp. 199-219. [DOI: https://dx.doi.org/10.1016/S1361-9209(97)00009-6]

91. Deng, J.Y.; Wong, N.H.; Zheng, X. The study of the effects of building arrangement on microclimate and energy demand of CBD in Nanjing, China. Procedia Eng.; 2016; 169, pp. 44-54. [DOI: https://dx.doi.org/10.1016/j.proeng.2016.10.006]

92. Fang, C.; Wang, S.; Li, G. Changing urban forms and carbon dioxide emissions in China: A case study of 30 provincial capital cities. Appl. Energy; 2015; 158, pp. 519-531. [DOI: https://dx.doi.org/10.1016/j.apenergy.2015.08.095]

93. Cervero, R.; Radisch, C. Travel choices in pedestrian versus automobile oriented neighborhoods. Transp. Policy; 1996; 3, pp. 127-141. [DOI: https://dx.doi.org/10.1016/0967-070X(96)00016-9]

94. McCormack, E.; Rutherford, G.S.; Wilkinson, M.G. Travel impacts of mixed land use neighborhoods in Seattle, Washington. Transp. Res. Rec.; 2001; 1780, pp. 25-32. [DOI: https://dx.doi.org/10.3141/1780-04]

95. Kuzmyak, J.R.; Baber, C.; Savory, D. Use of walk opportunities index to quantify local accessibility. Transp. Res. Rec.; 2006; 1977, pp. 145-153. [DOI: https://dx.doi.org/10.1177/0361198106197700117]

96. Song, Y.B. Influence of new town development on the urban heat island—The case of the Bundang area. J. Environ. Sci.; 2005; 17, pp. 641-645.

97. Weng, Q.; Lu, D.; Schubring, J. Estimation of land surface temperature–vegetation abundance relationship for urban heat island studies. Remote Sens. Environ.; 2004; 89, pp. 467-483. [DOI: https://dx.doi.org/10.1016/j.rse.2003.11.005]

98. Masnavi, M.R. The new millennium and the new urban paradigm: The compact city in practice. Achieving Sustainable Urban Form; Routledge: Oxfordshire, UK, 2000; pp. 64-73.

99. Guo, Z. Does residential parking supply affect household car ownership? The case of New York City. J. Transp. Geogr.; 2013; 26, pp. 18-28. [DOI: https://dx.doi.org/10.1016/j.jtrangeo.2012.08.006]

100. Van, E.; Looman, R.; Bruin-Hordijk, G. The effects of urban and building design parameters on solar access to the urban canyon and the po-tential for direct passive solar heating strategies. Energy Build; 2012; 47, pp. 189-200.

101. Shi, Y.; Ren, C.; Zheng, Y.; Ng, E. Mapping the urban microclimatic spatial distribution in a sub-tropical high-density urban environment. Archit. Sci. Rev.; 2016; 59, pp. 370-384. [DOI: https://dx.doi.org/10.1080/00038628.2015.1105195]

102. Dawodu, A.; Cheshmehzangi, A. Impact of floor area ratio (FAR) on energy consumption at meso scale in China: Case study of ningbo. Energy Procedia; 2017; 105, pp. 3449-3455. [DOI: https://dx.doi.org/10.1016/j.egypro.2017.03.789]

103. Perini, K.; Magliocco, A. Effects of vegetation, urban density, building height, and atmospheric conditions on local temperatures and thermal comfort. Urban For. Urban Green.; 2014; 13, pp. 495-506. [DOI: https://dx.doi.org/10.1016/j.ufug.2014.03.003]

104. Coseo, P.; Larsen, L. How factors of land use/land cover, building configuration, and adjacent heat sources and sinks explain Urban Heat Islands in Chicago. Landsc Urban Plan.; 2014; 125, pp. 117-129. [DOI: https://dx.doi.org/10.1016/j.landurbplan.2014.02.019]

105. Tyrinopoulos, Y.; Antoniou, C. Factors affecting modal choice in urban mobility. Eur. Transp. Res. Rev.; 2013; 5, pp. 27-39. [DOI: https://dx.doi.org/10.1007/s12544-012-0088-3]

106. Yan, H.; Shen, Q.; Fan, L.C.; Wang, Y.; Zhang, L. Greenhouse gas emissions in building construction: A case study of One Peking in Hong Kong. Build. Environ.; 2010; 45, pp. 949-955. [DOI: https://dx.doi.org/10.1016/j.buildenv.2009.09.014]

107. Godoy-Shimizu, D.; Steadman, P.; Hamilton, I.; Donn, M.; Evans, S.; Moreno, G.; Shayesteh, H. Energy use and height in office buildings. Build. Res. Inf.; 2018; 46, pp. 845-863. [DOI: https://dx.doi.org/10.1080/09613218.2018.1479927]

108. Borck, R. Will skyscrapers save the planet? Building height limits and urban greenhouse gas emissions. Reg. Sci. Urban Econ.; 2016; 58, pp. 13-25. [DOI: https://dx.doi.org/10.1016/j.regsciurbeco.2016.01.004]

109. Cho, J.S. Design Methodology for Tall Office Buildings: Design Measurement and Integration with Regional Character; Illinois Institute of Technology: Chicago, IL, USA, 2002.

110. Krüger, E.; Pearlmutter, D.; Rasia, F. Evaluating the impact of canyon geometry and orientation on cooling loads in a high-mass building in a hot dry environment. Appl. Energy; 2010; 87, pp. 2068-2078. [DOI: https://dx.doi.org/10.1016/j.apenergy.2009.11.034]

111. Ding, C.; Wang, Y.; Xie, B.; Liu, C. Understanding the role of built environment in reducing vehicle miles traveled accounting for spatial heterogeneity. Sustainability; 2014; 6, pp. 589-601. [DOI: https://dx.doi.org/10.3390/su6020589]

112. Handy, S. Critical assessment of the literature on the relationships among transportation, land use, and physical activity. Transportation Research Board and the Institute of Medicine Committee on Physical Activity, Health, Transportation, and Land Use. Resour. Pap. TRB Spec. Rep.; 2005; 282, pp. 1-81.

113. Cao, X.; Mokhtarian, P.L.; Handy, S.L. Examining the impacts of residential self-selection on travel behaviour: A focus on empirical findings. Transp. Rev.; 2009; 29, pp. 359-395. [DOI: https://dx.doi.org/10.1080/01441640802539195]

114. Handy, S.L.; Clifton, K.J. Local shopping as a strategy for reducing automobile travel. Transportation; 2001; 28, pp. 317-346. [DOI: https://dx.doi.org/10.1023/A:1011850618753]

115. Berdica, K.; Andjic, Z.; Nicholson, A.J. Simulating Road Traffic Interruptions–Does it Matter What Model We Use?. The Network Reliability of Transport; Emerald Group Publishing Limited: Bingley, UK, 2003.

116. Lu, Q.C.; Peng, Z.R. Vulnerability analysis of transportation network under scenarios of sea level rise. Transp. Res. Rec.; 2011; 2263, pp. 174-181. [DOI: https://dx.doi.org/10.3141/2263-19]

117. Taylor, M.A. Critical transport infrastructure in Urban areas: Impacts of traffic incidents assessed using accessibility-based network vulnerability analysis. Growth Chang.; 2008; 39, pp. 593-616. [DOI: https://dx.doi.org/10.1111/j.1468-2257.2008.00448.x]

118. Krizek, K.J. Residential relocation and changes in urban travel: Does neighborhood-scale urban form matter?. J. Am. Plan. Assoc.; 2003; 69, pp. 265-281. [DOI: https://dx.doi.org/10.1080/01944360308978019]

119. Miller, E.J.; Ibrahim, A. Urban form and vehicular travel: Some empirical findings. Transp. Res. Rec.; 1998; 1617, pp. 18-27. [DOI: https://dx.doi.org/10.3141/1617-03]

120. Cervero, R.; Arrington, G.B. Vehicle trip reduction impacts of transit-oriented housing. J. Public Transp.; 2008; 11, 1. [DOI: https://dx.doi.org/10.5038/2375-0901.11.3.1]

121. Van Acker, V.; Witlox, F. Car ownership as a mediating variable in car travel behaviour research using a structural equation modelling approach to identify its dual relationship. J. Transp. Geogr.; 2010; 18, pp. 65-74. [DOI: https://dx.doi.org/10.1016/j.jtrangeo.2009.05.006]

122. Potoglou, D.; Kanaroglou, P.S. Modelling car ownership in urban areas: A case study of Hamilton, Canada. J. Transp. Geogr.; 2008; 16, pp. 42-54. [DOI: https://dx.doi.org/10.1016/j.jtrangeo.2007.01.006]

123. Zahabi, S.A.H.; Miranda-Moreno, L.; Patterson, Z.; Barla, P.; Harding, C. Transportation greenhouse gas emissions and its relationship with urban form, transit accessibility and emerging green technologies: A Montreal case study. Procedia-Soc. Behav. Sci.; 2012; 54, pp. 966-978. [DOI: https://dx.doi.org/10.1016/j.sbspro.2012.09.812]

124. Ye, H.; He, X.; Song, Y.; Li, X.; Zhang, G.; Lin, T.; Xiao, L. A sustainable urban form: The challenges of compactness from the viewpoint of energy consumption and carbon emission. Energy Build.; 2015; 93, pp. 90-98. [DOI: https://dx.doi.org/10.1016/j.enbuild.2015.02.011]

125. Hong, J.; Goodchild, A. Land use policies and transport emissions: Modeling the impact of trip speed, vehicle characteristics and residential location. Transp. Res. Part D Transp. Environ.; 2014; 26, pp. 47-51. [DOI: https://dx.doi.org/10.1016/j.trd.2013.10.011]

126. Wang, S.; Liu, X.; Zhou, C.; Hu, J.; Ou, J. Examining the impacts of socioeconomic factors, urban form, and transportation networks on CO₂ emissions in China’s megacities. Appl. Energy; 2017; 185, pp. 189-200. [DOI: https://dx.doi.org/10.1016/j.apenergy.2016.10.052]

127. Holz-Rau, C.; Scheiner, J.; Sicks, K. Travel distances in daily travel and long-distance travel: What role is played by urban form?. Environ. Plan. A; 2014; 46, pp. 488-507. [DOI: https://dx.doi.org/10.1068/a4640]

128. Agarwal, A.; Ziemke, D.; Nagel, K. Bicycle superhighway: An environmentally sustainable policy for urban transport. Transp. Res. Part A Policy Pract.; 2020; 137, pp. 519-540. [DOI: https://dx.doi.org/10.1016/j.tra.2019.06.015]

129. Makido, Y.; Dhakal, S.; Yamagata, Y. Relationship between urban form and CO₂ emissions: Evidence from fifty Japanese cities. Urban Clim.; 2012; 2, pp. 55-67. [DOI: https://dx.doi.org/10.1016/j.uclim.2012.10.006]

130. Ou, J.; Liu, X.; Wang, S.; Xie, R.; Li, X. Investigating the differentiated impacts of socioeconomic factors and urban forms on CO₂ emissions: Empirical evidence from Chinese cities of different developmental levels. J. Clean. Prod.; 2019; 226, pp. 601-614. [DOI: https://dx.doi.org/10.1016/j.jclepro.2019.04.123]

131. Zhang, Y.; Wu, Q.; Fath, B.D. Review of spatial analysis of urban carbon metabolism. Ecol. Model.; 2018; 371, pp. 18-24. [DOI: https://dx.doi.org/10.1016/j.ecolmodel.2018.01.005]

132. Cirilli, A.; Veneri, P. Spatial structure and carbon dioxide (CO₂) emissions due to commuting: An analysis of Italian urban areas. Reg. Stud.; 2014; 48, pp. 1993-2005. [DOI: https://dx.doi.org/10.1080/00343404.2013.827333]

133. Lee, S.; Lee, B. The influence of urban form on GHG emissions in the US household sector. Energy Policy; 2014; 68, pp. 534-549. [DOI: https://dx.doi.org/10.1016/j.enpol.2014.01.024]

134. Liang, Z.; Wu, S.; Wang, Y.; Wei, F.; Huang, J.; Shen, J.; Li, S. The relationship between urban form and heat island intensity along the urban development gradients. Sci. Total Environ.; 2020; 708, 135011. [DOI: https://dx.doi.org/10.1016/j.scitotenv.2019.135011]

135. Ewing, R.; Rong, F. The impact of urban form on US residential energy use. Hous. Policy Debate; 2008; 19, pp. 1-30. [DOI: https://dx.doi.org/10.1080/10511482.2008.9521624]

136. Luo, Y.L.; Zhang, C.X. Research on the Low-Carbon Land Use Pattern. Advanced Materials Research; Trans Tech Publications, Ltd.: Bach, Switzerland, 2012; Volume 598, pp. 241-246.

137. Wang, S.; Zeng, J.; Huang, Y.; Shi, C.; Zhan, P. The effects of urbanization on CO₂ emissions in the Pearl River Delta: A comprehensive assessment and panel data analysis. Appl. Energy; 2018; 228, pp. 1693-1706. [DOI: https://dx.doi.org/10.1016/j.apenergy.2018.06.155]

138. Wang, Y.; Hayashi, Y.; Chen, J.; Li, Q. Changing urban form and transport CO₂ emissions: An empirical analysis of Beijing, China. Sustainability; 2014; 6, pp. 4558-4579. [DOI: https://dx.doi.org/10.3390/su6074558]

139. Su, M.; Liang, C.; Chen, B.; Chen, S.; Yang, Z. Low-carbon development patterns: Observations of typical Chinese cities. Energies; 2012; 5, pp. 291-304. [DOI: https://dx.doi.org/10.3390/en5020291]

140. Wang, S.; Fang, C.; Sun, L.; Su, Y.; Chen, X.; Zhou, C.; Feng, K.; Hubacek, K. Decarbonizing China’s Urban Agglomerations. Ann. Am. Assoc. Geogr.; 2019; 109, pp. 266-285. [DOI: https://dx.doi.org/10.1080/24694452.2018.1484683]

141. Wang, S.; Liu, X. China's city-level energy-related CO₂ emissions: Spatiotemporal patterns and driving forces. Appl. Energy; 2017; 200, pp. 204-214. [DOI: https://dx.doi.org/10.1016/j.apenergy.2017.05.085]

142. Wang, S.; Fang, C.; Wang, Y. Spatiotemporal variations of energy-related CO₂ emissions in China and its influencing factors: An empirical analysis based on provincial panel data. Renew. Sustain. Energy Rev.; 2016; 55, pp. 505-515. [DOI: https://dx.doi.org/10.1016/j.rser.2015.10.140]

143. Wang, S.; Li, Q.; Fang, C.; Zhou, C. The relationship between economic growth, energy consumption, and CO₂ emissions: Empirical evidence from China. Sci. Total Environ.; 2016; 542, pp. 60-371. [DOI: https://dx.doi.org/10.1016/j.scitotenv.2015.10.027] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/26520261]

144. Wang, S.; Wang, Z.; Fang, C. Evolutionary characteristics and driving factors of carbon emission performance at the city level in China. Sci. China-Earth Sci.; 2022; 65, pp. 1292-1307. [DOI: https://dx.doi.org/10.1007/s11430-021-9928-2]

145. Wang, S.; Xie, Z.; Wu, R.; Feng, K. How does urbanization affect the carbon intensity of human well-being? A global assessment. Appl. Energy; 2022; 312, 118798. [DOI: https://dx.doi.org/10.1016/j.apenergy.2022.118798]

Word count: 11197

Show less

© 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.

Abstract

Translate

As a result of global climate change and urban development, the interaction between urban form and carbon emissions has become a frontier issue and a key area of carbon emission research. This paper presents a scientometric analysis of 2439 academic publications between 2002 and 2021 on urban form and carbon emissions to explore the current state of global research and future development potential. Citespace and VOSviewer were the primary analysis tools. The results showed the following: (1) The number of articles published on urban form and carbon emission research shows an increasing trend, especially after 2012. (2) Scientific research institutions and authors in developed countries paid attention sooner to the urban ecological environment. With the deepening of economic globalization, developing countries began to pay more attention to the urban environment. (3) Through an analysis of keyword clusters, timelines, and stacked area charts, the development of the urban form and carbon emissions can be divided into the following three stages. The first is the budding stage, which is characterized by preliminary research on the atmospheric environmental impact factors. The second stage is the development stage, with urban areas becoming the leading research object of carbon theory. The third stage is the mature stage, which is characterized by an emphasis on the optimization of carbon emissions. (4) Finally, the influence of urban form on carbon emissions includes four main aspects: land use, built environment, transportation networks, and development patterns.

Details

Title

The Impacts of Urban Form on Carbon Emissions: A Comprehensive Review

Author

Sun, Changlong¹; Zhang, Yongli²; Ma, Wenwen¹; Wu, Rong²; Wang, Shaojian³

¹ School of Economics and Management, Wenzhou University of Technology, Wenzhou 325000, China
² School of Architecture and Urban Planning, Guangdong University of Technology, Guangzhou 510062, China
³ School of Geography and Planning, Sun Yat-sen University, Guangzhou 510275, China

First page

1430

Publication year

2022

Publication date

2022

Publisher

MDPI AG

e-ISSN

2073445X

Source type

Scholarly Journal

Language of publication

English

DOI

https://doi.org/10.3390/land11091430

ProQuest document ID

2716553646

The Impacts of Urban Form on Carbon Emissions: A Comprehensive Review

Jump to:

Full Text

Abstract

Details

Suggested sources