1 Introduction

Transport CO2 emissions contribute substantially to global greenhouse gas (GHG) emission increases, which will cause climate change and increase the vulnerability of ecosystems and human societies (Ostad-Ali-Askar et al., 2018). It is projected by the International Energy Agency’s World Energy Outlook 2013 that transport fuel demand will increase nearly 40% globally by 2035 (ADB, 2016), and there will be large increases of transport CO2 emissions from developing Asian countries (Timilsina & Shrestha, 2009).

Understanding transport CO2 emission factors’ effects comprehensively is important for making rational planning strategies. Previous studies have examined and modeled the relationships between transport CO2 emissions and their significant factors, including socio-economic characteristics, urban form factors, and metro accessibility. It is found that car availability, high income, and location in the outer and sprawling areas will notably cause larger transport CO2 emissions (Brand & Boardman, 2008; Brand & Preston, 2010; Brand et al., 2013; Büchs & Schnepf, 2013; Ko et al., 2011; Shuai et al., 2018; Wang et al., 2017; Yang et al., 2018, 2019, 2020). Fostering polycentric and satellite cities can decrease transport CO2 emissions significantly (Cirilli & Veneri, 2014; Grunfelder et al., 2015; Knaap et al., 2016; Modarres, 2011; Sun, Zacharias, et al., 2016; Sun, Zhou, et al., 2016; Veneri, 2010; Yang et al., 2018, 2020). Metro or light rail constructions can reduce driving distance and transport CO2 emissions (Cao, 2019; Huang et al., 2019; Spears et al., 2017; Boarnet et al., 2017). These previous studies have only examined the average effects of the significant transport CO2 emission impact factors. However, if the variation effects of these factors among different locations of transport emission distribution are further measured, a detailed view of the relationship between transport CO2 emissions and their impact factors can be obtained. Then, the results will guide for more effective urban and transport planning strategy making. Presently, research with such focus is lacking, though. Thus, this paper will identify the heterogeneous effects of transport CO2 emission impact factors by using the quantile regression model. This method can estimate the factors’ effects at any quantile of the outcome variable’s distribution, and thus capture a more comprehensive evaluation of the characteristics among transport emitters. Also, this method can overcome the shortcomings of the models based on the conditional mean method, which means that when the outcome variable is not normally distributed and heteroscedastic, the estimation results would probably be biased (Koenker & Hallock, 2001; Wang et al., 2019; Xu & Lin, 2016). Then, according to the quantile regression model results, this study will propose urban and transport planning strategies for emission reduction and climate change mitigation in developing cities.

China and India, with most of their cities in the inland areas, are two economic giants experiencing rapid economic growth, urban expansion, and motorization. Transport CO2 emission increases have been evident in recent decades. It is necessary to focus on Chinese and Indian developing cities for mitigating transport CO2 emission increases. Thus, four typical inland Chinese and Indian cities are selected as case cities, namely Beijing, Xian, and Wuhan in China, and Bangalore in India. Beijing, located in the eastern part, with a strong center, is the capital city of China. Xi’an and Wuhan are two provincial cities located in the western and middle inland, respectively; their noticeable differences lie in the urban forms (Xi’an has a strong center, while Wuhan has a polycentric urban form, separated by two big rivers). In India, Bangalore is located in the southern part of the inland, with a strong center, and is known as ‘Asia’s Silicon Valley’, with famous information technology industries. These four inland cities have experienced urban expansions, as well as population, motorization, and economic increases in recent decades, representing the general situations of urban and transport developments among Chinese and Indian cities. Due to the large number of commute trips in urban transportation, as well as the inflexibility of these trips, this study intends to focus on the CO2 emissions that result from commuting.

In recent decades, Chinese and Indian cities are experiencing important stages of development, in terms of urban growths, motorization and economic increases, metro and rail network constructions, urban agglomeration developments, energy technology improvements, and new energy vehicle promotions. These developments are crucial issues for transport emission increases and climate change, not only in China and India, but also in other global cities. Therefore, this paper’s findings will provide empirical evidence and reference value globally.

The remainder of the paper is organized into the following sections. In Sect. 2, previous studies on transport CO2 emission impact factors and the research gap will be discussed. In Sect. 3, the process of data collection in the four case cities will be briefly introduced, and the quantile regression model will be illustrated for measuring the heterogeneous effects of transport CO2 emission factors. Section 4 analyzes the model results. The last section provides discussions and conclusions of this study.

2 Literature review

Exploring and measuring significant impact factors of transport CO2 emissions, as well as travel behaviors, have been interests of scholarly research for many years. They are the key steps for rational planning strategies to develop sustainable travel patterns and mitigate transport CO2 emission increases. Numerous researchers have found that household and commuter socio-economic characteristics can significantly affect transport CO2 emissions. Car availability is found to be the most notable predictor of transport CO2 emissions (Brand & Boardman, 2008; Brand & Preston, 2010; Brand et al., 2013; Ko et al., 2011). By using the linear regression method, with log-transformed transport CO2 emissions as the explained variable, it has been found that, in the UK, owning at least one car tends to increase the weekly transport CO2 emissions by 44%-59% (Brand et al., 2013), and owning two cars or more can increase the individual transport CO2 emissions by 13.3% (Brand & Preston, 2010). Higher income can also increase transport CO2 emissions (Ko et al., 2011; Büchs & Schnepf, 2013; Brand et al., 2013; Brand & Preston, 2010; Brand & Boardman, 2008; Susilo & Stead, 2009; Carlsson-Kanyama & Lindén, 1999). A 1% increase of household annual income can increase the total annual transport CO2 emissions by 59.8% (Büchs & Schnepf, 2013). Individuals with incomes higher than £40,000 have 49.9% larger transport CO2 emissions (Brand & Preston, 2010). Additionally, better education backgrounds could have effects on the emission increases. Individuals with education backgrounds of more than 16 years produce 15.4% larger transport CO2 emissions (Büchs & Schnepf, 2013). Similarly, full-time employment, commuters with professional occupations, and those working in the government and in foreign companies produce more transport CO2 emissions than those with part-time jobs and other work unit types (Brand & Preston, 2010; Brand et al., 2013; Ko et al., 2011; Wang et al., 2017; Yang et al., 2017).

Urban form factor effects on travel patterns and transport CO2 emissions have also attracted considerable interest from scholars in recent decades. Many studies found that cities with polycentric patterns can promote more sustainable travel patterns and less transport CO2 emissions. Higher degrees of polycentricity and higher job-to-population ratios can result in shorter commute times in some Chinese cities, Italian metropolitan areas, and Southern California (Modarres, 2011; Sun, Zacharias, et al., 2016; Sun, Zhou, et al., 2016; Veneri, 2010), and can also decrease commuting distances in Danish city regions (Grunfelder et al., 2015). Employment share increases in the central cities have shown to cause larger commuter CO2 emissions in Italy (Cirilli & Veneri, 2014). Compared to a metropolis with one strong center in China, the polycentric city of Wuhan has shorter commuting distances, smaller car mode share, more public bus usage, and more local commute trips (Yang et al., 2018). Under the scenario of a polycentric pattern, the average commuter’s CO2 emissions are 15% smaller than cities with one strong center and similar economic levels, and 51%-75% of the resident transport CO2 emissions will be reduced in future (Yang et al., 2020). Also, developing satellite cities can make individuals commute shorter distances, make more local trips, and use more non-motorized modes of transportation, thus producing significantly less transport CO2 emissions (Yang et al., 2019). Fostering satellite cities will reduce transport CO2 emissions by 75–82% in the long-run in developing cities (Yang et al., 2020). Numerous studies also find that household locations can influence transport CO2 emissions significantly. It is found that in the Greater Toronto Area, a 1 km increase of the straight-line distance from the city center will cause a 0.25 km increase of vehicle miles travelled (Miller & Ibrahim, 1998). In the Minneapolis-St. Paul Twin Cities Area, a 1 mile increase of the distance to the downtown area is connected to approximately 0.08–0.1 kg increase of transport CO2 emissions per day (Wu et al., 2019). Households located near the radial road and outer ring road areas produce much more transport CO2 emissions than those located in the city center in Chinese cities (Wang et al., 2017; Yang et al., 2017, 2019). In developing Chinese and Indian cities, when commuters are located in the outer areas, more than 10 km from the city center, their commuting CO2 emissions per trip tend to be about 0.3 kg larger than emissions of those located in the inner areas, within 5 km from the center (Yang et al., 2020).

Presently, numerous cities have begun metro or light rail constructions to provide mass public transportation services, with fast speeds, for the urban residents. Plenty of scholars have started to focus on the impacts of rail transit on travel behaviors and transport CO2 emissions. Moving into metro neighborhoods is found to be positively related to decreased driving distances by autos and more transit uses (Cao & Ermagun, 2016; Huang et al., 2019), and living in new housing near rail stations can reduce auto ownership (Chatman, 2013). Evidence from a study based in Minneapolis shows that in light rail corridors, residents’ vehicle miles driven will be reduced by approximately 20% more than those in the urban control corridors, after controlled for neighborhood characteristics (Cao, 2019). Driving distances can be reduced by 10 miles per day among the households living within 1 km walking distance to the light rail transits, compared to controlled households located farther away (Spears et al., 2017). Households located within a half mile of light rail transits produce smaller transport CO2 emissions (Boarnet et al., 2017). Developing cities with metro services will generally produce less resident transport CO2 emissions by 43–62% by 2050 (Yang et al., 2020). In recent years, some new forms of transfer modes greatly promote the first-and-last mile access trips to metros, such as shared bicycles/cars, feeder buses/customized shuttle buses, electric bicycles/motors, and electric scooters (Baek et al., 2021; Chen et al., 2021; Zuo et al., 2020). These convenient transfers have attracted more residents to use transit and metro modes, and have reduced transport CO2 emissions to some extent.

Also, recent study results show that nature-based solutions can improve and protect ecosystem services, and can bring about changes in land use and land cover in urban areas (Zwierzchowska et al., 2021; Pan et al., 2021). Greenways can reduce pedestrian exposure to air pollution (Ahn, et al., 2021), and residents living near the greenway drive less, and thus, reduce their transport emissions (Ngo et al., 2018). Pedestrian and environmentally-friendly designed streets can promote walkability and access to metro trips (Sun, Zacharias, et al., 2016; Sun, Zhou, et al., 2016). All these nature-based solutions will be beneficial for reducing transport emissions and mitigating climate change.

The above predictors of transport CO2 emissions have been discussed extensively, but limitations still exist. At present, few studies describe the varied effects of the impact factors at different locations of transport CO2 emission distribution. It is vital to obtain heterogeneous emission factor characteristics comprehensively to effectively make planning strategies and implement specific emission reduction policies to address the current developing situations of Chinese and Indian cities. These development situations include urban growths, motorization and economic increases, metro and rail network constructions, urban agglomeration developments, energy technology improvements, and new energy vehicle promotions. In order to identify factors’ heterogenous effects, models based on the conditional mean method, which are frequently used in the previous studies (Huang et al., 2019; Spears et al., 2017; Boarnet et al., 2017; Sun, Zhou, et al., 2016; Sun, Zacharias, et al., 2016; Brand et al., 2013; Brand & Preston, 2010; Büchs & Schnepf, 2013; Cirilli & Veneri, 2014), are not appropriate. The conditional mean method can only provide average effects of the impact factors from transport emissions, neglecting the various characteristics of factors’ effects among the high and low emitters. In addition, the conditional mean method generally assumes that the outcome variable is normally distributed and homoscedastic. However, in most cases, these two assumptions cannot be satisfied. In terms of transport CO2 emissions, they are always larger than zero and are non-normally distributed in most cases. If the conditional mean method is applied for the transport emission model estimation, the results would probably be biased, which may lead to ineffective or insufficient planning strategies or policy suggestions. However, the quantile regression method can overcome the above shortcomings. This method is robust to outlying observations and does not make distributional assumptions (Koenker & Hallock, 2001; Xu & Lin, 2016). It can provide specific estimations on each quantile of the emission distribution and, therefore, describe the relations between the outcome variable and the independent variables more completely (Cameron & Trivedi, 2009).

To address the above limitations in the existing research, this study will explore the heterogeneous relationships among the impact factors and transport CO2 emissions in typical developing Chinese and Indian cities. The quantile regression model will be used, and then, varied characteristics of factors’ effects can be analyzed at high and low levels of transport emissions. The significant impact factors’ heterogenous effects to be identified include car availability, distance to the center/subcenters, polycentric and satellite city forms, and whether the city has metro services or not. According to the model results, rational planning strategies will be proposed for reducing transport CO2 emissions and mitigating climate change in developing cities.

3 Data and methodology

3.1 Data collection and description

Simple random samplings were carried out in the urban areas of Beijing, Xi’an, Wuhan, and Bangalore in the years of 2010, 2012, 2010, and 2011–2012, respectively. Face-to-face inquiries were implemented in the household neighborhoods. Altogether, 1,400 households and 1,915 commuters were interviewed in Beijing, 1,501 households and 2,449 commuters in Xi’an, 1,194 households and 2,050 commuters in Wuhan, and 1,967 households and 3,934 commuters in Bangalore. The questionnaires included inquiries related to commuting trip information (distance, travel mode, workplace, and household location) and household and commuter socio-economic characteristics (car availability, household income, housing tenure, age, work unit type, and educational background). Home-to-center distances (HCD) in Beijing, Xi’an, and Bangalore, and home-to-subcenter distances (HSD) in Wuhan, were calculated by using ArcGIS software. The same calculations were done for home-to-work distances, as well.

Beijing is the political, economic, and cultural center of China. Beijing has 1,268 km2 of urban built-up area, and there is a population of 12.8 million people in the main urban area. The per capita GDP amounts to about US$ 11,218, and the motor vehicles amount to 4.8 million. Xi’an has 522 km2 of urban built-up area, and there is a population of 4.5 million people in the main urban area. The per capita GDP is approximately US$ 8,140, and the motor vehicles are about 1.47 million. Wuhan has 520 km2 of urban built-up area, and there is a population of 5.46 million people in the main urban area. The per capita GDP is about US$ 10,563, and the motor vehicles are about 1.19 million. Bangalore has 741 km2 of urban built-up area, and there is a population of 5.83 million people in the main urban area. The per capita GDP is about US$ 8,664, and the two-wheelers amount to 3.72 million, taking up 69.1% of the total motor vehicles. In regards to the aspect of urban form, monocentric patterns have been fostered in Beijing, Xi’an, and Bangalore, while Wuhan developed polycentric forms, with three towns (HanKou, WuChang, and HanYang) separated by the Yangtze and Han Rivers, since the city’s initial formation. Satellite cities in the outer areas of Beijing have evolved in recent decades, including Changping, Huairou, Shunyi, Miyun, Pinggu, Fangshan, and Daxing. More detailed information of the four case cities can be obtained in the literature of Yang et al. (2020). The three Chinese cities’ metro lines have been in operation, but Bangalore’s metro line was under construction during the survey year.

Commuting CO2 emissions are equal to the CO2 emission factor (by mode, fuel type, and occupancy) multiplied by the commuting trip distance (IPCC 1997). Well-To-Wheel (WTW) CO2 emission intensities for different fuel types were calculated to obtain CO2 emission factors. The calculation method is described in greater detail in Wang et al. (2017) and Yang et al., (2017, 2019, and 2020). Table 1 reports the statistics and percentiles of individual commuting CO2 emissions in the four city samples. Bangalore in India has larger transport CO2 emissions than the Chinese city of Xi’an, despite their similar economic levels and urban forms. Beijing’s top 25% emitters produce the most transport CO2 emissions. Wuhan has the smallest transport CO2 emissions, and Xi’an’s emissions are in the middle level.

Table 1 Summary statistics of individual commuting CO2 emissions
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3.2 The quantile regression model

The quantile regression model was first introduced by Koenker and Bassett (1978). This method can depict a more complete picture about the relationship between the outcome \(y\) and the regressors \(x\) at different points in the conditional distribution of \(y\) (Cameron & Trivedi, 2009). Compared to the conditional mean regression method used in the previous studies, such as the frequently used ordinary least square (OLS) method, or other methods based on the OLS method, quantile regression has several advantages. First, OLS regression is sensitive to outliers; however, quantile regression estimates are more robust to address this problem. Second, quantile regression could estimate the covariate effects on any percentile of the distribution, not only obtaining the conditional mean estimation on the entire distribution. Third, the quantile regression method avoids assumptions about the regression error distribution. This means that if the regression error is not normally distributed and is heteroscedastic, using OLS method would produce biased estimation results. The quantile regression method does not need to be applied under the above assumptions. (Cameron & Trivedi, 2009; Wang et al., 2019; Xu & Lin, 2016).

The standard quantile approach is used to specify the conditional quantile function to be linear, and parameters of the intercept and slope may vary with each quantile. The \(q\) th conditional quantile function of \(y\) given \(x\) is denoted as \(Q_{q} \left( {y|x} \right)\). The standard linear conditional quantile function is

$$y_{i} = {\varvec{x}}_{i}^{\prime } {\varvec{\beta}}_{q} + \varepsilon_{qi} ,\quad 0 < q < 1$$
(1)
$$Q_{q} \left( {y_{i} |{\varvec{x}}_{i} } \right) = {\varvec{x}}_{i}^{\prime } {\varvec{\beta}}_{q}$$
(2)

where \(Q_{q} \left( {y_{i} |x_{i} } \right)\) means the \(q\) th quantile of the dependent variable \(y_{i}\); \({\varvec{x}}_{i}^{\prime }\) indicates the vector of independent variables; \({\varvec{\beta}}_{q}\) is the vector of estimated coefficients; \(\varepsilon_{qi}\) indicates a random error term.

The \(q\) th quantile regression estimator \(\widehat{{\varvec{\beta}}}_{q}\) is the solution of minimizing the following function

$$Q\left( {{\varvec{\beta}}_{q} } \right) = \sum\limits_{{i:y_{i} \ge {\varvec{x}}_{i}^{\prime } {\varvec{\beta}}_{q} }}^{N} q \left| {y_{i} - {\varvec{x}}_{i}^{\prime } {\varvec{\beta}}_{q} } \right| + \sum\limits_{{i:y_{i} < {\varvec{x}}_{i}^{\prime } {\varvec{\beta}}_{q} }}^{N} {(1 - q)} \left| {y_{i} - {\varvec{x}}_{i}^{\prime } {\varvec{\beta}}_{q} } \right|$$
(3)

From Eq. (3) it can be seen that quantile regression could be considered as a weighted regression. As Cameron and Trivedi (2009, pp. 207) point out, “If \(q = 0.9\), for example, then much more weight is placed on prediction for observations with \(y_{i} \ge {\varvec{x}}_{i}^{\prime } {\varvec{\beta}}_{q}\) than for observations with \(y_{i} < {\varvec{x}}_{i}^{\prime } {\varvec{\beta}}_{q}\)”.

Since the objective function (3) is not differentiable, it is not applicable to use gradient optimization methods. However, the linear programming method can be used and can provide relatively fast computation of \(\widehat{{\varvec{\beta}}}_{q}\) (Cameron & Trivedi, 2005). The following concisely introduces this method, quoted from StataCorp (2017, pp. 2158):

“Define \(\uptau\) as the quantile to be estimated; the median is \(\tau = 0.5\). For each observation \(i\), let \(\varepsilon_{i}\) be the residual

$$\varepsilon_{i} = y_{i} - {\mathbf{x}}_{i}^{^{\prime}} \widehat{{\varvec{\beta}}}_{\tau }$$

The objective function to be minimized is

$$\begin{aligned} c_{\tau } (\varepsilon _{i} ) = & \left( {\tau {\mathbf{1}}\{ \varepsilon _{i} \ge 0\} + \left( {1 - \tau } \right){\mathbf{1}}\{ \varepsilon _{i} < 0\} } \right)\left| {\varepsilon _{i} } \right| \\ = & \left( {\tau {\mathbf{1}}\{ \varepsilon _{i} \ge 0\} - \left( {1 - \tau } \right){\mathbf{1}}\{ \varepsilon _{i} < 0\} } \right)\varepsilon _{i} \\ = & \left( {\tau - {\mathbf{1}}\{ \varepsilon _{i} < 0\} } \right)\varepsilon _{i} \\ \end{aligned}$$
(4)

where \({\mathbf{1}}\left\{ \cdot \right\}\) is the indicator function. This function is referred to as the check function; the slope of \(c_{\tau } (\varepsilon_{i} )\) is \(\uptau\) when \({\upvarepsilon }_{\mathrm{i}}>0\) \(\varepsilon_{i} > 0\) and is \(\tau - 1\) when \({\upvarepsilon }_{\mathrm{i}}<0\) \(\varepsilon_{i} < 0\), but is undefined for \({\upvarepsilon }_{\mathrm{i}}=0\) \(\varepsilon_{i} = 0\). Choosing the \({\widehat{\upbeta }}_{\uptau }\) \(\widehat{{\varvec{\beta}}}_{\tau }\) that minimize \(c_{\tau } (\varepsilon_{i} )\) is equivalent to finding the \(\widehat{{\varvec{\beta}}}_{\tau }\) that make \(\boldsymbol{x\beta }_{\tau }\) best fit the quantiles of the distribution of \(y\) conditional on \({\varvec{x}}\).

This minimization problem is set up as a linear programming problem and is solved with linear programming techniques. Here \(2n\) slack variable, \(u_{n \times 1}\) and \(v_{n \times 1}\), are introduced, where \(u_{i} \ge 0,v_{i} \ge 0\) and \(u_{i} \times v_{i} = 0\), reformulating the problem as

$$\min_{{{\varvec{\beta}}_{\tau } ,u,v}} \{ \tau {\mathbf{1}}_{n}^{^{\prime}} u + \left( {1 - \tau } \right){\mathbf{1}}_{n}^{^{\prime}} v|y - \boldsymbol{X\beta }_{\tau } = u - v\}$$
(5)

where \({1}_{\mathrm{n}}\) \({\mathbf{1}}_{n}\) is a vector of \(1s\). This is a linear objective function on a polyhedral constraint set with \(\left(\begin{array}{c}\mathrm{n}\\ \mathrm{k}\end{array}\right)\) \(\left( {\begin{array}{*{20}c} n \\ k \\ \end{array} } \right)\) vertices, and the goal is to find the vertex that minimizes (4). Each step in the search is described by a set of \(k\) observations through which the regression plane passes, called the basis. A step is taken by replacing a point in the basis if the linear objective function can be improved. If this occurs, a line is printed in the iteration log. The definition of convergence is exact in the sense that no amount of added iterations could improve the objective function. A series of weighted least-squares (WLS) regression is used to identify a set of observations as a starting basis. The WLS algorithm for \(\tau = 0.5\) is taken from Schlossmacher (1973) with a generalization for \(0<\uptau <1\) \(0 < \tau < 1\) implied from Hunter and Lange (2000).”

The bootstrap method is applied to acquire the standard errors and confidence intervals of the estimated coefficients in the quantile regression model. In order to obtain a sound estimation, the bootstrap replications should be more than 500 or 1,000 times (Efron & Tibshirani, 1993). In our estimations, the bootstrap replications are 1,000 times.

The independent variables in the quantile regression model consist of the following impact factors: household car availability, household annual income, dummy variable of whether the city form is polycentric or has a strong center, dummy variable of whether a commuter is located in the satellite cities, dummy variable of whether a city has metro services, and distance from home to the city center/subcenter (HCD/HSD). The dependent variable is individual commuting CO2 emissions in the form of a natural logarithm. Table 2 presents the definitions and the summary statistics of the variables in the quantile regression models.

Table 2 Definitions and summary statistics of the variables in the quantile regression models
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4 Quantile regression model results

4.1 Pooled samples of four case cities

Columns 1 to 5 in Table 3 shows the quantile regression model results of the pooled samples at the 10th, 25th, 50th, 75th, and 90th quantiles of commuting CO2 emissions. Column 6 in Table 3 shows the estimation results using the OLS regression method. Figure 1 illustrates the varied coefficients at different quantiles of the emissions, the coefficients’ mean levels using the OLS method, and the distribution of commuting CO2 emissions. It is found that the signs of the estimated coefficients are consistent between the quantile regression and OLS methods. The OLS coefficients are approximately at the middle level of the upper and lower limits of the quantile regression method Fig. 2.

Table 3 Quantile regression model results of four case city pooled samples
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Fig. 1
figure 1

Quantile regression coefficients and commuting CO2 emission distribution. Note: (a). The lines in bold black refer to the coefficients of OLS regression; (b). The lines in dark green refer to the varied coefficients in the quantile regression; (c). The gray bands refer to the confidential intervals of the coefficients in the quantile regression; (d). The bar graph shows the distribution of commuting CO2 emissions

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Fig. 2
figure 2

Percentages of mode choice and car availability below and above the 10th, 25th, 50th, 75th, and 90th quantiles in the pooled samples

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Results in Table 3 show that the changing tendency of the coefficient of car availability turns out to be an inverted U-shape. As commuting CO2 emissions increase from the lower level to the middle level, the positive coefficient of car availability displays a tendency to increase rapidly. At the 10th quantile, car availability could increase 69.5% of the emissions, while this percentage will amount to 118% at the 50th quantile. When the commuting CO2 emissions are larger than the middle level, the positive coefficient of car availability has a slight decrease, increasing 93.2–115.8% of the emissions. Car availability’s effect on increasing the emissions rises by approximately 30% as emissions rise from the 10th quantile to the 50th quantile. As shown in Fig. 3, the marginal effects of car availability are between 33.36 g and 72.38 g of CO2 per trip. At the 75th quantile, the marginal effect turns out to be the largest, registering at 2.2 times that of the minimum. These results are mainly due to the fact that, shown in Fig. 2 and Fig. 4, longer commute distances (averaging 11 km), as well as higher car mode share (66.3%), exist among the commuters with emissions larger than the middle level. Among the low emitters, with emissions smaller than the middle level, the average commute distance is only 4.2 km, and the car mode share is only 17.9%. Conversely, the bus mode share for these individuals is as high as 62.1%.

Fig. 3
figure 3

Average marginal effects on commuting CO2 emissions below and above the 10th, 25th, 50th, 75th, and 90th quantiles in the pooled samples

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Fig. 4
figure 4

Average distance below and above the 10th, 25th, 50th, 75th, and 90th percentiles in the pooled samples

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The changing tendency of the coefficient of HCD/HSD also turns out to be an inverted U-shape. The positive coefficient of HCD/HSD has a slight increasing trend as commuting CO2 emissions increase to the 75th quantile. At the 25th quantile, a 1 km increase of HCD/HSD causes the emissions to increase by 2.16%; at the 75th quantile, this quantity increases to 3.77%. HCD/HSD’s effect on increasing the emissions rises by 32% as emissions rise from the 25th quantile to the 75th quantile. At the 90th quantile, the positive coefficient of HCD/HSD has a slight decrease, increasing 2.46% of the emissions. The marginal effects of HCD/HSD also increase as emissions increase to the 75th quantile. Figure 3b shows that a 1 km increase of HCD/HSD could cause 1.1 g to 1.67 g increases of emissions per trip. At the 75th quantile, the marginal effect becomes the biggest, at 2.1 times that of the minimum. These results are caused by the increased commute distances as quantiles increase, from the smallest average level of 1.47 km, increasing to the largest average level of 15.75 km, shown in Fig. 4.

Model results also indicate that, among the high emitters with emissions larger than the middle level, satellite city forms could reduce 56.4–92.4% of the commuting CO2 emissions. These effects are not statistically significant among the low emitters at the 10th or 25th quantiles. Figure 3(d) shows that the marginal effects of satellite cities are between − 38.35 g and − 57.75 g of CO2 per trip. At the 75th percentile, the marginal effect turns out to be the largest, at 1.5 times that of the minimum. Additionally, polycentric urban forms could generally reduce commuting CO2 emissions. At the 10th, 25th, and 90th quantiles, polycentricity could reduce 39.9%, 14.8%, and 11.7% of the emissions, respectively. Figure 3c reports that the marginal effects of polycentric forms are between − 7.55 g to − 19.15 g of CO2 per trip. At the 10th percentile, the marginal effect becomes the largest, calculated as being 2.5 times that of the minimum level.

The significant effects polycentric or satellite city forms have on reducing emissions can be illustrated from the following characteristics. High emitters are characterized by much longer commute distances and more frequent car usage, especially for the high emitters in the top 10th percentile. These commuters have an average travel distance of 15.75 km, with a car mode share of 89.6%, shown in Fig. 2e and Fig. 4b. However, polycentric and satellite city urban forms can promote job-to-housing balances, shorten commute distances, and, thus, reduce driving frequency, which will substantially lower transport CO2 emissions. Among the samples taken from the polycentric city of Wuhan, fewer driving trips, more non-motorized trips, and more intra-trips inside the three subcenters existed, resulting in lower levels of transport CO2 emissions. Among the samples taken from the satellite cities in Beijing, more local trips inside the satellite cities were observed, and, thus, the transport CO2 emissions were lower, compared with the sprawling areas.

Based on the above results, it can be concluded that controlling the percentage of oil-fueled cars, shortening the HCD/HSD, and developing satellite cities have the largest effects for reducing transport CO2 emissions from the emitters of the middle and higher levels. Moreover, these middle and higher levels of emitters account for about 50% of the total emitters. Therefore, the above policies will have enormous effects on transport CO2 emission reductions.

Another notable finding reveals that the effects metro services have on reducing the emissions decrease as the emissions increase from the 25th quantile to the 90th quantile. At the 10th quantile, cities with metro services could reduce 34.3% of the emissions, and at the 25th quantile, cities with metro services could reduce 39.1% of the emissions; while at the 75th quantile, this effect drops to 13.5%, and at the 90th quantile, this effect becomes the lowest, at only 9.41%. Calculated from the model results, it can be obtained that the effects of metro services reducing the emissions will suffer a 19% decrease when commuting CO2 emissions increase by 1 kg. Figure 3e and Fig. 5 indicate that the marginal effects of this impact factor are between − 19.94 g to − 6.4 g of CO2 per trip. At the 25th quantile, the marginal effect is the greatest, at 3.1 times that of the minimum. On the one hand, these results are due to the increasing tendencies of car availability, household income, driving frequencies, and commute distances, and the decreasing tendency of metro mode usage as transport CO2 emissions increase, as reflected in Fig. 2 and Fig. 4. Despite the advanced metro services and metro networks in the sprawling areas in Beijing, commuters will still heavily rely on driving when car availability and long-distance commutes are present. Another possible reason lies in the long travel times that exist while transferring to other modes when using buses and metros. For instance, among the bus or metro users of Beijing, half of the commuters take more than a 1/3 of their total travel time just in transferring; among the bus users in Bangalore, half of the commuters spend more than 1/2 of their total travel time before and after riding the bus.

Fig. 5
figure 5

Commuters’ percentages, transport CO2 emissions per trip, and metro’s marginal effects at different quantiles. Note: HWD refers to home-work distance in kilometers; CA refers to car availability

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Figure 5 shows that, between the 25th quantile and the 50th quantile of the emissions, metro’s effects on reducing the emissions have a great extent of decrease. We analyze the samples’ characteristics between the 25th to 50th quantile, between the 50th to 75th quantile, and between the 75th to 90th quantile. It is found that, when commuting distance reaches and exceeds 5.8 km, and the percentage of car availability reaches 41.2% or more, metro’s effects in reducing the emissions drop continuously at a rate of 37.8%. Thus, for low-carbon transportation development, it is suggested to form employment and life circles within a 5–6 km radius. Meanwhile, in order to attract more high emitters to use metros, better transit resources, such as feeder buses or customized shuttle buses, need to be allocated around the metro stations. For the first-and-last mile access to metros, shared bicycles and electric scooters are now popularly used (Baek, 2021; Zuo et al., 2020; Chen et al., 2021), which can expand the access distance to metros, compared with walking, making transfer to metros more convenient for those residents located farther away from the station. These non-motorized travel modes need to be encouraged. Also, urban roads with better walkability and greenways can encourage more travelers to walk or use non-motorized traffic modes in their transfers to metros (Ahn, et al., 2021; Pan et al., 2021; Ngo et al., 2018; Sun, Zhou, et al., 2016; Sun, Zacharias, et al., 2016). Therefore, bicycle lanes, pedestrian streets, and greenways need to be constructed with high service levels. These above suggested measures will be beneficial for the first-and-last mile access to metros and will make metros more attractive to those commuters with cars and high income. In addition, it is necessary to combine proper traffic demand management policies and metro network construction together to control the driving frequencies. The percentage of oil-fueled cars in ownership needs to be controlled, and newer energy-operated vehicles need to be encouraged through the use of transport policies. Car ownership restriction or congestion pricing policymaking need to consider the travel behaviors and sensitivity of travel consumptions among the high-income commuters.

By using the model equations at the 50th, 75th, and 90th quantiles, the increasing rates of transport CO2 emissions produced by the high emitters were calculated when the impact factors’ values varied. These impact factors include distance to the city center, metro provision, mono/polycentric form, and whether satellite cities exist. The high emitters refer to those with car availability and higher household annual incomes between US$ 20,000–40,000. The results are shown in Fig. 6. As the distances to the city center increase, the increasing rates of transport CO2 emissions increase continuously. At the 50th quantile, the increasing rates change between 0.01 and 0.07, at the 75th quantile, the increasing rates change between 0.02 and 0.18, and at the 90th quantile, the increasing rates change between 0.03 and 0.14. Generally, the increasing rates of transport CO2 emissions become larger as the emissions increase. When there are metro provisions, polycentric forms, or satellite cities, the increasing rates of transport CO2 emissions will decrease, especially for the scenarios with satellite city developments. It is noteworthy that, under the scenarios with polycentric and satellite city developments, as the distances to the city center increase, though, the increasing rates of transport CO2 emissions will have more declines. At the 75th quantile, there exist larger extents of the decreased emissions when the distance to the city center is more than about 8–10 km, and at the 50th and 90th quantiles, the emissions will have more decreases when the distance to the city center is more than about 15–18 km. These results indicate that when the urban radius is more than approximately 10–15 km, transport CO2 emissions in the outer areas will increase significantly, while developing polycentric and satellite cities can greatly decrease the increasing extents in the outer areas. These results suggest that, for transport emission reductions, it is necessary to control the urban development radius within 10–15 km under the monocentric pattern, and when the city continues to sprawl, polycentric structures and satellite cities need to be formed.

Fig. 6
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Increasing rates of transport CO2 emissions and distances to the city center under different scenarios

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4.2 Samples of four single cities, car availability, high income, and polycentric/satellite cities

In order to test the robustness of the heterogeneous effects of the impact factors, different quantile regression models were established by using different subsamples. These subsamples include samples of the four single cities, a sample of the commuters with car availability, a sample of the commuters with high household annual income (> US$ 40,000), and a sample of the commuters located in the polycentric city or satellite city.

Table 4 shows the model results in the samples of the four single cities. In the cities of Beijing, Xi’an, and Bangalore, all of which have one strong center, the positive coefficients of car availability and HCD/HSD increase stably along with the increase of emissions, while in the polycentric city of Wuhan, such an increasing tendency does not exist. The positive coefficients of car availability become smaller when the emissions are larger than the middle level. Additionally, at the 10th, 25th, 50th, and 90th quantiles, HCD/HSD does not have statistically significant effects on the emission increases, which illustrates that HCD/HSD is not the significant factor behind emission increases, under the polycentric form.

Table 4 Quantile regression model results of four case city samples
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Results in Tables 5 and 6 report that, in the sample of the commuters with car availability, satellite city form’s effects in reducing the emissions are statistically significant among high emitters at the 50th, 75th, and 90th quantiles. At the 75th quantile, satellite city form could reduce as much as 117.8% of the emissions, which is about 2 times that of the minimum. The marginal effects are between − 48.8 g and − 98.5 g of CO2 per trip. HCD/HSD effects are statistically significant and have a slight increasing tendency. At the 50th, 75th, and 90th quantiles, a 1 km increase in HCD/HSD could increase emissions by 2.04%, 3.09%, and 2.97%, respectively, with the marginal effects between 1.7 and 2.6 g of CO2 per trip, showing larger effects compared with those in the pooled samples. At the 75th quantile, the marginal effect is the greatest, at about 1.53 times that of the minimum.

Table 5 Quantile regression results from the subsamples of car availability, high-income, and polycentric/satellite city
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Table 6 Marginal effects on commuting CO2 emissions
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In the sample of the commuters with higher household annual incomes (> US$ 40,000), the positive coefficient of car availability increases with the emission increases, with the marginal effects between 254.5 g and 340.4 g of CO2 per trip. At the 75th quantile, the marginal effect is the largest, at about 1.3 times that of the minimum. The effect of polycentric forms reducing emissions is significant among high emitters, with the marginal effects between − 79.7 g to − 145.9 g of CO2 per trip. At the 90th quantile, the average marginal effect becomes the largest, at about 1.8 times that of the minimum. Meanwhile, the effect of satellite city forms reducing emissions is also significant among high emitters, with much larger marginal effects between − 726.9 g and − 1202.5 g of CO2 per trip. At the 75th quantile, the marginal effect becomes the maximum, at about 1.7 times that of the minimum. Notably, the positive coefficients of HCD/HSD in the high-income sample are much larger than those in other samples. At the 25th, 50th, and 75th quantiles, a 1 km increase in HCD/HSD could increase the emissions by 10.8%, 9.06%, and 8.26%, respectively, with the marginal effects between 12.4 g and 18.5 g of CO2 per trip. At the 25th quantile, the marginal effect turns out to be the largest, at about 1.5 times that of the minimum.

Looking at the results in the sample of the commuters located in the polycentric or satellite cities in Table 5, no increasing tendency exists in the positive coefficients of car availability, which is similar to the model results for the Wuhan sample in Table 4. In addition, the results show that the effect of car availability increasing the emissions becomes smaller compared with those in the high-income sample, and the effects of HCD/HSD increasing emissions also become smaller. A 1 km increase of HCD/HSD will increase emissions by 1% to 1.4%, with marginal effects between 0.4 to 0.6 g of CO2 per trip.

5 Conclusion

In this study, to make rational planning strategies for reducing transport CO2 emissions and climate change, heterogeneous effects of the significant impact factors from transport emissions were measured in four typical developing Chinese and Indian cities.

The quantile regression method was applied, because it can overcome the shortcomings of the conditional mean method, frequently used in the previous literature. The conditional mean method can only provide the mean level of the impact factors’ effects, and the regression error has normal distribution and homoscedastic assumptions, while the quantile regression method can estimate the covariate effects on any percentile of transport emission’s distribution and is not limited to the conditional mean model’s assumptions. The quantile regression model is estimated by linear programming techniques, and the standard errors and confidence intervals of the estimated coefficients are calculated by the bootstrap method.

The significant impact factors of transport CO2 emissions include household car availability, mono/polycentric urban form, satellite city development, metro service, and distance from home to the city center/subcenter. The statistical test and robust test results indicate the reliability of the model results. According to the study results, specific urban and transport planning strategies are proposed to reduce transport CO2 emissions under Chinese and Indian cities’ development situations. These development situations include urban growths, metro and rail network constructions, urban agglomeration formations, motorization and economic increases, energy technology improvements, and new energy vehicle promotions. The above development situations are equally important issues for other global cities. Therefore, this study’s findings will be beneficial to transport CO2 emission reductions globally.

Model results indicate that the marginal effects of a city having metro services vary from − 19.94 to − 6.4 g of CO2 per trip. From the lowest emitters to the highest emitters, the effects of metros reducing the emissions drop at a rate of 27%, averagely. That means metro service provisions could not bring about substantial emission reductions among the high emitters, while the marginal effects of car availability change from 33.4 to 340.4 g of CO2 per trip. From the low emitters to the high emitters, the effects of car availability increasing the emissions have a general rising tendency. The largest marginal effect of car availability increasing the emissions is seen within the high-income sample (254.5 g to 340.4 g of CO2 per trip). It is also found that, after the commute distance reaches 5.8 km or more and the car availability’s percentage amounts to 41.2% or greater, metro’s effects on reducing the emissions decrease continuously at a rate of 37.8%.

Therefore, for low-carbon transportation development, it is recommended to form employment and life circles within a 5–6 km radius. Dependence only on metro construction cannot bring about desired emission reductions in future. It is necessary to combine traffic demand management policies together to reduce driving, including controlling the percentage of oil-fueled cars owned, car use restriction, and congestion pricing. In the policymaking of car restriction or congestion pricing, it is necessary to consider the travel behaviors and sensitivity of travel consumptions among the high-income commuters. In addition, to attract high emitters toward using public transit and metro services more often, better public transit resources (feeder buses or customized shuttle buses) need to be allocated around the metro stations. Additionally, high service levels of bike lane facilities, pedestrian streets, and greenways need to be constructed. These could attract more travelers to use low-carbon traffic modes when transferring to metros, and attract more residents located farther from metro stations to access metros.

Model results also indicate that the marginal effects of HCD/HSD are between 0.5 to 18.5 g of CO2 per trip. The effects of HCD/HSD contributing to the increase of emissions grow at a rate of approximately 32.1%. In the sample of the commuters with car availability, marginal effects of HCD/HSD are a little larger (1.7–2.6 g of CO2 per trip) than those in the pooled samples (1.1–2.3 g of CO2 per trip), and in the high-income sample, the marginal effects turn out to be the largest, at 12.4–18.5 g of CO2 per trip. Under future urban expansion and urban agglomeration development, HCD/HSD will continue to become longer. This reality, paired with the higher marginal effects of HCD/HSD among the high-income and car availability commuters, will inevitably create surges in transport CO2 emissions. In order to mitigate these rapid increases in future, it is of great importance to foster polycentric forms inside the main city and develop satellite cities in the outer areas, since the model results report that polycentric and satellite city forms could reduce transport CO2 emissions significantly, with marginal effects of − 145.9 g to − 7.6 g of CO2 per trip and − 1202.5 g to − 38.4 g of CO2 per trip, respectively. Additionally, these two factors’ effects become larger in the high-income and car availability samples. Furthermore, car availability’s and HCD/HSD’s effects on increasing the emissions decline under the scenarios of developing polycentric and satellite city forms. Under the scenarios with polycentric and satellite city developments, as HCD increases farther, the increasing rates of transport CO2 emissions have significant declines. When the distance to the city center is more than 8–10 km and 15–18 km, developing polycentric and satellite cities can greatly decrease the increasing extents of the emissions caused by the increase of HCD. These above results manifest that, under the situation of the urban form with one strong center, there will be smaller transport CO2 emissions if the urban radius is within approximately 10–15 km. When the city sprawls beyond this range, transport CO2 emissions will substantially increase. Therefore, it is necessary to control the urban radius within 10–15 km under the monocentric urban pattern and to foster polycentric structures and satellite cities in the outer areas.

In summary, for transport CO2 emission reductions, it is necessary to combine the following planning strategies together, including controlling the percentages of oil-fueled cars, metro and rail network construction, and providing better public transit services around metro stations. Meanwhile, bicycle lanes, pedestrian streets, and greenways need to be constructed with high service levels for the first-and-last mile transfer to public transit or metros. Also, employment and life circles are suggested to be within a 5–6 km radius, and urban radius is recommended to be 10–15 km under the urban form with one strong center. When the city continuously sprawls, polycentric structures and satellite cities need to be formed.