Analyzing Temporal and Spatial Characteristics and Determinant Factors of Energy-Related CO₂ Emissions of Shanghai in China Using High-Resolution Gridded Data

Abstract

In this study, we create a high-resolution (1 km x 1 km) carbon emission spatially gridded dataset in Shanghai for 2010 to 2015 to help researchers understand the spatial pattern of urban CO₂ emissions and facilitate exploration of their driving forces. First, we conclude that high spatial agglomeration, CO₂ emissions centralized along the river and coastline, and a structure with three circular layers are the three notable temporal–spatial characteristics of Shanghai fossil fuel CO₂ emissions. Second, we find that large point sources are the leading factors that shaped the temporal–spatial characteristics of Shanghai CO₂ emission distributions. The changes of CO₂ emissions in each grid during 2010–2015 indicate that the energy-controlling policies of large point emission sources have had positive effects on CO₂ reduction since 2012. The changes suggest that targeted policies can have a disproportionate impact on urban emissions. Third, area sources bring more uncertainties to the forecasting of carbon emissions. We use the Geographical Detector method to identify these leading factors that influence CO₂ emissions emitted from area sources. We find that Shanghai’s circular layer structure, population density, and population activity intensity are the leading factors. This result implied that urban planning has a large impact on the distribution of urban CO₂ emissions. At last, we find that unbalanced development within the city will lead to different leading impact factors for each circular layer. Factors such as urban development intensity, traffic land, and industrial land have stronger power to determine CO₂ emissions in the areas outside the Outer Ring, while factors such as population density and population activity intensity have stronger impacts in the other two inner areas. This research demonstrates the potential utility of high-resolution carbon emission data to advance the integration of urban planning for the reduction of urban CO₂ emissions and provide information for policymakers to make targeted policies across different areas within the city.

1. Introduction

The urbanization process has spurred the consumption of energy and resources, which leads to climate change and environmental degradation, and has a significant impact on the natural environment and human production lifestyles [1]. Existing studies have shown that cities are the main sources of anthropogenic CO₂ emissions, with the amount accounting for more than 75% of global CO₂ emissions [2]. Urban expansion exacerbates vulnerability to climate change, while the advanced technologies and wealth that accompany urban development can be used to improve the capability for climate change mitigation and adaptation. Therefore, cities should play a key role in tackling climate change [3].

To quantify CO₂ emissions scientifically and accurately in urban areas is an important topic in the research of urban climate change. Recently, the needs for high-resolution carbon emissions data have risen due to both scientific and policy-related reasons.

In terms of the scientific needs, high-resolution carbon emissions data is the prior data for the inversion of observation data. At present, scientists can obtain remote sensing data of carbon concentration through satellites, but they still need to use the atmospheric inversion method to translate the observations from the satellite to the distribution of carbon fluxes that best fit the actual distribution. It is the distribution of carbon fluxes that can help scientists better understand the mechanisms of the global and regional carbon cycle and the carbon exchange among the atmosphere, the sea, and the land. The existing observation data and the atmospheric transport model are the two basic elements for the inversion method to simulate the spatial distribution of carbon concentration in a given area. However, due to the limited number of observation points, other prior information is needed to reduce the uncertainty of the inversion results. The high-resolution carbon emissions data based on regional carbon emissions inventory can be used as the prior data.

As for policymakers, there are two needs. Firstly, the high-resolution carbon emissions data combined with satellite observations can provide policymakers with a scientific and independent way to verify the effects of carbon mitigation policies. Even though this need has been concentrated at the global or national scale in the past, as cities become more important in the field of climate change, the need for high-resolution carbon emissions data at the urban scale has also increased. Secondly, high-resolution carbon emissions data can provide policymakers with more information than the traditional carbon emissions inventories, such as spatial information. Spatial attributes are one of the basic elements of the urban planning system. Wang et al. [4] demonstrated that urban spatial planning has a great impact on its carbon footprint, and that more work needs to be done to integrate the spatial planning with carbon mitigation. High-resolution carbon emission data can directly build the link between carbon emissions and urban spatial forms, which will raise the awareness of policymakers to integrate carbon reduction policies with urban planning.

Quantitative methods for CO₂ emissions include the mass balance method, the ground-based monitoring method, and the satellite observations method [5,6]. The mass balance method is the most commonly used estimation method, and it has been adopted by many studies to estimate the CO₂ emissions of different cities. Based on urban energy statistics, those studies compiled an energy-related CO₂ emissions inventory and then further analyzed the relationship between CO₂ emissions and other social–economic factors, such as the speed of economic development and the changes in industrial structure [7,8,9]. These studies can help policymakers understand the amounts of CO₂ emitted in cities and find the driving forces of the emissions. However, due to the lack of accuracy, the results still contain large uncertainty [10]. What is more, due to the lack of information about the spatial attributes, it cannot demonstrate the spatial differences in CO₂ emissions within the urban domain.

There are difficulties surrounding using the ground-based monitoring method to monitor the overall carbon emissions of a whole region due to the limited number of observation sites [11]. Generally, the data observed by the ground-based monitor is used as a priori data for carbon flux inversion methods to aid in the adjusting of estimation.

In recent years, with the number of CO₂-monitoring satellites increased, there have been research achievements regarding satellite monitoring data. Satellite observations provide a more independent, broad, and efficient means of monitoring atmospheric CO₂ emissions [12], and many studies used atmospheric measurements to produce temporal and spatial CO₂ data at city scales [13,14]. Scholars from the United States, France, Japan, and other countries have compared the data from CO₂ inventories with the satellite inversion data for those countries based on their CO₂ satellite data. The results conclude that CO₂ inventory data is consistent with satellite observations [15,16]. Nevertheless, studies using the satellite monitoring method are still subject to many uncertainties, including the mismatch between the resolution of the transport model, the spatial variability of the actual carbon fluxes and concentrations, and the uncertainties of parameters input into the inversion model [17].

Given the need for scientific, independent, continuous, and reliable data on CO₂ emissions and the critical importance of data verification to the study of climate change, integrating the three CO₂ quantification methods has become a trend in current academic research. Researchers used the atmospheric inversion method to translate the observations from the satellite to the optimized distribution of carbon fluxes concentration that best fit the actual distribution. The atmospheric inversion method uses a statistical method, which relies on the uncertainty in the prior estimate of emissions and other observation errors, to adjust and produce an optimized posterior estimate. Meanwhile, the mass balanced method and the ground-based observation method combined with the advanced technology of the Geographic Information System (GIS) are used to produce high-resolution carbon emission data. The combined method has been developed to determine the spatial and temporal distribution of CO₂ emissions at the global and regional level [18].

At the city level, high-temporal resolution CO₂ emissions inventories have been established as well. For instance, the US Vulcan project established an inventory of CO₂ emissions of the different sectors of US cities for each hour in 2002 [19,20]. Based on the Vulcan project, the Hestia project further improved the spatial and temporal resolution of the previous inventory data and attempted to develop a data system for CO₂ emissions on the scale of urban buildings, streets, and manufacturing facilities. A high-resolution carbon emissions data system has been constructed for four cities; these are Indianapolis, Indiana [21], Los Angeles, California [22], Salt Lake City, Utah [6], and Baltimore, Maryland. Based on the high-resolution energy-related CO₂ emissions data, the Indianapolis Flux Experiment (INFLUX) project was conducted by integrating the data of the high-resolution CO₂ emissions inventory, tower monitoring, and aircraft monitoring to explore ways to reduce uncertainties and establish the Measurement, Reporting, and Verifying (MRV) system. The results indicate that high-resolution energy-related CO₂ inventory data based on the bottom–up method, combined with the atmospheric inversion method, can be used to more accurately predict regional CO₂ emissions trends [22,23].

Compared with developed countries, due to the lack of data, studies of high-resolution CO₂ emissions spatial gridded data systems in China are relatively rare. In 2016, China launched a carbon satellite, named the TanSat, and relevant research has already been carried out [24]. Wang et al. [25] established a 10 km $\times$ 10 km CO₂ emissions map to reveal the spatial distribution of CO₂ emissions in China based on the point-source data of CO₂ emissions from power plants using the data derived from the First China Pollution Source Census (FCPSC). This research mainly introduced the methodology of mapping the high-resolution CO₂ emissions map and deduced the characteristics of CO₂ emissions in China. Cai et al. [26] created a China high-resolution emission gridded database from 288 Chinese cities and analyzed different characteristics of city CO₂ emissions by their locations and development states. This research revealed different driving factors and the degree of their impact on city CO₂ emissions in China’s different cities in various area regions. The study suggested that cities’ low carbon development strategies should adapt to local conditions. However, this study mainly analyzed the intercity differences of CO₂ emissions, and rarely analyzed the differences of CO₂ within the city. The internal differences of CO₂ emissions reveal the heterogeneous nature of the local situation, such as the urbanization state and land-use activities. The reasons behind these differences provide direction for urban policymakers to improve the process of transitioning to a low-carbon future.

This study takes Shanghai as a case study for several reasons: First, Shanghai is one of the world’s biggest cities, with an area of 6340 square kilometers and a population of more than 24 million in 2017. These characteristics make Shanghai a great representation of urban carbon emissions. Second, Shanghai’s statistic data are well organized and can fulfill the needs of establishing high-resolution CO₂ emissions data. Third, Shanghai has been accelerating the speed of energy transition and low carbon development since 2010. In 2012, Shanghai was selected as the pilot city of low carbon development; then, in 2013, it pledged to cap its coal consumption by 2017. The effects of this transition still need to be verified.

Based on the previous study on Shanghai’s 1 km × 1 km high-resolution energy consumption and carbon emissions gridded data system in 2010 [27], this study extended the data of energy consumption and its related CO₂ emissions from 2010 to 2015. Through the time-series gridded data, this study aims to answer the following questions: (1) What are the temporal and spatial characteristics of Shanghai’s CO₂ emissions? (2) What factors impact the energy-related CO₂ emissions in Shanghai? (3) Is there any effect of the carbon mitigation policy implemented by the Shanghai government since 2010? (4) What can be done for the policymakers to mitigate carbon emissions in the future?

This paper includes the following four parts. The first part introduces the research methods used in this study, including the method of creating, adding, and updating gridded data from 2010 to 2015 and the mechanism of the geographic detector. In the second part, we elaborate on the three spatial characteristics of Shanghai CO₂ emissions. We analyze the impact mechanism of Shanghai CO₂ emissions from the perspectives of large point sources and area sources in the third part. In the last part, we conclude some findings and policy recommendations.

2. Research Methods

2.1. Method of Processing and Updating Gridded Data

We use a bottom–up approach to construct a 1 km × 1 km Shanghai energy consumption and carbon emissions spatial grid database. This database contains three types of emissions sources—namely point, line, and area sources.

Point sources indicate energy consumption and carbon emissions generated with a specific longitude and latitude location. In Shanghai, the point source emitters include eight types: (1) 26 power plants, which are further divided into 20 coal-fired power plants, (2) four gas-fired power plants; (3) two oil-fired power plants; (4) eight centralized heating enterprises; (5) four iron and steel production sets; (6) seven chemical enterprises and chemical industry parks; (7) three gas plants; and (8) over 3000 middle and small coal-fired boilers. All of the point sources are located and mapped with the GIS.

Line sources refer to carbon emissions from transport along the traffic lines, including on-road transportation, waterway transportation, and air transportation. This research mainly includes carbon emissions from on-road transportation, but excludes waterway transportation and air transportation. We apply a “top–down” approach to estimate on-road transportation-related energy consumption and total associated CO₂ emissions. The mass balance method is used to estimate carbon emissions. Then, we use GIS to distribute the on-road traffic energy-related CO₂ emissions into each grid with the consideration of traffic flow by road types in different urban regions. The geographical information of Shanghai and the spatial distribution of point sources and line sources are shown in Figure 1.

Area sources refer to those sources that cannot determine the geospatial locations. Such emission sources are usually characterized by “low-quantity and wide-area diffused”, such as carbon emissions from the commercial sector, residential sector, and industrial enterprises below a designated size. We use social–economic data that can reflect the usage characteristics of various energy types in each area as the proxy system to indirectly obtain the gridded energy consumption and CO₂ emissions data. For example, population distribution by streets is used as the proxy system to allocate CO₂ emissions from the residential sector. The other proxy systems include the industry concentration, gross domestic product (GDP) of the tertiary industry, and GDP of the agriculture sector. More details of the equations and procedures for the estimation of CO₂ emissions from the three sources in Shanghai are shown in the supplementary material.

Based on our previous work of constructing the Shanghai Energy Consumption and Carbon Emissions Space Gridded Data System [27], this study added energy consumption and its related CO₂ emissions data to the system for the years 2011–2015 in a gridded format. The data for the year 2010 was also updated to be consistent with the new dataset. The main updated methods are:

(1) Adding the data of aviation kerosene consumption and its related CO₂ emissions to point sources. First, the data of total consumption of aviation kerosene for the years 2010–2015 is from the Shanghai Energy Statistical Yearbook, followed by the estimation of related CO₂ emissions. Second, based on the detailed land information, the airport land information is derived for locating each grid for Shanghai Hongqiao Airport and Shanghai Pudong Airport. Then, the CO₂ emitted from aviation kerosene data are assigned to these grids according to the proportion of aircraft landing data in the two airports.

(2) Designating CO₂ emissions from line sources in each year to the grids, where each part of the road is located. The total CO₂ emissions from line sources are calculated based on the statistical data from the Shanghai comprehensive traffic statistics yearbook.

(3) Updating the main proxy for distributing area source emissions. The demographic data are drawn from China’s Sixth Census in 2010, featuring small units and high resolution. Since the census is conducted every 10 years, the population data by streets that are used in this study for 2011–2015 are mainly based on the rate of change in the annual population demographic data, and are then assigned to the streets accordingly.

Besides, we also use a “top–down” approach to estimate fossil fuel CO₂ (ffCO₂) emissions in Shanghai. The “top–down” method uses the mass balance method to estimate ffCO₂ emissions. Activity data (fuel consumption data) is from the Shanghai Energy Statistical Yearbook. The emission factor of each type of energy is from Intergovernmental Panel on Climate Change IPCC 2006 Guidelines for National Greenhouse Gas Inventories [28] and our studies [29]. The results estimated via the “top–down” method are used as a reference result in this paper. For more details regarding this method, please see the supplementary material.

2.2. Mechanism of the Geographic Detector

The Geographic Detector is a new spatial statistical method for analyzing spatial heterogeneity and detecting the potential driving forces that result in spatial differentiation. The principle of the geographical detector is that if a factor (X) has an impact on the distribution of the research target (Y), the spatial distribution of the factor (X) tends to be similar to that of the research target (Y) [30]. In this study, we assume that if the spatial distribution of a factor (X) is similar to that of Shanghai CO₂ emissions from area sources (Y), it denotes that this factor X has an impact on Y.

We apply the Geo Detector software [31] to detect the correlation between various factors and the area sources of CO₂ emissions. The Geo Detector consists of four detectors: namely, the risk detector, the factor detector, the ecological detector, and the interaction detector. The risk detector uses t-tests to identify whether the CO₂ emitted from various sub-regions are significantly different. It is used to measure and find the spatial heterogeneity of variable Y. The factor detector uses the q value to assess the impact of factors (X) on the spatial pattern of CO₂ emissions (Y). The ecological detector uses F-tests to assess whether the impact of two different factors (X1, X2) on the distribution of CO₂ emissions are significantly different. The interaction detector is used to assess whether the two factors (X1, X2) have an interactive influence on Y. By comparing the combined contributions with their independent contributions, the result will show whether the two factors can enhance or weaken each other, or if they can impact Y independently.

In this study, we apply the factor detector to quantify the determinant power of each factor (X) on the spatial distribution of Shanghai CO₂ emissions from area sources (Y). The key equations are as follows:

$$\mathrm{q}=1-\frac{SSW}{SST}=\frac{{{\displaystyle \sum }}_{h=1}^L{N}_h{\sigma }_h^2}{N{\sigma }^2}$$

(1)

Given a region consisting of N units (in this study, a unit is a 1 km x 1 km grid). The region can be divided into h (1≤ h ≤L) sub-regions according to the attributes associated with the geographical stratum of potential impact factors (X). L is the number of total strata. The total variance of Y of the region (SST) is denoted as $\mathrm{N}{\sigma }^2$, and the dispersion variance of Y over the sub-regions of the attributes h is denoted as ${\sigma }_h^2$. The q-statistic is used to indicate the degree of determinant power [32]. As shown in Equation (1), the q value range is [0, 1]. The larger the value is, the stronger the correlation of X with the spatial pattern of Y. In extreme cases, the value of q is 1, indicating that factor X completely controls the spatial distribution of Y, or the value of q is 0, indicating that factor X has no relation to Y, and the value of q represents the percentage of Y that is determined by X.

At present, Geo Detector is mainly applied in analyzing the driving forces that affect the spatial configuration of environmental issues [33,34] and urban development [35]. Recently, some researchers also used it to study the impact factors of regional CO₂ emissions. Wu et al. [36] applied the Geo Detector software to analyze the influencing factors of CO₂ emissions from the industrial sector in Inner Mongolia and proved its reasonable usefulness. This paper applied the Geographic Detector method to detect the leading factors that determine the spatial pattern of Shanghai’s energy-related CO₂ emissions from area sources.

Many scholars have used different regression models and decomposition models to study the drivers of urban CO₂ emissions from different perspectives. Kennedy et al. [37] compared the carbon emissions of multiple cities around the world, and found that population density, economic development, geographical location, and industrial structures are the key factors that affect urban carbon emissions. Besides, the method of carbon accounting can also impact the result of city CO₂ emissions. Marcotullio et al. [38] and Wu et al. [39] have performed similar studies, and population size, population density and the state of economic development are the common factors that were used to explain the growth and spatial pattern of urban CO₂ emissions. Besides, land-use change is the basic characteristic of the urbanization process. Many researchers have paid attention to the interaction between land use and climate change. Land-use types are closely linked to human activities, and have an impact on urban carbon emissions [40,41]. Meanwhile, many scholars have found that urban land-use types have a strong correlation with urban CO₂ emissions [42,43,44,45,46]. All of those researchers agreed that increasing the construction land use will increase the city’s CO₂ emissions. The land-use type and urban morphology in the city are the basic elements of urban planning [47], and urban planning plays an important role in reducing urban CO₂ emissions [48,49]. Based on the previous studies, we select five types and 10 factors that may affect the spatial distribution of CO₂ emissions in Shanghai. The five types of factors are population density, human activity, land-use types, energy efficiency, and urban planning. We use population activity intensity and construction land development intensity to represent human activity. We divide the land-use types into residential land, traffic land, public building land, and industrial land. The urban planning factor includes city-level types and types of urban circle layers.

3. Trends and Characteristics of Shanghai’s ffCO₂ emissions

3.1. Trends of ffCO₂ Emissions in Shanghai

Based on the results estimated via the “top–down” reference approach, we conclude that ffCO₂ emissions in Shanghai have reached its peak by 2011. Shanghai’s coal consumption reached its peak in 2011 when coal consumption by the power sector reached 39.23 Mt, and the total coal consumption reached 61.42 Mt. In 2011, ffCO₂ emissions in Shanghai reached 242.4 Mt, and ffCO₂ emissions per capita were 10.3t/person. Since then, ffCO₂ emissions in Shanghai have begun to decline due to reasons such as the increasing use of electricity that transferred from other provinces and the development of natural gas generation within Shanghai. In 2015, ffCO₂ emissions declined to 225.0MMt, and the ffCO₂ emissions per capita were 9.3t/person. Compared to the peak values, they declined by 7.2% and 10.1%, respectively.

ffCO₂ emissions estimated via the “bottom–up” approach are different from the results estimated via the reference approach. When ffCO₂ emissions estimated via the “bottom–up” approach, ffCO₂ emission sources are categorized into three types, which are the point sources, the line sources, and the area sources, and the total ffCO₂ emissions were around 10% lower than the reference result. There are two reasons for such differences. First, oil consumed by waterways is not included in the bottom–up approach. Second, the heat values of natural gas are calculated differently between the power plants and Shanghai statistical bureau. When we add up ffCO₂ emissions from waterway traffic to the results estimated via the bottom–up approach, the revised results via the “bottom–up” approach were less than 3% lower than the reference result. This difference demonstrates that the result estimated via the two approaches were consistent with each other. The results of the two approaches are shown in

Table 1. Results of fossil fuel CO₂ (ffCO₂) emissions estimated via two approaches (Unit: Mt-CO₂).

	Top–Down Approach					Bottom–Up Approach
	Coal Total	Petroleum Products Total	Natural Gas	Coke Moving in from Other Provinces	Total ffCO2	Coal Total	Petroleum Products Total	Natural Gas	Total ffCO2
2010	130.7	96.3	9.1	2.6	238.7	130.5	72.7	9.3	212.6
2011	135.7	92.6	11.2	2.9	242.4	135.2	71.5	11.2	217.9
2012	126.4	96.0	13.0	1.5	236.8	126.4	75.1	12.8	214.3
2013	125.5	98.8	14.7	2.9	242.0	125.4	79.1	14.3	218.9
2014	108.7	96.7	14.6	4.9	224.9	108.7	77.7	14.2	200.7
2015	105.4	101.1	15.6	2.9	225.0	105.4	82.3	14.1	201.7

.

3.2. High Spatial Clustering of CO₂ Emissions in Shanghai

3.2.1. Spatial Differences in ffCO₂ Emissions

As shown in Figure 2, same as in the past five years since 2010 [28], the differences in CO₂ emissions among Shanghai’s 7209 grids were still huge in 2015. In the box diagram, the CO₂ emissions in 332 grids are identified as abnormal values (Hinge = 3), accounting for approximately 5% of the total grids. In the percentile chart, the cumulative CO₂ emissions from the largest 1% of all the grids (i.e., 72 grids) account for 72% of the total CO₂ emissions. The total CO₂ emissions from the largest 50 grids account for 67.5% of the total CO₂ emissions. The cumulative CO₂ emissions from the top 20 grids account for 56.9% of the total CO₂ emissions, and the total CO₂ emissions from the top 10 grids account for 48.5% of the total CO₂ emissions. Moreover, when we look into the grids in more detail, we find that those grids represent the locations of coal-fired power plants, airports, and chemical enterprises and chemical industry parks. This result indicates that Shanghai’s ffCO₂ emissions are highly agglomerated and are spatially related to large energy consumption points.

3.2.2. The High Spatial Agglomeration of ffCO₂ Emissions Has Not Changed over Time

Shanghai’s coal consumption rose from 52.58 Mt in 2010 to a peak at 61.49 Mt in 2011, which was then followed by a yearly decline to 47.29 Mt in 2015. Accordingly, the total ffCO₂ emissions in Shanghai rose from 238.7 Mt in 2010 to 242.4 Mt in 2011, and then declined to 225.0 Mt in 2015. Due to the shutdown of several coal-fired power plants, CO₂ emissions in the corresponding grids have been reduced significantly. However, the high spatial clustering characteristic of CO₂ emissions in Shanghai is still robust.

As shown in

Table 2. Accumulated CO₂ emissions in different groups of grids and their shares from 2010 to 2015.

Year	Units	Top 10 1	Top 20	Top 50	Top 72 (1%) 2	Top 100	Top 702	Other Grids 3	Total Emissions
2010	Mt-CO2	105.9	126.5	151.5	159.3	163.5	184	24.9	208.9
	%	50.69	60.56	72.52	76.26	78.27	88.08	11.92	100.0
2011	Mt-CO2	115.8	134.1	156.6	164.1	167.9	189.5	28.4	217.9
	%	53.14	61.54	71.87	75.31	77.05	86.97	13.03	100.0
2012	Mt-CO2	112.5	129.1	149.6	156.8	160.4	182.8	31.5	214.3
	%	52.50	60.24	69.81	73.17	74.85	85.30	14.70	100.0
2013	Mt-CO2	115	131.5	153.9	161.9	165.4	187.2	31.7	218.9
	%	52.54	60.07	70.31	73.96	75.56	85.52	14.48	100.0
2014	Mt-CO2	98	113.9	135.3	143.3	146.7	168.4	32.3	200.7
	%	48.83	56.75	67.41	71.40	73.09	83.91	16.09	100.0
2015	Mt-CO2	97.8	114.8	136.2	145	148.4	169.6	32.1	201.7
	%	48.49	56.92	67.53	71.89	73.57	84.09	15.91	100.0

Notes: ¹ Indicating the total CO₂ emissions from the largest 10 grids; ² Indicating the largest 72 grids that account for 1% of total grids; ³ Indicating the total CO₂ emissions from the other grids.

, the cumulative ffCO₂ emissions from the top 10 grids accounted for approximately 50% of the total emissions during the time from 2010 to 2015, while the difference of the total CO₂ emissions from the top 20 grids and other sets of grids from 2010 to 2015 is also within 5%. This result implies that there is a spatial locking effect of the high agglomeration of ffCO₂ emissions in Shanghai, due to key industrial point sources such as coal-fired power plants. This characteristic will not change due to limited reductions in specific point source emissions, and is also unlikely to change tremendously over time.

Shanghai ffCO₂ emissions from natural gas, on-road traffic, and other area sources increased from 2010 to 2015; as a result, the trends of spatial agglomeration decreased slightly.

3.3. CO₂ Emissions Concentrated along the River and Coastal Area

We used GIS to calculate the distance between the center point of each grid and the coastline (or the Yangze River shoreline). The result shows that the average distance between the grid and coastline is 16.53 kilometer (km), and the largest distance is around 61.3 km. Among the 7209 grids, about 6.87% (495 grids) and 22.29% (1607 grids) of grids were respectively located within 1 km and 5 km from the coastline, and half of the grids were located within 12.8 km. Around 17.8% of the grids were located beyond 30 km from the coastline. Figure 3 shows the frequency distribution of the distances between each grid and the coastline.

Figure 4 presents a scatterplot of CO₂ emissions in each grid according to the distance of each grid center to the coastline and the Yangtze River shoreline in 2015. The red line represents the cumulative ffCO₂ emissions from those grids within the distance to the coastline and the Yangtze River shoreline. It shows that the grids with larger CO₂ emissions are mainly located within 5 km of or 25–30 km from the river and/or coastline. The total ffCO₂ emitted from the grids that are located within 1 km of the coastline (6.87% of total grids) were 51.51 Mt, accounting for 24.23% of the total grid emissions. More than half (50.32%) of ffCO₂ emissions were emitted from grids that are located less than 5 km from the coastline (22.29% of total grids), with CO₂ emissions that amount to 106.98 Mt. It is obvious that the ffCO₂ emissions in Shanghai are concentrated in the river and coastal area. This characteristic is mainly influenced by large point sources, which are power plants, steel plants, and chemical plants. Large point sources tend to locate along river and coastal areas to fulfill their water and transportation demands, and reduce their environmental impact.

Another group of high-CO₂ emission grids, which are located within areas that are 25–30 km from the coastline, includes the Wujing Power Generation Base and its surrounding chemical parks. Near the Huangpu River, the Wujing Power Generation Base is a legacy of previous urban planning. The Huangpu River area is the focus and highlight of Shanghai’s future development plans. Therefore, in accordance with the 13^th Five Year Plan for Huangpu River Area Development, the industrial infrastructure in these areas will be upgraded from production functions to that of the modern service industry. This change will enhance the trend of CO₂ emissions being most concentrated in the coastal area.

3.4. Circular Layers Structure of CO₂ Emissions

Shanghai’s urban form is characterized by a circular layer structure formed by urban ring roads [26]. As the dominant structure of Shanghai’s urban form, circular layers have a direct impact on Shanghai’s population, transportation, business, and other economic and social spatial layouts. In this study, the Shanghai circular layers divide the city into five zones, which are Puxi inside the Inner Ring viaduct (Pxii), Pudong inside the Inner Ring viaduct (Pdii), Puxi between the Inner Ring viaduct and the Outer Ring viaduct (Pxio), Pudong between the Inner Ring viaduct and the Outer Ring road (Pdio), and the areas Outside the Outer Ring road (Aoo). The total CO₂ emissions in Shanghai in 2015 and CO₂ emissions of different zones by energy sources are shown in

Table 3. CO₂ emissions in Shanghai’s five zones by energy sources in 2015 (Unit: %). Pxii: Puxi inside the Inner Ring viaduct, Pdii: Pudong inside the Inner Ring viaduct, Pxio: Puxi between the Inner Ring viaduct and the Outer Ring viaduct, Pdio: Pudong between the Inner Ring viaduct and the Outer Ring road, Aoo: areas Outside the Outer Ring road.

Circle	Coal-Related CO2	Oil-Related CO2	Natural Gas-Related CO2	Energy-Related CO2
Pxii	0.01	5.87	5.66	2.80
Pdii	0.00	1.01	0.62	0.45
Pxio	5.09	8.96	8.67	6.91
Pdio	2.27	7.99	5.77	4.85
Aoo	92.63	76.17	79.29	84.99
Total	100	100	100	100

.

The circular layer structure has a direct impact on the spatial distribution of urban CO₂ emissions. CO₂ emissions increased substantially from the Inner Ring to the Outer Ring area in 2015. The Outer Ring area of Shanghai is the main area of CO₂ emissions, with 85% of total CO₂ emissions originating from this area. A total of 11.7% of the CO₂ emissions are from the area between the Inner Ring road and the Outer Ring road. Only 3.3% of the CO₂ is emitted from the Inner Ring area.

Moreover, the circular layer structure has different impacts on the spatial layout of the consumption of different energy types. CO₂ emission sources of high-carbon intensity energy are located away from the city center, accounting for 92.6% of the total emissions. Only 7.4% of the total CO₂ emissions are distributed in the Inner Ring and the Outer Ring areas. A very small amount of coal is consumed in the Inner Ring area. Oil and natural gas consumption-related CO₂ emissions show a marked increase from the center to the outer zones, but the area within the Inner Ring still accounts for a certain share. Compared with coal-related CO₂ emissions, emissions from oil and natural gas outside of the Outer Ring zones have declined significantly, whereas they have increased substantially in the areas within the Outer Ring.

3.5. Differences in Grid Changes in CO₂ Emissions

The changes in Shanghai ffCO₂ emissions from 2010 to 2015 in the grids are shown in Figure 5. The grids show that increases in CO₂ emissions (red-colored grids) are relatively centralized, and are mainly located within the Outer Ring areas, especially in the Puxi area, followed by Pudong Airport and Hongqiao Airport. Meanwhile, CO₂ emissions from the trunk roads outside the Outer Ring area have also increased significantly. The distribution of CO₂ emissions is more dispersed, mainly due to the reductions in industrial coal consumption, which have led to the decrease of CO₂ emissions in the corresponding grids accordingly.

4. Impact Mechanisms of CO₂ Emissions

Based on the characteristics of emission sources, we divided Shanghai’s ffCO₂ emissions into two categories, which we named the large point sources and area sources. The large point sources indicate those large-scale energy infrastructures, such as coal-fired power plants, steel mills, refineries, and airports. In 2015, the total ffCO₂ emissions estimated via the point source, the line sources, and the area sources were 201.7 Mt, and the large point sources located in 57 girds emitted 129.90 Mt of CO₂, accounting for 64.4%. The area sources include those “low-quantity and wide-area diffused CO₂ emissions sources” (e.g., emissions from natural gas boilers) and linear sources (e.g., vehicle fuel road emissions). In 2015, CO₂ emissions from area sources accounted for 35.6% of total gridded emissions. The impact mechanisms of the two CO₂ emission sources are different; we will discuss this in the following part.

4.1. Impact Mechanisms of CO₂ Emissions from Large Point Sources (LPS)

Large point sources (LPS) for ffCO₂ emissions in Shanghai are the leading factors shaping the temporal and spatial characteristics. Among them, the coal-related CO₂ emissions of LPS are mainly generated from five power generation bases in Shanghai, namely, Waigaoqiao, Shidongkou, Wujing-Minhang, Caojing, and Lingang. Coal consumption in Shanghai reached a peak of 61.49 Mt in 2011, and coal consumption by power plants reached its maximum of 36.67 Mt at the same time. Since Shanghai’s GDP growth has continued to slow down and the imported power from other provinces has increased substantially, Shanghai’s local coal-fired power generation and coal consumption have decreased rapidly. Coal consumption in 2015 was more than 25% less than it was during the peak year of 2011. According to the 13th Five-Year Plan of Shanghai for energy development, the newly increased demand for electricity in Shanghai in the near future will be met by the power imported from other provinces or will be generated from natural gas, which indicates that the impact of key sources of coal-related CO₂ emissions is locked in, and can be clearly predicted and controlled.

The oil-related CO₂ emissions of LPS are mainly from the three petrochemical parks and the two airports (see Figure 1a). In 2015, the total consumption of oil from those chemical parks reached 9.09 Mt, accounting for 81.6% of the total oil consumption in the industrial sector. Given that the Jinshan Petrochemical and Shanghai Chemical Industry Park are located on the edge of Hangzhou Bay, and the coast of Hangzhou Bay is planned to be one of the national oil refining and petrochemical bases, Gaoqiao Petrochemical will be moved to the coastal areas of Hangzhou Bay as well. Besides, the two airports will remain the current location for a long time. Thus, it is safe to conclude that the large point sources of oil-related CO₂ emissions will be located on the north shore of Hangzhou Bay, Hongqiao Airport, and Pudong Airport in the long term. The geographical information on all of LPS is shown in Figure 1a.

Besides, Shanghai launched its pilot carbon emission trading system (ETS) in November 2013. The ETS covered all the LPS by 2015. Hence, the government can take advantage of the ETS to mitigate CO₂ emissions from LPS. First, the government can obtain information on energy consumption and CO₂ emissions from these LPS through their annual CO₂ emissions report, which will help policymakers track the trend of Shanghai CO₂ emissions. Second, the government can encourage LPS to reduce CO₂ emissions by adjusting the volume of free allocation. Therefore, the ETS will enhance the downward trend of CO₂ from LPS.

Therefore, we conclude that CO₂ emissions from LPS in Shanghai has been strictly controlled by the government, and its trend is controllable and predictable. The policies aiming to control coal consumption that have been implemented since 2012 have had positive effects on the reduction of CO₂ emissions. This transition also demonstrates that the government can have a great influence on the spatial layout of urban’s CO₂ emissions by implementing targeted policies to mitigate ffCO₂ emissions from large point sources.

4.2. Impact Mechanisms of CO₂ Emissions from Area Sources

Compared with CO₂ emissions from LPS, there are more complicated factors that affect the spatial pattern of CO₂ emissions from area sources. ffCO₂ emissions from area sources have increased from 49.51 Mt to 71.84 Mt between 2010–2015, and its share in the total gridded ffCO₂ increased from 23.3% to 35.6%. This trend shows the increasing impact of ffCO₂ from area source on the total ffCO₂ emissions in Shanghai. In this study, we applied the Geo Detector method and tools to identify and analyze the drivers that affect the spatial pattern of ffCO₂ emissions from area sources.

According to the specific situation of Shanghai, this study selects five impact factors controlling CO₂ emissions from area sources, including population density, human activity intensity, land-use type, energy efficiency, and urban planning. Following the requirements of the Geo Detector model, we transformed each factor into a categorical variable. Continuous variables such as population density, different land types, and energy consumption intensity are classified into categorical variables according to the natural breaks classification method of GIS. Population activity intensity consists of six categories according to population density at the daytime and nighttime, and their ratio of daytime-to-nighttime at the district level. The population density is ranked by high, middle, and low levels, and the ratio of daytime-to-nighttime population is divided into two levels: namely, the high ratio of the daytime-to-nighttime population, and the low ratio of the daytime-to-nighttime population. The meaning and data sources of all the factors are shown in

Table 4. The meaning and data sources of each evaluated impact factor.

Factor Types	Factors	Meaning	Data Resource
Population density	X1: Population density	Persons per grid (square kilometer)	Author mapped based on Shanghai Statistical Yearbook
Human activity intensity	X2: Population activity intensity	Street (township) is divided into six categories according to population density at the daytime and nighttime and the ratio of daytime-to-nighttime population.	Author mapped based on the study of [50]
	X3: Constructed land development intensity	Constructed land area accounts for the proportion of the total area in each grid.	Author calculated
Land-use types	X4: Residential land	The total area of residential land in each grid.	Author interpreted from high-resolution remote sensing image.
	X5: Traffic land	The total area of traffic land in each grid.
	X6: Public building land	The total area of public building land in each grid.
	X7: Industrial land	The total area of industrial land in each grid.
Energy efficiency	X8: Energy consumption intensity	Energy consumption per 10000 RMB GDP output value of above-designated size enterprise.	Shanghai Statistical Yearbook on Energy
Urban planning	X9: City-level types	Including the central city, key new town, Chengguan town, key towns, and rural areas.	Shanghai Urban Plan (1999–2000)
	X10: Types of urban circle layers	Including areas within the Inner Ring road, areas between Inner Ring road and Outer Ring road, and areas outside the Outer Ring road.	Shanghai Urban Plan (1999–2000)

, and the spatial distribution of those variables is shown in Figure 6.

We used the factor detector from the Geo Detector software to analyze the determinant power of the 10 identified factors that are responsible for the spatial variability in the area sources’ CO₂ emissions in Shanghai. The spatial strata of CO₂ emissions from area sources are the dependent variable Y, and the spatial strata of identified factors are the independent variable X. The q-statistic method in the Geo Detector software is used to measure the degree to which variable X can explain the spatially stratified heterogeneity of Y. The statistical significance is tested at the level of p ≤ 0.05. The q and p values of each factor are shown in

Table 5. Driving factor detection results of CO₂ emissions from Shanghai area sources in 2010 and 2015.

Year	Value	X1	X2	X3	X4	X5	X6	X7	X8	X9	X10
2010	q statistic	0.30	0.28	0.21	0.11	0.23	0.16	0.07	0.11	0.19	0.32
	p value	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00
2015	q statistic	0.42	0.36	0.24	0.14	0.26	0.18	0.06	0.28	0.25	0.42
	p value	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00

.

The results show that the explanation of all the factors on CO₂ emissions from area resources is statistically significant. Urban circular layers (X10) and population density (X1) are the most influential factors in 2010, with q values of 0.32 and 0.30, respectively. These are followed by the population activity intensity factor (X2), with a q value of 0.28. Each of these three factors can explain around 30% of Shanghai’s area sources’ CO₂ emissions, and are the leading factors of surface-derived CO₂. Furthermore, the values of q for traffic land (X5), constructed land development intensity (X3), and city-level types (X9) ranged from 0.19–0.23, indicating that these factors also have strong determinant power for area sources’ CO₂ emissions. These results demonstrate that urban planning can influence the urban CO₂ emissions from area sources.

Compared with 2010, the q values of all the factors are higher in 2015, except for industrial land (X7), which indicates that the determinant power of this factor is significantly enhanced. Among them, the top three factors are still circular layers (X10), population density (X1), and population activity intensity (X2), and this consistency demonstrates the locking effect of these leading factors. The increase in the q value is the largest for energy consumption per unit of output value (X8), increasing from 0.11 in 2010 to 0.28 in 2015, indicating that the policies aiming to adjust the structure of the economy have had an effect on the spatial distribution of CO₂ emissions from area sources from 2010 to 2015. However, the explanatory power of industrial land is still weak. We think that this weak relationship is reasonable, as Shanghai’s industrial land planning intends to concentrate the dispersed industrial land to the industrial park, which will increase the trend of spatial clustering of industrial land. This trend is different from the dispersive character of ffCO₂ emissions from area sources.

Besides those leading factors, we find that different types of land use have limited power to determine the spatial distribution of CO₂ emissions from area sources. In 2015, the determinant power of traffic land (X5) is the highest (q = 0.26) among the four types of construction land, followed by public building land (X6, q = 0.18). The determinant power of industrial land (X7) is the lowest (q < 0.1). This conclusion derived from our study is different from those of many previous studies, which suggests the changes in land-use types have a strong relationship with urban carbon emissions [46]. We conclude the reasons that caused this difference are: First, the principle of statistical correlation is different. The Geo Detector mechanism we used focused on analyzing the spatial heterogeneity of two variables, and assumed that if the spatial distribution of the two variables tends to be consistent, there is a statistical correlation between them. In contrast, the regression analysis that the other studies used is based on the values of different variables. Spatial distribution can reveal the difference of a variable by way of spatial heterogeneity, while the value used in the regression model can only represent the total value. Therefore, even if the total value is consistent, when the internal difference is huge, the correlation results will be different. Second, the urban development is unbalanced. As the high-resolution CO₂ emissions products provide more specific spatial information about types of land use and quantitative CO₂ emissions, the urban unbalanced development characteristics can be detected. The results show that the factor of industrial land has greater power in the area of the Outer Ring (q = 0.14), while it has little determinant power (q = 0.03) in the Inner Ring and Middle Ring area.

Given that Shanghai’s ffCO₂ emissions show a significant circular layer structure, we continue to explore the q values of the factors that affect the spatial pattern of area sources’ CO₂ emissions from Shanghai’s different circular layers. The circular layers consist of three parts, namely the area within the Inner Ring (Inner Ring), the area between the Inner Ring and the Outer Ring (Middle Ring), and the area outside the Outer Ring (Outer Ring) in 2015. The results are shown in

Table 6. Determinant power of each factor to determining the spatial pattern of CO₂ emissions from area sources in the different circular layers in 2015.

Circle		X1	X2	X3	X4	X5	X6	X7	X8	X9
Inner Ring	q statistic	0.32	0.39	0.08	0.06	0.09	0.05	0.02	0.45	--
	p value	0.00	0.00	0.22	0.60	0.45	0.58	0.84	0.00	--
Middle Ring	q statistic	0.19	0.17	0.03	0.07	0.16	0.10	0.03	0.30	0.03
	p value	0.00	0.00	0.07	0.00	0.00	0.00	0.03	0.00	0.04
Outer Ring	q statistic	0.11	0.08	0.17	0.07	0.16	0.10	0.14	0.08	0.02
	p value	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00

.

The factor that is statistically significant (p ≤ 0.05) and has a larger q value (q ≥ 0.1) is the leading factor in determining the spatial pattern of CO₂ emissions within a given circular area. The results show that the leading factors of each layer are very different, especially in the areas outside the Outer Ring compared to the other two areas. The energy consumption intensity (X8), population activity intensity (X2), and population density (X1) are the leading factors for CO₂ emissions within the areas of the Inner Ring and the areas between the Inner Ring and the Outer Ring. In contrast, the constructed land development intensity (X3), traffic land (X5), and industrial land (X7) are the leading factors in CO₂ emissions for areas outside the Outer Ring.

The determinant power of the same factor may have different effects on the area sources’ CO₂ emissions in different circular layers. The q values of population density, population activity intensity, and energy consumption intensity declined from the inside to outside layer by layer, indicating that these factors have stronger determinant power on the spatial pattern of CO₂ emissions in the central city than in external areas. The increasing q values of industrial land, public building land, and traffic land from inside to outside shows that the type of land use has a stronger determinant power in the outside area than it does in the inside area.

This variation demonstrates that the determinant power of key factors will change within the urban areas, and that the leading factors may change between a local area and a regional area. We think that most of the variation can be explained by the unbalanced development of the city. The urban circular layers’ structure is the external form of the city’s unbalanced development. In the inner area, the development of urbanization is maintained at a high level. The CO₂ emissions in this area are mainly driven by factors related to the population, including population density and human activity. Besides, the factor of energy consumption intensity is also the determinant factor, indicating that improving energy efficiency is an important measure to reduce urban CO₂ emissions, even in the most developed areas of the city. From the perspective of policy, first, the government should implement policies aiming to decrease the intensity of CO₂ emissions of human activity. For instance, these policies can include increasing building energy efficiency standards and thus decreasing CO₂ emissions from the residential and commercial sectors; encouraging the development of public transportation to decrease the use of private cars; and encouraging the mix of the residential and commercial areas to decrease commuting needs. Second, the removal of industries with high energy consumption and high labor intensity in the Inner Ring area will also have a great impact on carbon reduction. In the Outer Ring area, the dominant factors are different. Factors such as urban development intensity, traffic land, and industrial land, which have strong positive relationships with urban growth, have stronger power to determine CO₂ emissions in this area. Therefore, urban planning in this area should focus on industrial structure adjustment and low-carbon manufacturing. Policies aiming to restrict the development of high energy consumption projects and increase the use of renewable energy should be encouraged. In conclusion, the concept of low carbon development should be integrated into the urban planning of the area outside the outer ring in different ways. The high resolution of carbon emissions data can be a useful tool to help policymakers locate the areas where low carbon development measures should be implemented.

5. Conclusions and Outlook

This study uses Shanghai as a case study, showing that high-resolution energy consumption and spatially gridded CO₂ emissions data are useful in the management of urban CO₂ emissions.

Firstly, a high-resolution spatially gridded urban dataset can directly display the spatial pattern of urban CO₂ emission and facilitate our understanding of the imbalance of urban development. Based on this dataset, we conclude that high spatial agglomeration, CO₂ emissions centralized along the river and coastline, and the three circular layers structure are the three notable temporal–spatial characteristics of Shanghai ffCO₂ emissions.

Secondly, a high-resolution spatially gridded urban dataset can help policymakers identify the leading impact factor of urban CO₂ emissions in a given area within the city. Thus, a more targeted policy that adapts to local conditions can be made to promote their low-carbon development. We divide sources of CO₂ emissions in Shanghai into two categories: namely, the large point sources and the area sources. The impact mechanisms of the two different categories are discussed and concluded as following.

Large point sources of CO₂ emissions have a strong locking effect on the spatial pattern of CO₂ emissions in Shanghai, and limited changes in the energy consumption of some of the grids would not change the current distribution. Based on the trend analysis of the large point sources, we can accurately predict the trends of total energy consumption and CO₂ emissions in Shanghai over the long term. Energy consumption in Shanghai entered a steady growth stage in the last five years. In the future, the total energy consumption may enter a saturation period, but the structure of energy consumption will change dramatically. Currently, the Shanghai energy system is under transformation; the implementation of many actions such as the closure of small and medium-sized coal-fired boilers, the reduction in coal-fired power generation, the relocation of steel plants, and the construction of a modern service-oriented city will directly influence the total energy consumption and CO₂ emissions. Even though it is hard to predict the changes of CO₂ emissions from each grid in the short term, the trends of CO₂ emissions for the longer term are predictable and controllable. For instance, we conclude that due to the continuous reduction of coal consumption in coal-fired power plants since 2011, the decline of CO₂ emissions is recognized as a long-term trend. The proportion of the relevant grids’ emissions will decrease year by year, but the absolute amount is still large. Due to the relocation of Shanghai’s petrochemical industry and the enhancement of the spatial agglomeration of shipping centers and trade centers, oil consumption in Shanghai will continue to increase in the future. Therefore, the decline in oil-related CO₂ emissions in 2013 is a short-term phenomenon. CO₂ emissions from the grids that cover Hongqiao and Pudong Airports will account for larger proportions of the total long-term gridded CO₂ emissions. Based on those trends, we expect Shanghai’s total CO₂ emissions to reach their peak before the total energy consumption.

However, for policies that target the reduction of energy and coal consumption of large point sources so as to reduce the total urban ffCO₂ emissions, their effects will gradually weaken. In order to promote Shanghai’s low carbon development, the local government should pay more attention to the ffCO₂ emissions from area sources. The share of ffCO₂ emissions from area sources in the total gridded ffCO₂ emission in Shanghai has increased by 12.3% between 2010–2015, and this rising trend will continue.

Compared to the clear trend of CO₂ emissions from the large point sources, the mechanisms of CO₂ emissions from area sources are more complex. In this study, we applied the Geo Detector method to detect the drivers that have determined the configuration of energy-related carbon emissions in Shanghai. The results show that the urban circular layer structure is not only an important boundary of the layout of urban and industrial functions, but is also an important factor leading the spatial pattern of urban CO₂. The CO₂ emissions of Shanghai already had a clear structure of circular layers in 2010, and the circular layers had an even stronger determinant power of the spatial distribution of CO₂ emissions in Shanghai by 2015. As the circular layers structure is a result of early urban planning, this trend demonstrates that urban planning has a determinant force on the CO₂ emissions of area sources. Thus, the government should link the idea of low carbon development with its urban development strategy more closely. Urban planning can comprehensively influence the utilization and distribution of land, resources, buildings, transportation, and other elements in the process of urbanization. Mitigating and controlling CO₂ emissions should also be included in urban planning. For instance, from the perspective of urban planning, the government can promote the construction of the public transportation system, encourage the mix of residential and commercial areas in urban planning, and encourage the use of the electric vehicle to reduce ffCO₂ emissions from the transport sector. High-resolution CO₂ emissions data can provide a useful tool to advance policymakers’ awareness of the combination of carbon mitigation and urban planning, help policymakers identify hot spots for CO₂ emissions within the urban domain, and support policymakers to make a more targeted policy that is in line with the local social–economic context.

Finally, high-resolution CO₂ emissions data can provide prior information for the inversion of atmospheric CO₂ data observed by satellites, thus laying the foundation for the development of an integrated ground–airspace carbon monitoring network. This network will provide basic scientific data to facilitate policymaking and implementation to reduce CO₂ emissions effectively, and hence improve measures aiming at climate change mitigation and adaptation. In order to complete this comparison, the following work still needs to be done to improve the accuracy and comprehensiveness of the data. Firstly, researchers need to allocate the data of CO₂ emitted from waterway transportation to related grids and add the data of the carbon sinks for each grid cell. Secondly, researchers need to collect real-time data on residents’ natural gas consumption. We hope to use this study to maximize the accuracy of urban CO₂ emission inventories and to promote the pertinence and the timeliness of the policy.