Predictions and driving factors of production-based CO2 emissions in Beijing, China

Sustainable Cities and Society 53 (2020) 101909

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Predictions and driving factors of production-based CO2 emissions in Beijing, China

T

Zhao Liua, Fang Wanga,*, Zhiyao Tangb,*, Jintong Tangb a

NSFC-DFG Sino-German Cooperation Group on Urbanization and Locality (UAL); College of Architecture and Landscape Architecture, Peking University, Beijing, 100871, PR China b College of Urban and Environmental Sciences, Peking University, Beijing 100871, PR China

ARTICLE INFO

ABSTRACT

Keywords: Urban development Production-based CO2 emissions Economic efficiency Emission control STIRPAT model Beijing

China, as the largest emitter of greenhouse gases worldwide, has severe air pollution problems. Emission reduction in metropolises considerably contributes to air quality improvement. In this study, the predictions and driving factors of production-based CO2 emissions in Beijing are analyzed based on regional spatial differentiation. The results show that production-based CO2 emissions in Beijing increased from 12.78 million tons in 1980 to 45.91 million tons in 2015, and the growth rate of CO2 emissions decreased from 2010 to 2015. Moreover, it is probable that the total CO2 emission will rapidly increase by approximately 27 million tons in the following decade. By 2025, the carbon emissions are projected to reach approximately 69.66 million tons with an increase of approximately 23.75 million tons compared with that in 2015. For every 1 % growth in the secondary industry scale, which is the main carbon emission driving factor, CO2 emissions increase by 0.51 %. Population increase and economic development lead to increasing emissions, whereas employment expansion, public transportation system improvement, reduction in household electricity consumption, and waste reduction contribute to emission reduction. This research can be used by policymakers as reference to appropriately target management policies for air pollution control and emission reduction.

1. Introduction Many countries, including China, are now in the process of fast urbanization and industrialization. The rapid expansion in construction land development has resulted in a wider geographic spread of greenhouse gas emissions (mainly carbon dioxide), which impacts global warming and climate change. These lead to more extreme climates and greater damage risks. Emission growth results in air pollution and high risk of acquiring human respiratory diseases. In view of this, since the 1980s, countries around the world have attached considerable importance to the research on emission control, especially air pollution and CO2 emissions (Gasser, Guivarch, Tachiiri, Jones, & Ciais, 2015; Haddow, Bullock, & Haddow, 2017). A key problem in every city in the world is how to build a positive interaction between economic development, reduction of CO2, reaction to climate change, improvement of air quality, and life quality of residents (Deng, Feng, & Cao, 2018; Huisingh, Zhang, Moore, Qiao, & Li, 2015). In 2018, the total quantity of CO2 emissions in China, as one of the main contributors to CO2 emissions and air pollution, grew by 2.5 % and increased to 9.5 Gt compared with that in 2017 (International Energy Agency, 2019).

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According to the evaluation in six different scenarios, China will reach a carbon emission peak of approximately 3.00 PgC∙a−1 by 2030 (Zheng, Zhu, Wang, & Fang, 2016). China is thus promoting a series of national strategies that aim to slow down the growth rate of CO2 emissions. These strategies include the supply-side structural reform (mainly focusing on industrial structure transformation), “Made in China 2025” plan (development of new manufacturing as key), interim procedures for the administration of carbon emission trading (limiting total carbon emission in each province and city), and setting the goal of reducing by 2030 the total amount of carbon emissions by 60–65% compared to that in 2005. The carbon intensity reduction targets of China by 2020 and 2030 are 40–45% and 60–65%, respectively, which can be achieved under current policies. The total CO2 emissions, however, will fail to satisfy the 450-ppm scenario (8.4 Gt by 2020 and 7.1 Gt by 2030) if these goals are to be achieved only by improving the industrial structure and energy structure (Xu, Chen, & Chen, 2017). It is therefore necessary to implement specific goals for emission reduction (air pollution and greenhouse gases) in the cities of each country. This is because cities are the core of global climate change mitigation and strategic for low-carbon development as they shelter

Corresponding authors. E-mail addresses: [email protected] (Z. Liu), [email protected] (F. Wang), [email protected] (Z. Tang), [email protected] (J. Tang).

https://doi.org/10.1016/j.scs.2019.101909 Received 18 July 2019; Received in revised form 17 October 2019; Accepted 17 October 2019 Available online 23 October 2019 2210-6707/ © 2019 Elsevier Ltd. All rights reserved.

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more than half of the world’s population, consume three quarters of the global energy, and emit CO2 (Mi et al., 2019). In China, 85% of CO2 emissions emanate from cities (Shan et al., 2017). The city governments in China have formulated a large number of indicators and considerable guidance for green cities according to various patterns of environmental assessment to resolve climate change and drive urban actions, which can reduce CO2 emissions and air pollution (Wang, Liu, Zhou, Hu, & Ou, 2017; Yang & Li, 2013). As of 2018, more than 200 eco-cities have been planned or built in China (Chinese Society for Urban Studies, 2018). In 2015, 11 cities, including Beijing, jointly established the Alliance of Peaking Pioneer Cites of China. Beijing is projected to reach its peak CO2 emissions by 2020. From 2013 to 2016, approximately 15 policies, regulations, and notices on carbon emission reduction, including carbon emission trading pilot projects, trading units, trading management measures, emission offsetting management measures, and total control of emission, have been issued by the Beijing government. In terms of the research on urban CO2 emissions, several feasible methods have been developed for constructing CO2 emission inventories for cities. These include the inventories of production-based and consumption-based CO2 emissions that result in significant differences in calculating CO2 emissions (Laine, Ottelin, Heinonen, & Junnila, 2017; Liu, Liang, & Wang, 2015). The consumption-based carbon footprints capture the trans-boundary carbon flows embodied in international trade, whereas production-based emissions illustrate direct local emissions. Methods that combine these two perspectives, e.g., the city carbon map concept, have also been developed (Wiedmann, Chen, & Barrett, 2016). From a consumption-based perspective, since the global financial crisis, the decline in consumption-based CO2 emissions in Europe is partly because of emissions from the production of imported products from China (Karstensen, Peters, & Andrew, 2018). The spatial distribution of the total amount of carbon footprints resulting from the product consumption of residents is related to urban development and socio-demographic indicators (e.g., income per capita, population density, buildings, and transportation) (Gao, Chen, & Wang, 2019; Minx et al., 2013; Ren & Cao, 2019; Yang & Meng, 2019). Ottelin et al. (2019) therefore presumes that the adoption of a consumptionbased analysis enables wealthy cities and nations to realize that they have to accept responsibility for the emissions driven by their demand but occurs outside their territorial boundaries. From a production-based perspective, the most common approach is to calculate, modify, or predict the CO2 emissions of various fuels based on fossil fuel combustion data (Liu, Guan et al., 2015) as well as to analyze the impact of energy structure and energy intensity on carbon emission (Xu, Sathaye, & Kramer, 2013). Shan et al. (2017) developed a set of methods based on the energy balance table to construct a production-based CO2 emission inventory. Various sectors contribute differently to CO2 emissions. In China the industrial and transportation sectors are the two largest contributors of CO2 emissions (Zhao, Zhang, Li, Shao, & Geng, 2017). From 2005 to 2012, CO2 emissions in the cities of China decreased because of changes in production structure and efficiency gains (Guan et al., 2018). The research on the driving mechanism and influencing factors of production-based CO2 emissions can contribute to the formulation of more elaborate sustainable development policies (Yang & Meng, 2019), including the following three stages. (a) The CO2 emissions are calculated and predicted, that is, the carbon flux introduced by urban development is measured through soil carbon, vegetation carbon, and building prediction models to estimate future emission change (Arneth et al., 2017). (b) The driving mechanism and influencing factors of CO2 emissions are analyzed (Xu, Dong & Yang, 2018). (c) Policies on CO2 emission reduction from the perspective of policy-making and spatial planning adjustment are proposed (Wang et al., 2017). In view of the low environmental and economic efficiencies of most cities in China, it is possible to reduce carbon emissions while promoting economic developments by improving energy efficiency and transforming the model of economic development (Saidi & Hammami, 2015; Yang, Cao, & Lo,

2018). Moreover, there are bidirectional causal links between CO2 emissions and economic structure, and between CO2 emissions and energy consumption structure. A unidirectional causal link that runs from CO2 emissions to GDP, urbanization, and trade is also identified (Zhou, Wang, & Feng, 2018). In China, the variation in industries among different regions has a significant effect on production-based CO2 emissions because the country has a vast territory, and its resource distribution varies among regions. Heavy industries that are resourceintensive tend to export more carbon-intensive products, whereas cities with more developed industrial structures tend to import these products to reduce their carbon emissions (Azizalrahman & Hasyimi, 2019). In metropolises, production-based CO2 emissions are mainly caused by energy consumption. Statistical data on urban energy consumption are also available; accordingly, studies on production-based methods are conducted as reference for other research on CO2 emissions in other cities. Based on the foregoing discussion, this study selects Beijing, which is a typical metropolis in China, as the study area. Three research questions are put forward, as follows: (a) What are the spatial differences in CO2 emissions among various municipal districts in Beijing, and how can targeted strategies be provided to control these emissions? (b) How can the tendency of the total CO2 emissions in Beijing be predicted based on previous statistical data? (c) What are the driving factors influencing CO2 emissions, and what policies can be implemented to control these emissions in Beijing? Previous studies on CO2 emissions in the cities of China have not considered the variations in the management of municipal districts of these cities. The evaluation of the impact of emission reduction and urban-related policies on the change in CO2 emissions has also not been considered. In addition, the effect of the policy regarding non-capital function decentralization on CO2 emission reduction and air pollution control has recently gained considerable attention. This study therefore analyzes the change in carbon emissions and spatio–temporal pattern variance among municipal districts with high-speed urbanization in Beijing in the last 40 years. In terms of the synergic relationship between society, economy, and ecology, the carbon emission efficiencies in different regions are evaluated, and the amounts of carbon emissions in these regions are predicted. The driving mechanism and influencing factors of carbon emission change are thereafter analyzed. The results can be employed as references for the formulation of policy guidance for regions in Beijing because of variations in carbon emissions from the perspective of social and urban sustainability. The results afford references for the governance of support space in the construction of a low-carbon city as well as spatial guidance for creating a low-carbon industry chain. Fig. 1 shows the analytical framework of this research. 2. Data and methods 2.1. Study area Cities present high carbon emissions due to the concentration of social and economic activities. Beijing, the capital of China, is an international metropolis with worldwide influence. It is the political and cultural center of China, and its city scale has expanded for approximately 40 years, with a typical urban development that represents the epitome of rapid urban development in China. In 2017, China has the third largest urban population and the second highest gross domestic product (GDP) in China. Its rapid development has led to high energy consumption and a sharp increase in CO2 emissions from fossil energy and electric power resources, increasing the air pollution and aggravating the haze issues. For example, in 2013, the annual average concentration of PM2.5 reached 89.5 μg/m3, approximately 1.5 times higher than the national standard (35 μg/m3). To reinforce environmental management and response, the “Blue Sky Protection Campaign” action plan from the government of China established many policies that were successively promulgated in Beijing, including: Ambient Air Quality Standard (AAQS), Clean Air Action Plan in Beijing from 2013 to 2

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Fig. 1. Analytical framework and its efficiency for examining production-based CO2 emissions in Beijing.

Fig. 2. Spatial distribution of administrative districts in Beijing.

2017, Action Plan for Energy Conservation, and Low Carbon and Circular Economy in Beijing for The 13th Five-Year Plan from 2016 to 2020. Through organic restructuring, transformation and relocation of factories, traffic restriction, pollution emission control in construction sites and pollution joint-prevention in Beijing, Tianjin, and Hebei Province. Beijing is gradually moving towards a low-carbon status to actively respond to climate change and promote urban energy conservation and emission reduction. By the end of 2018, the total land area of Beijing with 16 municipal districts was approximately 16 410 km², and the population reached approximately 21.54 million. The central urban areas include the Dongcheng (DC), Xicheng (XC), Chaoyang (CY), Fengtai (FT), Shijingshan (SJS), and Haidian (HD) districts. Although these areas only account for approximately 8% of the total area, the population accounts for approximately 57% of the total, and the GDP for approximately 70%. The concentration of CO2 emissions in Beijing is therefore concentrated in the central urban areas (Fig. 2).

electricity, etc.). Based on the ratio of the actual calorific value of various energy sources to the calorific value of standard coal fuel (conversion coefficient of energy resources), the amount of various energy sources can be converted into standard coal, and the total annual comprehensive energy consumption can be calculated. The formula to calculate the total production-based CO2 emission is as follows:

2.2. Methods

2.2.2. Gini coefficient The Gini coefficient is an important analysis index that can be used to comprehensively measure the difference in the income distribution of residents. This index has been widely used in the equity evaluation system of resources and energy consumption, providing a reference to

Et =

Em ×

(1)

i 4

where Et is the total net CO2 emission (10 t) in the year t, Em is the CO2 emission of construction land (104 tons of standard coal), i is the carbon emission coefficient of standard coal (t·t−1). The CO2 emissions from construction lands mainly derive from the combustion of various fuels and electric power supply. Therefore, according to the Guidelines for National Greenhouse Gas Inventories by the Intergovernmental Panel on Climate Change (IPCC), the carbon emission coefficient of standard coal is 0.7559 (kg/kg) (Fang, Guo, Piao, & Chen, 2007; Lai et al., 2016).

2.2.1. Spatio-temporal pattern variation of carbon emissions Production-based CO2 emissions caused by economic and social activities mainly involve energy consumption (coal, oil, natural gas, 3

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measure the sustainable development of resources, environment, and economy. To measure the difference and equilibrium degree of CO2 emissions in the region, the Gini coefficient is used in this study, and the formula is as follows:

G=

n i=1

n |x j= 1 i 2 2n x¯

2.2.5. Stochastic impacts by regression on population affluence and technology (STIRPAT) model As CO2 emissions are often affected by social, economic, ecological, and other factors, the STIRPAT model can reasonably explain the relationship between environmental impacts and population, economy, and technology. This model has a strong flexibility and can select many influencing factors to improve its applicability (Li, Mu, Zhang, & Gui, 2012). The STIRPAT model is based on the classical IPAT model (I= aPi AiTi e ) constructed by York, Rosa, and Dietz (2003). In this model, the environmental pressure caused by human activities is impacted by population (P), affluence (A), and technology (T). The model can establish the relationship between a variety of cultural factors and the environment; this is beneficial for analyzing the dynamic mechanism of carbon emission intensity. The model therefore takes the logarithm on both sides of the equation to change itself into an additive model. The formula is as follows:

xj | (2)

where xi and xj are the CO2 emissions of municipal districts i and j, respectively; x¯ is the average regional CO2 emissions in Beijing; n is 16 because there are 16 municipal districts; G is the Gini coefficient (0 ≤ G ≤ 1), and the higher value of G indicates the greater degree of deviation among the regions. According to international conventions, G 0.2 indicates the absolute average level, 0.2 < G 0.3 means a relatively average level, 0.3 < G 0.4 means a relatively reasonable level, 0.4 < G 0.5 means a large gap, G> 0.5 means great disparity, 0.4 is selected as the warning line of the distribution gap.

lnIi = a + blnPi + clnAi + dlnTi + e

where P represents the population; A represents the degree of prosperity, i.e., situation of economic development; T is technology, namely the energy efficiency or CO2 emission intensity; b, c, and d represent the elasticity coefficient of population, affluence, and technology, respectively; e is the model error; and I is the net CO2 emission. To further explore the influencing mechanism of urban population, economy, transportation, and the lives of residents on production-based CO2 emissions, referring to Li et al. (2012), and to Wang, Zhou, and Zhou (2012), 12 variables are introduced into the STIRPAT model. These variables are permanent resident population (PRP, 104 people), GDP (102 million yuan), urban road mileage (RM, km), passenger capacity of public transportation (PCPT, 104 people), industrial structure (IS, %), household consumption level (HCL, yuan), total electricity consumption (TEC, 104 kWh), urban and rural household electricity consumption (HEC, 104 kWh), per capita disposable income (PCDI, yuan), Engel coefficient (EC, %), population of employees in the end of the year (PE, 104 people), and the proportion of employees for the three main industry categories: primary industry, secondary industry, and tertiary industry (EI, %).

2.2.3. Carbon emission economic efficiency Economic efficiency index (EEI) can be used to measure the fairness between economic contribution and CO2 emissions of the regions. The EEI is an indicator that can reflect the regional productivity of a highcarbon industry. The formula is as follows:

EEIi =

GDPi GDP Ei E

(3)

where GDPi and GDP are the GDP of municipal district i and Beijing, respectively; Ei and E are the total CO2 emissions of municipal district i and Beijing, respectively. If EEIi > 1, then the economic contribution rate of municipal district i is greater than the increase in CO2 emissions, implying that the economic efficiency of the high-carbon industry is relatively high. In contrast, EEIi < 1 indicates that the high-carbon industry should be controlled or modified. 2.2.4. Gray forecasting model (GM) The research objects of the gray system prediction theory are small samples, with partial information and uncertain system. The Gray Forecasting Model (GM) aims to identify the degree of difference between the development trends of systemic factors, that is, only a small amount of research data can be used for correlation analysis to make a prediction. The GM (1,1) model is the most commonly used, and is suitable for long-term prediction with high accuracy. When , x 0 , as the original data, the original data is added together for one generation to get a new sequence. Thus, x1 = {x 0 (1), x 0 (1) + x 0 (2), …, x 0 (1) + x 0 (2) + x 0 (3)+…+x 0 (n)} , and the new sequence can be replaced by the differential equation d x1 + ax1 = b . According to the gray system theory, A= (a, b)T , and the dt value of a and b can be calculated by the least square method. Moreover, xˆ 0 (t+ 1) = xˆ1 (t+ 1) xˆ1 t , and t= 1,2, …, n 1. The variance ratio and small error probability of the original data sequence are calculated to test the fitting precision of the model. The formula is as follows:

C=

P= P{| where

2.3. Data Beijing and its 16 municipal districts were selected as the research object. Carbon emissions from 1980 to 2017 were examined. These data were obtained from the Beijing Statistical Yearbook as 10 000 tons of standard coal equivalent by the total energy consumed. The carbon emission coefficient of standard coal was therefore used for calculation. Only data from 2005 to 2017 were selected to calculate the carbon emission efficiency of each municipal district in Beijing because of limited regional statistical data. A series of statistical data since 1980 from the Beijing Statistical Yearbook was selected to analyze the influencing factors of carbon emissions. The data were selected in the light of integrity and diversity for time series prediction. 2.4. Research process Based on the obtained statistical data, the production-based CO2 emissions from energy use in Beijing from 1980 to 2015 are listed in Table 1. With the rapid expansion of urban land use (Appendix A), these emissions sustained a growth trend from 1980 to 2015, with an overall growth rate of 259.21%. From 2000 to 2010, the CO2 emissions presented a rapid growth, increasing from 27.76 to 42.61 million tons. From 2010 to 2015, the carbon emissions had a gradual rise trend of up to 3.3 million tons, which was 7.75% more than the carbon emissions in 2010. The sharp drop in the increase rate was mainly because of the decline in the total energy consumption. To compare the production-based CO2 emissions of different municipal districts in Beijing, the natural breakpoint method is adopted.

¯0 ]2 [ 0 (t ) n 1 [x 0 (t ) x¯0 ]2 n 1

0 (t )

(4a)

¯0 | < 0. 6745S1}

[ 0 (t ) n 1

¯0 ]2

(5)

(4b)

is the standard deviation of the absolute error se-

[x 0 (t ) x¯0 ]2 n 1

quence, is the standard deviation of the original sequence, C is the variance ratio, and P is the small error probability. When C<0.35 and P>0.9 , the model has the highest precision. 4

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Table 1 CO2 emissions in Beijing from 1980 to 2015 (Unit: 106 t). Year

1980

1985

1990

1995

2000

2005

2010

2015

1980–2015

CO2 emissions from energy use Value of change every five years Rate of change every five years (%)

12.78 – –

14.82 2.04 15.9

18.16 3.34 22.53

23.67 5.51 30.40

27.76 4.09 17.28

33.83 6.07 21.86

42.61 8.77 25.94

45.91 3.30 7.75

– 33.13 259.21

The CO2 emissions are divided into five levels. These emissions, as well as the ranks of 16 municipal districts, are shown in Appendix B (Fig. B1 and B2). The Yanqing (YQ) and Mentougou (MTG) districts have low carbon emissions. In Huairou (HR), Pinggu (PG), and Miyun (MY), the CO2 emission control is more advanced compared with the other districts in Beijing. The CY, XC, HD, Fangshan (FS), Shunyi (SY), and FT districts continued to have high carbon emissions during the 10-year analysis. In 2015, the CO2 emissions of six municipal districts accounted for 70.4% of the total emissions in Beijing (Table B1, Appendix B). Shunyi (SY) is an industrially developed area, where the recent distribution of a large number of industrial lands and unlimited sprawl of construction lands has resulted in a significant increase in carbon emissions, i.e., 403.05% in 10 years. In 2017, however, the total industrial output value of SY reached 300 billion yuan, and it ranked first among the municipal districts of Beijing, indicating a drastic growth in CO2 emissions. Recently, Tongzhou (TZ) has been established as a subcenter of Beijing. In this area, the relocation of numerous industries and administrative institutions accelerated the growth rate of its CO2 emissions, i.e., 71.11% in 10 years. The Chaoyang (CY) district is an important industrial base, which includes old cities with densely populated areas; CO2 emissions thus remain high. To control the regional population and relocation of non-capital functions introduced by the government, the logistics and storage functions related to wholesale are relocated in CY. The improvement of the regional environment and the rapid development of high and new technology industries have led to a significant reduction in the number of permanent residents, thus decreasing CO2 emissions by 22.06%. In addition, the adjustment mode of the industrial structure includes a decrease in the proportion of the secondary industry and an increase in the tertiary industry as pillar industries. This resulted in a sharp decrease in CO2 emissions (i.e., 83.75%) from 2005 to 2015 in SJS. The Gini coefficient is calculated to analyze the differences and relatively unbalanced characteristics of CO2 emissions in different areas of Beijing. The Gini coefficients in 2005, 2010, and 2015 are 0.470, 0.421, and 0.436, respectively. These values indicate that within the 10-year analysis, significant differences in the CO2 emissions of different municipal districts of Beijing are observed, exhibiting a yearly decreasing trend. The number of municipal districts with similar amounts of carbon emissions in different regions of Beijing is gradually increasing. In regions adjacent to Beijing, the energy utilization efficiency is also similar. As a result of the optimization and adjustment of industrial structures under the different policies of various regions, however, a spatial differentiation occurs to a certain extent. Using the natural breakpoint method, the CO2 emission economic efficiency index (EEI) of different municipal districts of Beijing from 2005 to 2017 are calculated. The values are classified into five levels to illustrate the spatial distribution (Fig. C1, Appendix C). Based on the foregoing analysis and using the GM (1, 1) model, only the effects of production-based CO2 emissions are considered as the original data sequence in Beijing (from 1980 to 2017). To make a forecast and perform an accuracy test for the next decade that are not affected by policies and measures on CO2 emissions, only these data are therefore utilized. Moreover, using the SPSS statistical software and considering the CO2 emissions from 1980 to 2017, the relationship between carbon emissions and influencing factors is analyzed. It is found that the improvement of urbanization quality mainly requires employment and wealth creation; urbanization is therefore measured by the PRP and PE.

The industry sector is the main generator of carbon emissions resulting from energy consumption; therefore, the GDP proportion of secondary industry (SIS) is used to measure the level of industrialization. The larger the population carried by the real economy, the greater the impact on resources and environment; the urban economy is thus measured by TEC and GDP. The adjustment of urban industrial scale and structure is an important approach to control carbon emissions; the IS and EI are thus used to measure the energy service conditions. Low energy consumption and low emissions are alternatives to realize a lowcarbon economy in the field of transportation; RM and PCRT are thus used to measure urban traffic. The change in the lifestyle of residents can also partly lead to the increase in CO2 emissions; the residents’ living standards and behavioral characteristics are thus measured by the HCL, HEC, PCDI, and EC. 3. Results 3.1. Economic efficiency of production-based CO2 emissions As for the temporal and spatial variations in the past 12 years, the EEI presents a ring-shaped development. The EEI in urban central areas always have the highest values, but the regional gap is gradually narrowing; the EEI in the suburban area remains stable. The XC, DC, and HD districts lead in maintaining high economic development. After dropping, the basic EEI stability in the MTG, TZ, HR, CP, MY, DX, PG, YQ, and FS districts mostly exhibit an upward trend. The CY and SJS districts exhibit the most significant changes in EEIs, i.e., from low to high carbon efficiency. In recent years, the commodity trading market and general manufacturing industry in the CY have been disorganized. High-precision and culturally innovative industrial projects are therefore introduced to develop the headquarters economy and achieve a good emission reduction effect. The EEI in the SJS significantly increased from 2010 to 2015. This is attributed to the rationalization and improvement of regional economic structure adjustment, the industrial transformation that simultaneously promotes economic development and ecological environmental protection, the decrease in energy consumption, and the rapid economic growth in the region. Similar to that of HD, the EEI of FT, which is in the middle level, remains stable. The relatively high economic efficiency of CO2 emissions in central urban areas is caused by the agglomeration of industries and population in these areas as well as the rapid development of high and new technology industries and tertiary industry. The economic growth rate is higher than carbon emissions. The aim of the Beijing Urban Master Plan (2004–2020) is to strengthen the economic functions of Beijing as a capital based on the advantages of talent, information, and science and technology. Accordingly, local governments have prioritized modern service industry, modern manufacturing industry, and knowledge-intensive industry based on modern and new high technology to continuously expand the scale of tertiary industry. The List of Prohibitions and Restrictions on Industries in Beijing have been successively revised in 2015 and 2018 to improve the quality of industry and energy conservation level. Since 2005, Beijing has invested special funds on several instances to support the development of hightech industrial agglomeration parks with the electronic information enterprise as the main part and independent innovation as the driving force. 5

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It should also be noted that the EEI in the central urban areas has significantly increased since 2013. This is because Beijing exerts considerable efforts to build multiple urban centers and reduce the proportion of functions (e.g., enterprise migration, industrial distribution, spatial restructuring, and type matching) that are not related to its status as a national capital. A significant number of relatively lowerend, low value-added, low-efficiency, and low-radiation economic sectors (mainly secondary industry) are transferred from Beijing to the surrounding areas (Hebei province and Tianjin City; Fig. 1) and satellite cities. These sectors include the general manufacturing industry, regional logistics base, regional wholesale market, and a part of the educational and health care institutions. In 2015, the number of restricted industries in the central urban areas increased from 42% to 79%, especially in manufacturing, construction, and wholesale industries in DC and XC, which are the core areas in Beijing (Zhu, Ye, & Zhang, 2018). In 2017, 1200 industrial enterprises with huge population, large site area, high energy and water consumptions, and high pollution levels were moved away from Beijing, which resulted in the drastic decrease in production-based CO2 emissions in the central urban areas. The growth of the service industry promotes the improvement of CO2 emission economic efficiency. With rapid industrial development, the EEI in SY exhibited a sharp drop because the rapid population growth led to the sudden increase in CO2 emissions over five years. Reducing the emissions of industries with high carbon discharge would be effective in managing these areas in the future.

of the primary, secondary, and tertiary industries, respectively; PEI, SEI, and TEI are the proportion of employees for the primary, secondary, and tertiary industries, respectively; R² is 0.999, and F is 2200.428. The significance test is positive (0.01), and the DW test value is 2.164, indicating that the variables have a slight autocorrelation, and the model representation presents a good result. According to the regression model, the predicted values are compared with the actual values (Table D1, Appendix D). The average error of results is 0.61%, and the maximum error is 3.66%, indicating that the model could well explain the factors related to CO2 emissions. According to the symbols of correlation coefficient in the STIRPAT model, PIS, PCPT, TEC, EC, PEI, SEI, and TEI have a negative impact on CO2 emissions. The importance of these influencing factors can be ranked in line with parameter flexibility coefficient in descending order: SIS> TIS> PRP> SEI> PEI> TEI> GDP> PCPT> HEC> RM> PCDI .

> TEC> HCL> EC>PIS The rationality of the industrial structure is determined by the proportion of the three industry categories in Beijing. The secondary industry may become the main source of carbon emissions. Every 1% growth of SIS increases the carbon emissions by 0.51%, and every 1% growth of SEI increases the carbon emissions by 0.21%. Too large secondary industry scale and small employment scale are not conducive to carbon control. In addition, the development of the tertiary industry is supposed to be an effective way to reduce carbon emissions. However, in Beijing, a large number of tertiary industries still represent significant carbon pollution sources. In the future, with increased energy efficiency and industrial technology, a higher level of service industry should inhibit CO2 emissions. In addition, the expansion of employment scale in various industries should reduce CO2 emissions. Therefore, the improvement of production efficiency is useful, and it can be attributed to the enhancement of the scale effect, agglomeration effect, and technology effect. As for the integrated urbanization levels, the population growth directly leads to shrinking of living space of residents. The migration of rural population to cities and the expansion of construction areas require complete infrastructure, developed transportation, and resource investment. The increase in energy consumption will be aggravated by the constantly increasing population. Moreover, the government of China has promoted the two-child policy, which may expand the natural growth rate of urban population and change the family structure in the future. As a result, carbon emissions from population change are likely to continue to rise. In addition, the rising economy will also lead to increased CO2 emissions, and for every 1% increase in GDP, carbon emissions will rise by 0.116%. The construction of urban transportation infrastructure will further drive the urban sprawl to the suburbs, also leading to increased carbon emissions. Moreover, the widespread popularity of public transportation is conducive to reduce the emissions of waste and environmental hazards. From the perspective of residents, decreasing EC, and increasing PCDI, HCL, and HEC, will lead to increased carbon emissions in Beijing. The transition of consumption scale and consuming pattern promotes economic development. The rapid growth of the tertiary industry leads to an increasing demand for energy production and supply of power. However, coal is used to produce electric power in most thermal power plants currently in Beijing, which results in a large amount of CO2 emissions. There will be no new large coal power plants by 2020, according to the Beijing Municipal Commission of City Administration and Environment. Local gas turbine power plants will reduce the generation of electricity year by year, and the proportion of electric power supply from outside the city or areas with abundant power generation will gradually increase, replacing coal for electricity transmission. Therefore, the carbon and pollutant emissions should be reduced, supporting the low-carbon development in the future.

3.2. Prediction of production-based CO2 emissions The CO2 emission forecast results are shown in Fig. 3. The formula of the GM model is x( t+ 1) = 37309exp(0.0366t) 36031 ± 27 , with a mean variance ratio (C) of 0.1237 (< 0.35) and a small probability error (P) of 1.0000 (> 0.90). This indicates that the GM model passes the posterior variance test with good prediction accuracy. Considering the average relative error of the prediction value, the prediction accuracy is approximately 98.12%. This model therefore has a certain reference value, which can be used to predict CO2 emissions in Beijing. According to the prediction results, the yearly carbon emissions from 2018 to 2027 are 53.92, 55.93, 58.01, 60.17, 62.42, 64.75, 67.16, 69.66, 72.26, and 74.96 million tons, respectively. Except for 2004 to 2005, which presented a slight decline, the predicted CO2 emissions increased by approximately 21 million tons (approximately 39%) from 2018 to 2027. This indicates that the necessity of reducing emissions in Beijing is urgent. Accordingly, stronger environmental protection policies, including the optimization of industrial structures, increased investments for environmental protection, and introduction of new energy sources, are necessary. 3.3. Driving factors of production-based CO2 emissions It can be observed from the list in Table 2 that the EC exhibits a low correlation value, whereas the other factors present high correlation values. The confidence level is higher than 99%, which indicates that the growth of carbon emissions is related to these factors. The primary industries of IS and PEI have negative correlations, but the remaining factors exhibit a positive correlation. Based on the STIRPAT theory, the influencing factors (as explanatory variables) and carbon emissions (as explained variable) are combined to build a regression model. The regression equation is as follows:

lnC=

0.001 lnPIS+ 0.501 lnSIS+ 0.469 lnTIS+ 0.349 lnPRP + 0.116 lnGDP+ 0.033 lnHCL+ 0.074 lnRM 0.047 lnTEC+ 0.110 lnHEC 0.182 lnPEI

0.216 lnSEI

0.067 lnPCDI

0.111 lnPCPT 0.021 lnEC

0.143 lnTEI+ 0.907

(6)

where C is the CO2 emission; PIS, SIS, and TIS are the GDP proportions 6

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Fig. 3. Carbon emissions in Beijing from 1980 to 2017 and forecast from 2018 to 2027 (Unit: 104 tons). Note: years marked in red represent predicted values (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article).

4. Discussion

pollution emissions in new and emerging industrial parks, and technology innovation. Additionally, controlling the size of urban population, improving the quality of urbanization, and guiding residents to a sustainable lifestyle would be of great significance for Beijing to effectively coordinate economic growth, ecological construction, and CO2 emission reduction. In terms of population size, Beijing should promote the stability of population urbanization and pay attention to the optimization of

In general, to achieve the goal of decreasing production-based CO2 emissions with years, policy makers should support: healthy and sound economic development, more strict control of the energy intensity of industrial sectors, gradual reduction of the proportion of urban industrial enterprises, the introduction of more enterprises with low energy consumption and pollution, production technology with low Table 2 Correlation between carbon emissions and various influencing factors in Beijing. Influencing factors

Correlation coefficient Significance level Influencing factors

Correlation coefficient Significance level

Industrial structure

PRP

PIS

SIS

TIS

−0.888** 0.000

0.956** 0.000

0.960** 0.000

Electricity consumption TEC

HEC

0.842** 0.000

0.802** 0.000

0.987** 0.000

PCDI

0.772** 0.000

HCL

0.921** 0.000

0.948** 0.000

Transportation RM

PCPT

0.939** 0.000

0.956** 0.000

Food consumption

Employment figure

EC

PEI

SEI

TEI

0.389* 0.016

−0.906** 0.000

0.681** 0.000

0.975** 0.000

** Significant difference at 0.01 level (two-sided); *Significant difference at 0.05 level (two-sided). 7

GDP

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population structure and quality. It is particularly important to consider the development of urbanization quality instead of land development. The expansion of the employment scale in high and new technology industry will be conducive to the control of carbon emissions. The development of emerging low-carbon industries and the promotion of industrial clusters as an upgrade of traditional industries can help decrease the number of energy-intensive industries. The optimization of public traffic network and construction of infrastructure will help residents choose public transportation. Moreover, to reduce the carbon intensity of the entire process, actions should include energy structure and technological progress to reduce emissions. In addition, they should provide guidance for residents and economic supply-side structural reform, which should be more specifically brought into the legal system to raise the public’s low-carbon awareness, promote green consumption, and keep the residents’ sustainable consumption mode. Moreover, air pollution control in Chinese cities is also extremely serious. These problems from carbon emissions and air pollution have the same situation to some extent, including fossil energy combustion, industrial process and waste disposal, so they are always put together to control or reduce emissions. Nowadays, the marginal cost for air quality improvement through end-of-pipe control is getting higher, and there is less room for improvement. Strengthening emission source treatment, adjusting the energy structure, industrial structure, traffic structure and land use spatial structure, through synergistic emission reduction, these structural adjustment measures have a significant effect on CO2 emissions. In terms of urban management, the two measures as a whole should be considered to identify the policies with great potential for cooperation and low cost, including demand management measures, structural adjustment measures and energy saving technology measures. In addition, these policies are conducive to construct a perfect technical system for air pollution control and carbon emission reduction, including increasing the use of renewable energy, improving the quality of renewable energy, promoting new-energy vehicles, developing public transport and rapid transit, and establishing and expanding carbon trading market to make more and more enterprises with the awareness of carbon emission cost. The significance of this study is that effective guidance measures can be established for different municipal districts, and effective prevention strategies and sustainability control indicators for CO2 reductions can be formulated for execution under the Carbon Emission and Air Pollution Reduction Scheme. The main factors of CO2 emission increase were analyzed, which can provide management guidance and policy basis for the target of urban emissions control to reach CO2 emission peak in 2030 in China, and for the air quality improvement to reduce the risk of respiratory disease. However, the limitations of this study include: (a) Due to the period of statistical data, most data are panel data from 1980 to 2017, and the energy consumption data of various municipal districts before 2005 was not acquired. Therefore, the carbon emission was not assessed for all stages according to the social development stage in China. Future research should be based on long-term large database. (b) In the prediction of carbon emissions, the impact of future urban policies was not considered, and the model has certain limitations. (c) Technical efficiency, energy structure, and policy intervention were not considered influencing factors. More relevant factors should be added in future studies to measure industry development. (d) The analysis of used indicator for production-based CO2 emissions has limitation, in the urban context, the selection of the carbon emission indicator mainly involves in the industrial actors, but for cities and individual consumers the consumption-based carbon footprint is much more relevant. Therefore, it is also important to pay attention to the impact of carbon footprint on metropolis. (e) Considering the driving factors of carbon emissions, the STIRPAT model (log-linear model) was constructed, and the nonlinear method could be used to identify the imitative effect more effectively, so as to determine the robustness of causality.

5. Conclusions The analysis of production-based CO2 emissions in Beijing is based on changes, forecasts, and influencing factors. The total productionbased CO2 emission in Beijing still maintains a rapid growth trend from 12.78 million tons in 1980 to 45.91 million tons in 2015, with an overall growth rate of 259.21%. In view of population regulation and organic decentralization, however, the increase rate of CO2 emission significantly dropped from 2010 to 2015 with 3.3 million tons, which is only 7.75% more than the carbon emissions in 2010. The urban central areas with population agglomeration, including DC, XC, CY, FT, SJS, and HD, are the main urban sources of carbon that account for 70.4% of the total emissions in Beijing in 2015. In contrast, MTG, PG, HR, MY, YQ, CP, and FS are the ecological conservation areas in the master plan. They have an important ecological value because their land areas account for 68% of Beijing. The policies that combine social development and ecological environment should also be improved. They should continue to maintain a dominant ecological position, promote the utilization of solar, wind, and other renewable energy sources, strictly manage land use in the red line of ecology, limit urban development boundaries, formulate pairing cooperation frameworks, strengthen resource complementarity, and formulate stricter emission reduction policies. The CO2 emission economic efficiency among the municipal districts in Beijing exhibits a ring-shaped development. The urban central areas have the highest efficiency value with a gradually narrowing regional gap, but the economic efficiency in the suburban area remains stable. The leading districts that maintain a high economic development are XC, DC, and HD. The EEIs in CY and SJS exhibit significant changes, i.e., from low to high carbon efficiency. The EEI in the urban central areas has significantly increased since 2013. This is because Beijing has exerted considerable efforts to build multiple urban centers to reduce the proportion of functions that are not related to its status as a national capital, including enterprise migration, industrial distribution, spatial restructuring, and type matching. The MTG, TZ, HR, CP, MY, DX, PG, YQ, and FS districts have a low economic efficiency, which exhibit a slight upward trend after dropping. These districts should focus on transitioning from an extensive to an intensive economy. General manufactures, and small and disorganized businesses should undergo such an industry transition or be reorganized to effectively improve the guide, regulation, and supervision of all types of responsive regulation policies to boost regional weakness. Moreover, regional industrial transformation should be accelerated to achieve emission reduction goals. These could be attained by supporting and guiding the implementation of green industry projects, improvement of public service capacity, regulation of carbon tax, and the establishment of emission trading and other market-based methods. In view of future trends, production-based CO2 emissions will exhibit a slight increase of approximately 27 million tons in the next decade. Carbon emissions in 2025 will reach approximately 69.66 million tons with an increase of approximately 23.75 million tons compared with 2015. Except for 2004 to 2005, during which CO2 emissions slightly declined, the CO2 emissions from 2018 to 2027 is projected to increase by approximately 21 million tons (approximately 39%), indicating that it is urgent to reduce emissions in Beijing. The results show that the top five factors that affect carbon emissions are industrial structure, population, employment, urban economic level, and public transport development. As the main driving factor, every 1% growth of the secondary industry scale increases the CO2 emissions by 0.51%. The conclusions indicate that the development of cities in China, including Beijing, demand an economic restructuring agenda to deindustrialize or control the growth rate of secondary industry. In the process of adjusting factories and enterprises, the government should increase investments for research and development, eliminate technical and competition barriers, provide high-quality green environments that can attract high-tech industries and talents, and improve the quality of 8

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urbanization and real economy. In addition, with the aim of reducing synergistic emissions, demand management policies, structural adjustment, and energy saving technology can be used to control air pollution and CO2 growth through structure adjustment and transportation quality improvement.

to the research, authorship, and/or publication of this article. Acknowledgment This research was supported by the National Natural Science Foundation of China (Grant No. 51778005).

Declaration of Competing Interest The authors declared no potential conflicts of interest with respect Appendix A. Land use change in Beijing from 1980 to 2015 Fig. A1

Fig. A1. Land use expansion in Beijing from 1980 to 2015 with rapid urban development. (Source: Land use data with 30-m spatial resolution sourced from Landsat TM/ETM remote sensing data).

Appendix B. Spatio–temporal pattern variation of CO2 emissions in 16 municipal districts of Beijing

Fig. B1. Rank of production-based CO2 emissions in16 municipal districts of Beijing in 2005, 2010, and 2015 (Unit: 104 tons). 9

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Fig. B2. Spatial difference of production-based CO2 emissions of 16 municipal districts in Beijing in 2005, 2010, and 2015 (Unit: 104 tons).

Table B1 Production-based CO2 emission and its proportion in 2005, 2010, and 2015 (Unit: 104 tons).

Note: Municipal districts that are central urban areas are marked in red.

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Appendix C. CO2 emission economic efficiency change in Beijing from 2005 to 2017

Fig. C1. Change in CO2 emission economic efficiency index (EEI) in different municipal districts of Beijing from 2005 to 2017. Note: municipal districts that are central urban areas are marked in red.

Appendix D. STIRPAT model of CO2 emission driving factors

Table D1 Predicted and actual values of CO2 emissions in Beijing from 1980 to 2017 (Unit: 104 t). Year

Predicted value

Actual value

Proportional error

Year

Predicted value

Actual value

Proportional error

1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998

1275.550 1277.811 1305.258 1353.829 1420.075 1496.728 1601.888 1668.893 1741.191 1804.285 1843.253 1937.267 2024.897 2189.433 2239.767 2399.741 2506.845 2535.664 2565.641

1278.159 1274.742 1286.668 1329.749 1436.547 1481.638 1608.000 1658.786 1750.442 1777.644 1815.499 1924.240 2001.625 2187.282 2268.553 2367.311 2502.115 2491.864 2551.427

0.20% −0.24% −1.44% −1.81% 1.15% −1.02% 0.38% −0.61% 0.53% −1.50% −1.53% −0.68% −1.16% −0.10% 1.27% −1.37% −0.19% −1.76% −0.56%

1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017

2621.024 2829.587 2854.696 2993.494 3116.768 3448.518 3507.312 3684.308 3773.003 3890.076 4025.245 4255.087 4295.044 4438.009 4496.846 4627.934 4706.873 4675.657 4807.556

2617.422 2776.480 2833.564 2972.187 3114.294 3443.532 3383.366 3617.531 3850.959 3876.754 4025.762 4260.865 4286.191 4397.947 4505.013 4576.904 4591.242 4664.339 4778.976

−0.14% −1.91% −0.75% −0.72% −0.08% −0.14% −3.66% −1.85% 2.02% −0.34% 0.01% 0.14% −0.21% −0.91% 0.18% −1.11% −2.52% −0.24% −0.60%

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