key: cord-336441-m6pur6td
authors: Wang, Changjian; Wu, Kangmin; Zhang, Xinlin; Wang, Fei; Zhang, Hongou; Ye, Yuyao; Wu, Qitao; Huang, Gengzhi; Wang, Yang; Wen, Bin
title: Features and drivers for energy-related carbon emissions in mega city: The case of Guangzhou, China based on an extended LMDI model
date: 2019-02-11
journal: PLoS One
DOI: 10.1371/journal.pone.0210430
sha: 
doc_id: 336441
cord_uid: m6pur6td

Based on the apparent energy consumption data, a systematic and comprehensive city-level total carbon accounting approach was established and applied in Guangzhou, China. A newly extended LMDI method based on the Kaya identity was adopted to examine the main drivers for the carbon emissions increments both at the industrial sector and the residential sector. Research results are listed as follow: (1) Carbon emissions embodied in the imported electricity played a significant important role in emissions mitigation in Guangzhou. (2) The influences and impacts of various driving factors on industrial and residential carbon emissions are different in the three different development periods, namely, the 10(th) five-year plan period (2003–2005), the 11(th) five-year plan period (2005–2010), and the 12(th) five-year plan period (2010–2013). The main reasons underlying these influencing mechanisms were different policy measures announced by the central and local government during the different five-year plan periods. (3) The affluence effect (g-effect) was the dominant positive effect in driving emissions increase, while the energy intensity effect of production (e-effect-Production), the economic structure effect (s-effect) and the carbon intensity effect of production (f-effect-Production) were the main contributing factors suppressing emissions growth at the industrial sector. (4) The affluence effect of urban (g-effect-AUI) was the most dominant positive driving factor on emissions increment, while the energy intensity effect of urban (e-effect-Urban) played the most important role in curbing emissions growth at the residential sector.

With anthropogenic releases of carbon emissions contributing toward climate change primarily characterized by global warming, many policymakers and scientific-researchers are seeking scales. Therefore, comprehensive inventory and specific boundary are the basis for city-level carbon emissions accounting, as well as local emission mitigation strategies [28] . The second strand of research focuses on the city-level driving factors and influencing mechanisms. Decomposition analysis (e. g. Index Decomposition Analysis (IDA) and Structural Decomposition Analysis (SDA)) and regression method (e. g. Stochastic impacts by regression on population, affluence, and technology (STIRPAT) model) are the most commonly applied methods for the scientific evaluation and quantitative analysis of factors influencing city-level carbon emissions, especially the Logarithmic Mean Divisia Index (LMDI) method based on the IDA framework. As shown in Table 1 , the SDA method based on the IO technique applied in the city-level driving factors and influencing mechanisms research in China were mainly focused on the four municipalities (i. e. Beijing, Shanghai, Tianjin and Chongqing), because of the available IO data base compiled by the Municipality Bureau of 

Wang et al. [41] 1997-2010 STIRPAT Beijing

Wang et al. [42] 2000-2010 LMDI Beijing

Mi et al. [43] 2010 IO Beijing

Wang et al. [44] 1997-2010 SDA Beijing

Tian et al. [45] 1995-2007 SDA Beijing

Wang et al. [46] 1996-2010 LMDI and Tapio index Beijing, Tianjin

Wang et al. [47] 1998-2009 STIRPAT Shanghai

Shao et al. [48] 1994-2009 STIRPAT Shanghai

Zhao et al. [49] 1996-2007 LMDI Shanghai

Shao et al. [50] 1994-2011 LMDI Shanghai

Kang et al. [51] 2001-2009 LMDI Tianjin

Li et al. [52] 1996-2012 STIRPAT Tianjin

Tan et al. [53] 2000-2012 LMDI Chongqing

Wang et al. [54] 2005-2010 LMDI Suzhou

Chen et al. [55] 2000-2013 Decoupling Index Macao

Liu et al. [56] 1995-2009 LMDI Beijing, Shanghai, Tianjin, and Chongqing

Statistics. In other words, the IO data sources were lacking in other provincial capital cities and medium-sized cities in China. Consequently, the IDA methods were used more frequently than the SDA methods based on IO technique [59] [60] [61] [62] , owing to its applicability in which each model can be applied to any available data at any aggregation level in a period-wise or timeseries manner [63] [64] [65] , where the preferred one in the LMDI [66] [67] [68] [69] [70] [71] [72] [73] . By means of LMDI method, Wang et al. [46] investigated the main driver for industrial carbon emissions in Beijing-Tianjin-Hebei economic band from 1996 to 2010, and the results showed that economic output and energy intensity were both the most important influencing factors. Wang et al. [42] decomposed the influencing factors of residential carbon emissions in Beijing and found that the rising per capita GDP and the declining energy intensity contributed the most to emissions changes during 2000 to 2010. Similar results, conducted by Wang et al. [54] , can also be found in Suzhou city. Zhao et al. [49] decomposed the main drivers for industrial carbon emissions in Shanghai and found that industrial output, energy intensity, energy and industrial structure were major determinants for emissions changes. Shao et al. [50] adopted an extended LMDI method and investigated the techno-economic drivers for industrial carbon emissions in Shanghai, and the results showed that industrial output scale was mainly responsible for emission increments while industrial structure adjustment, R&D intensity, and energy intensity were most significant factors in mitigating emissions. Kang et al. [51] performed a multi-sectoral decomposition analysis of city-level greenhouse gas emissions in Tianjin from 2001 to 2009, including the agricultural, industrial, transportation, commercial and other sectors, and the results showed that economic growth was the most important driver for emissions increments while energy efficiency was primarily responsible for emissions reductions. Hopefully, previous studies about driving factors and influencing mechanisms of city-level carbon emissions performed on China's megacities such as Beijing, Shanghai etc., have made great contributions to the realization of regional low-carbon road map. But, the previous LMDI studies mainly focused on the economic growth effect, the energy intensity effect, the population size effect, and the technology progress effect influencing the change of carbon emissions. It fails to distinguish the effects of economic structure, population structure, and energy structure on the change of carbon emissions. In fact, the strongest factors that offset China's energy consumption and carbon emissions have shifted from efficiency gains to structural changes [74, 75] . Structural indicators combined with other traditional and emerging factors should also be deeply considered and investigated in China's regional energy and carbon researches, which may perform different influencing mechanism in different regions during different development stages. Compared with four municipalities, there were relatively few studies conducting on Guangzhou city, one of China's first-tier cities, with the considerable GDP scale and total energy consumption amount (Table 2) . Meanwhile, Guangzhou has the highest level of per capita GDP and per capita energy consumption than the four municipalities. Guangzhou is the provincial capital of Guangdong province, the most developed coastal region in the southeast of China (Fig 1) . In addition, Guangzhou city is an important manufacturing base in China as well as a pilot zone for low-carbon city announced by China's National Development and Reform Commission (NDRC) in 2012. Therefore, Guangzhou city may serve as a demonstration of how to realize the targets of low-carbon city development, thereby highlighting the importance and representativeness of studying its features and drivers for carbon emissions in detail.

The innovation and contribution of this study compared with other references mainly lies in the following two aspects. Firstly, city-level total carbon accounting approach based on the apparent energy consumption data from the comprehensive energy balance table was established. Secondly, driving factors including structural indicators, e. g. economic structure, energy structure, and population structure, were investigated systematically by adopting the newly extended LMDI method based on the Kaya identity and the IDA theory. It was aimed to provide theoretical references for making more targeted policies on energy saving and emission reduction in China's mega cities.

This study is organized as follows: after the introduction, section 2 presents the methodology of city-level carbon accounting in Guangzhou and the method to decompose the changes in carbon emissions during the whole research period. Section 3 addresses the case analysis and the main results of Guangzhou city. Finally, we conclude our study and provide some policy implications for low-carbon city development. 

City-level total carbon accounting As mentioned in Table 1 , urban emissions inventory is the key for city-level total carbon accounting. However, there are accounting discrepancies between the different types of methods [21] . Based on China's energy statistic and the reference approach of IPCC Guideline for National Greenhouse gas Emission Inventories, there are mainly three approach to account, i. e. primary energy consumption (e. g. coal, oil, natural gas, and renewables) [63] , final energy consumption (e. g. coal, oil, natural gas, renewables, and electricity/heating) [61, 62] , and total energy consumption from the "Energy Balance Table" including the aggregate summary of energy production, energy transformation and final consumption [76, 77] . Compared with the primary energy consumption accounting and the final energy consumption accounting, apparent energy consumption data [78, 79] from the comprehensive energy balance table was recommended for this study. As shown in the Table 3 , the comprehensive energy balance table is mainly based on three modules, namely, 1) total energy available for local consumption as a result of interprovincial and international energy trades, 2) input-output of energy conversion transformation for thermal power, heating, coking, petroleum refining, and gas production, 3) final consumption due to primary industry, secondary industry, tertiary industry, and residential consumption. Primary industry, namely, agriculture, mainly includes farming, forestry, animal husbandry, and fishery. Secondary industry includes manufacturing industries and construction industry. Tertiary industry includes transportation, storage, post and telecommunication services, wholesale, retail trade and catering services, and other service sectors. Then, we calculated energy-related carbon emissions by energy types according to the IPCC Guidelines for National Greenhouse Gas Inventories based on the apparent energy consumption data from the comprehensive energy balance table, as follows the equation:

Greenhouse gas (GHG) in our case study is referred in particular to carbon emissions-that directly affect climate change, which didn't include other GHGs (i. e. CH 4 , N 2 O, etc.). Where the subscript i is the various fuels in this case study, t means the time in years. C t represents total carbon emissions in year t (in million tons, Mt). E i t represents the total energy consumption of fuel type i in year t (in million tons, Mt), and NCV i is the net caloric value of fuel i. CC i t is the carbon content of the fuel type i, and O i is the oxidation rate of fuel i. The conversion factors, net caloric value, oxygenation efficiency and carbon content of the various fuels are listed in Table 4 .

Data sources, consisting of the gross domestic product including agriculture, production, construction, and service, the population including rural population and urban population, the total energy consumption based on industrial and residential perspective, covering the period from 2003 to 2013, were all available. Economic, population and energy data were collected from the Guangdong Province Statistical Yearbook (2003-2014) and Guangzhou City Statistical Yearbook (2003) (2004) (2005) (2006) (2007) (2008) (2009) (2010) (2011) (2012) (2013) (2014) . Economic data was measured by GDP in Chinese Yuan in time series. Energy data includes physical quantity of total energy consumption by fuel types measured by tons of standard coal equivalent (tce), which were compiled by the Guangdong Province Statistical Bureau and Guangzhou City Statistical Bureau.

As mentioned and reviewed in Table 1 , the LMDI methods based on the IDA theory, were used frequently to quantitatively analyze the drivers of carbon emissions at the city-level. The LMDI technique based on the IDA theory and an extended Kaya identity was conducted to uncover the main driving forces for energy-related carbon emissions in Guangzhou city. The Kaya identity [18, 64, 80, 81] expresses carbon emissions as a product of four underlying driving factors:

Where P represents the population, G represents the GDP, E represents the total energy consumption; G P represents the per-capita GDP, E G represents the energy consumption intensity, and C E represents the carbon intensity of energy. Then, the Kaya identity was extended as follow:

Where X n k¼1 C i (i = 1, 2, 3, 4, 5, 6) represents carbon emissions from agriculture (i = 1), production (i = 2), construction (i = 3), service (i = 4), urban resident (i = 5), and rural resident (i = 6). k (k = 1, 2, 3, . . .n) represents the various energy types in this case study, mainly including coal, coke, crude oil, gasoline, diesel oil, kerosene, fuel oil, LPG, natural gas, and electricity. E i (i = 1, 2, 3, 4, 5, 6) represents total energy consumption by different sectors. GDP i (i = 1, 2, 3, 4) represents gross domestic product by different industries. P urban and P rural represent the total population of urban and rural residents, respectively. TI urban and TI rural represent the total income of urban and rural residents, respectively. AI urban and AI rural represent the average income of urban and rural residents, respectively. Then, Eq (3) can be rewritten as follow:

Where p = P, represents the total population size, UR represents the level of urbanization. g ¼ GDP P , represents the per capita GDP. e i ¼ E i GDP i (i = 1, 2, 3, 4, 5, 6), represents the energy consumption intensity of agriculture, production, construction, service, urban resident, and rural resident, respectively.

, represents the energy carbon intensity of agriculture, production, construction, service, urban resident, and rural resident, respectively. s i ¼ GDP i GDP (i = 1, 2, 3, 4), represents the economic structure of agriculture, production, construction, and service, respectively.

Then, the changes of regional carbon emissions between two years can be decomposed as:

Following the preferred LMDI method proposed by Ang et al. [66, 82, 83] , the various effects of each factor can be quantificationally calculated as:

In the newly established LMDI method, the population effect (ΔC p-effect ), the affluence effect (ΔC g-effect ), the energy intensity effect (ΔC e-effect ), the economic structure effect (ΔC s-effect ), and the carbon intensity effect (ΔC f-effect ). In addition, social and economic factors such as urbanization (ΔC UR-effect ), family income in urban and rural households (ΔC AI-effect ) were analyzed to further explain the changes of carbon emissions at the city level.

Economic development in Guangzhou was described with GDP, which was converted into the 2003 constant prices (Fig 2) . 

According to the urban emissions inventory in Guangzhou and Eq (1), we calculated the energy-related carbon emissions from 2003 to 2013. Total energy-related carbon emissions mainly include two parts, namely, carbon emissions generated in the boundary from fossil fuel combustion and out of the boundary embodied in the imported electricity (Fig 3) . Carbon emissions embodied in the imported electricity need to be calculated by considering the total amount of imported electricity and the corresponding emissions factor of power generation The case of Guangzhou based on extended LMDI model [56] . As for China's power supply system, electricity is supplied by the main six regional grids five-year plan period, respectively.), which were issued by the central government and performed by the local government. But in fact, carbon emissions mitigation in the boundary mainly benefited from contributions of the imported electricity from the neighbors Guizhou and Yunnan provinces via the cross-province electricity trades. During the same period, carbon emissions embodied in the imported electricity dramatically increased 3.236 million tons in 2010 to 4.379 million tons in 2013. Rapidly increasing carbon emissions embodied in imported electricity indicated that there were large amounts of fossil energy were burned for power. Final products-thermal power was consumed in Guangzhou, while carbon emissions were discharged in its neighbor producers. If these carbon emissions embodied in the imported electricity were allocated to the final consumer, Guangzhou would have paid more efforts and implemented more strict mitigation measures to achieve its energy saving and emission reduction targets.

Aiming to have a better understanding of the complex various influencing factors for carbon emissions in Guangzhou city, we decomposed the total carbon emissions into two parts including the industrial sector and the residential sector.

Driving factors of carbon emissions in industrial sector in Guangzhou. Driving factors of carbon emissions in industrial sector were decomposed yearly first (Table 5 ). In order to have a better understanding of influencing factors in long time series, we divided the carbon emissions process into 3 periods (Fig 4) , according to the regional five-year plans for socioeconomic development, combined with a certain historical background.

During the 10 th five-year plan period (2003) (2004) (2005) , Guangzhou announced the overall objective in the 10 th five-year plan that it would take the lead in realizing socialist modernization and building the modernization central city over China. Guangzhou's industrial carbon emissions increased by 6.29 million tons mainly because the rapid economic development. The affluence effect (g-effect) was the dominant positive effect in driving carbon emissions increase, bringing in a 5.43 million tons increase, accounting for 86.34% of the total industrial carbon emissions changes, which was followed by the energy intensity effect of service (e-effect-Service), the carbon intensity effect of service (f-effect-Service), and the economic structure effect (s-effect). In order to achieve the basic standards of modernization (i. e. per capita GDP is more than 5000 U.S. dollar, and the third industry added value accounted for more than 50% of GDP etc.), Guangzhou paid more efforts to foster the three pillar industries (i. e. electronic information manufacturing industry, automobile industry, and petrochemical Table 5 

Period The case of Guangzhou based on extended LMDI model economic structure effect (s-effect). The adjustment of industrial structure in Guangzhou was mainly focused on upgrading the traditional advantage industries (i. e. iron and steel, shipbuilding, machinery and equipment, electricity, paper, and textile etc.) and fostering the new and high technology industries (i. e. Software, new material, new energy, and digital industry etc.), announced in the 11 th five-year plan for socioeconomic development in Guangzhou Driving factors of carbon emissions in residential sector in Guangzhou. Driving factors of carbon emissions in residential sector were decomposed yearly first (Table 6 ) and divided into 3 periods (Fig 5) . Although the residential carbon emissions in Guangzhou accounted for a relatively small proportion of the total carbon emissions, it exhibited a rapid growth trend during the whole research period along with the improvement of urbanization level. The total residential carbon emissions increased from 0.55 million tons in 2003 accounting for 3.61% of the total carbon emissions to 2.01 million tons accounting for 6.59% in 2013. Furthermore, the residential carbon emission increments were mainly generated by the urban residents, which increased from 0. The case of Guangzhou based on extended LMDI model was always two times of the average income of rural residents during the same period. The energy intensity effect of urban (e-effect-Urban) played the most important role in curbing carbon emissions growth, resulting in a 0.26 million tons decrease, accounting for 34.87% of the total residential carbon emissions changes in absolute value. During the 12 th five-year plan period (2010-2013), Guangzhou's residential carbon emissions increased by 0.51 million tons. The affluence effect of urban (g-effect-AUI) and the carbon intensity effect of urban (f-effect-Urban) were the most two dominant positive driving factors on the carbon emissions increment, bringing in 0.49 million tons and 0.29 million tons increase, accounting for 95.88% and 57.43% of the total residential carbon emissions changes, respectively.

The energy intensity effect of urban (e-effect-Urban) played the most important role in curbing carbon emissions growth, resulting in a 0.33 million tons decrease, accounting for 64.95% of the total residential carbon emissions changes in absolute value.

Based on the apparent energy consumption data from the comprehensive energy balance table, a systematic and comprehensive city-level total carbon accounting approach was established and applied in one of the China's mega city-Guangzhou. Total carbon emissions including the fossil fuel combustion in the city's boundary and embodied in the imported electricity out of the boundary were still performing a rising tendency. Carbon emissions embodied in the imported electricity played a significant important role in emissions mitigation in Guangzhou, especially after 2009. Examining the effects of different boundaries on carbon accounting at city level is crucial for the emissions mitigation in urban China.

Then, the newly extended LMDI method based on the Kaya identity was adopted to examine the main drivers for the carbon emissions increments both at the industrial sector and the residential sector in Guangzhou city. Quantitative analyses and time-series analysis were performed on the influencing mechanism for various influencing factors both at the industrial sector and the residential sector for the three different development periods, namely, the 10 th five-year plan period (2003) (2004) (2005) , the 11 th five-year plan period (2005-2010), and the 12 th five-year plan period (2010-2013). The influences and impacts of various driving factors on industrial and residential carbon emissions are different in the three different development periods. Overall, the affluence effect (g-effect) was the dominant positive effect in driving carbon emissions increase, while the energy intensity effect of production (e-effect-Production), the economic structure effect (s-effect) and the carbon intensity effect of production (f-effect-Production) were the main contributing factors suppressing the carbon emission growth at the industrial sector. Considering the high urbanization level in Guangzhou, the affluence effect of urban (g-effect-AUI) was the most dominant positive driving factor on the carbon emissions increment, while the energy intensity effect of urban (e-effect-Urban) played the most important role in curbing carbon emissions growth at the residential sector.

In conclusion, affluence effect-economic growth, was still the most important contributor to the increases in carbon emissions in urban China. However, structural indicators, such as economic structure and energy intensity of different industrial sector, were playing significant negative effects on carbon emissions. In the future, economic structure adjustment and energy intensity improvement at the detailed production industries should be deeply investigated, in order to make these indicators play more negative effects on carbon emissions.

Considering the significant important role played by the emissions embodied in the imported electricity via the cross-province electricity trades, Guangzhou should adjust its current emissions mitigation policies which only consider its emissions occurring within the city's boundary. Guangzhou should make more joint efforts to help its neighbors improve the efficiency of thermal power generation. Guangzhou might make efforts to buy more clean power such as hydropower from its neighbors Yunnan and Guangxi provinces to replace a certain amount of thermal power. In addition, carbon trading scheme [87] about the cross-province electricity trades should be introduced and conducted on the imported electricity in Guangzhou.

In order to further promote the negative effects of the economic structure effect (s-effect), the energy intensity effect of production (e-effect-Production), and the carbon intensity effect of production (f-effect-Production), Guangzhou should pay more efforts to the optimization of energy consumption structure and industrial structure at the industrial sector. (1) Coal consumption in energy mix should be further decreased, while relatively low carbon energy such as natural gas should be accelerated both in the industrial and residential sectors. Renewable energy such as solar PV and hydropower should also be encouraged. (2) Efficiency of thermal power generation should be further improved in Guangzhou, especially the major power plants such as the Pearl River power plant and the Huarun power plant in Nansha district, the Guangzhou power plant in Liwan district, the Hengyun power plant, the Huangpu power plant, and the Ruiming power plant in Huangpu district. (3) Energy consumption structure and energy utilization technique should be also effectively optimized and improved, especially in the pillar industries (i. e. electronic information manufacturing industry, automobile industry, and petrochemical industry), the traditional advantage industries (i. e. iron and steel, shipbuilding, machinery and equipment, paper, and textile etc.) and the new and high technology industries (i. e. software, new material, new energy, and digital industry etc.) in Guangzhou in the current and near future.

Urbanization level of Guangzhou is relatively high than other cities in Guangdong province and even in China. The rapid increase in the average income of urban residents along with the high level of urbanization has made the affluence effect of urban (g-effect-AUI) become the most dominant positive driving factor on the residential carbon emissions increment during the whole research period. The residential energy consumption structure was mainly based on gasoline, liquefied petroleum gas, and natural gas in Guangzhou. In order to further promote the negative effects of the energy intensity effect of urban (e-effect-Urban), more clean fuels such as domestic solar systems should be added to the total residential energy consumption, and low carbon energy consumption behaviors and lifestyles should be advertised and practiced actively. 

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82. Ang