Relationship between social civilization forms and carbon emission intensity: A study of the Shanghai metropolitan area

Journal of Cleaner Production 228 (2019) 1552e1563

Contents lists available at ScienceDirect

Journal of Cleaner Production journal homepage: www.elsevier.com/locate/jclepro

Relationship between social civilization forms and carbon emission intensity: A study of the Shanghai metropolitan area Yishao Shi a, *, Hefeng Wang b, Shouzheng Shi c a

College of Surveying and Geo-Informatics, Tongji University, Shanghai, 200092, China College of Mining & Geomatics, Hebei University of Engineering, Handan, Hebei Province, 053860, China c College of Environmental Science and Engineering, Suzhou University of Science and Technology, Suzhou, 215011, China b

a r t i c l e i n f o

a b s t r a c t

Article history: Received 29 November 2018 Received in revised form 20 April 2019 Accepted 26 April 2019 Available online 1 May 2019

Historically, the classification of traditional social civilization forms on a macroscopic scale has been mainly based on religious or cultural differences, and the identification of spatial heterogeneity was not detailed. Likewise, Morris' methodology for measuring social development only considered the overall difference between the Western and the Eastern world but ignored the more detailed measurement of spatial differentiation. Additionally, the relationship between social civilization forms and carbon cycling remains uncertain. Aiming to improve these deficiencies, the goal of this article is to identify the heterogeneous social civilization forms on a metropolitan scale based on the data of land use patches and to quantify the carbon emission intensity of different social civilization forms in the Shanghai metropolitan area. The results show the following: (1) The carbon emission intensity of the industrial civilization form is 4.5 times that of the urban civilization form, 8 times that of the rural civilization form and 10 times that of the public civilization form. (2) The carbon sink capacity per unit area of the agricultural civilization form is 1.6 times that of the ecological civilization form and 17.4 times that of the wilderness form. (3) Shanghai's suburbs, especially the inner suburbs, have become a major area for carbon emissions due to the continued migration of population and industry. (4) Generally, the evolution of social civilization yields changes in the total amount and intensity of carbon emissions and carbon sinks. This study not only identifies the spatial heterogeneity of social civilization forms from the perspective of land use functional types rather than religious and cultural differences but also reveals the evolution characteristics of social civilization forms from the perspective of carbon cycle rather than cultural factors. © 2019 Elsevier Ltd. All rights reserved.

Keywords: Social civilization forms Land use space Carbon emission intensity Shanghai metropolitan area

1. Introduction “Civilization” refers to the process of a society developing into a centralized, urbanized, and stratified structure (Civilization_wikipedia, 2018). Social civilization has both a broad and a narrow sense. A generalized social civilization refers to the level of civilization and the progress of human society, which is the sum of the positive results from humans transforming the objective and subjective world, and the unity of material civilization, political civilization, national civilization and human civilization. Social civilization, in a narrow sense, refers to the social progress and the positive results of social construction, including the sum of the

* Corresponding author. E-mail addresses: [email protected] (Y. Shi), [email protected] (H. Wang), [email protected] (S. Shi). https://doi.org/10.1016/j.jclepro.2019.04.356 0959-6526/© 2019 Elsevier Ltd. All rights reserved.

social main body civilization, social relation civilization, social concept civilization, social system civilization and social behaviour civilization (Social civilization_baike.baidu, 2018). In short, social civilization refers to adequate production, living and ecological modes in human society. Social civilization is an educated, orderly, fair and advanced stage of development that human society expects. Social civilization exhibits imbalance in time and space. From the history of development of human society, social civilization can be divided into five stages: primitive civilization, agricultural civilization, industrial civilization, information civilization and ecological civilization (Sun, 2014). From the form of human civilization, social civilization can be divided into five types: material civilization, spiritual civilization, political civilization, legal civilization and ecological civilization. “Civilization” can also refer to the culture of a complex society. Quigley (1979) posed a division of culture into six levels, i.e., intellectual, religious, social, political,

Y. Shi et al. / Journal of Cleaner Production 228 (2019) 1552e1563

economic and military. Toynbee (1987) studied the prosperity and decline of twenty-one mature cultures (there are five other cultures that have disappeared). He summarized the development law of human civilization as the “law of progressive simplification”, i.e., “measuring the development of a civilization depends on its ability to transfer energy and attention from material to spiritual, aesthetic, cultural and artistic, and the ability of this conversion” (Graaf et al., 2014). Braudel (1995) and Huntington (1997) divided the global civilization into nine main types based on religious or cultural differences, i.e., Sinic, Indian, Japanese, Western, Islamic, Latin American, Orthodox and African civilizations. However, the main shortcoming of the above classifications is that excessive emphasis is placed on the decisive role of religious and cultural factors in the evolution of world civilization. Cultural factors are only one of many influencing factors, and sometimes they are not the only crucial factor. Factors such as power, wealth, and unfair distribution can sometimes have a greater impact than cultural factors. On the other hand, the classification of civilization is not detailed enough and largely ignores internal heterogeneity. To measure social development across time and space, Morris (2013) selected four characteristics such as energy acquisition, social organization, war capability and information technology to comprehensively measure social development from a historical perspective. The research of current social civilization forms is focused on the relationship between culture and civilization and the construction of new civilization patterns. Using historical and comparative research methods, Sakaeva et al. (2018) discussed the relation and difference between culture and civilization and pointed out that civilization is a sustainable socio-cultural formation and is a necessary condition for cultural and human development. Gabitov et al. (2018) believed that it is important to introduce a new culture of peace, i.e., a new sustainable development model of modern telecommunication civilization, aimed at reaching a long-term state of global balance and sustainable development. In general, this study identified some deficiencies in the existing research associated with social civilization forms. (1) Previous studies mostly focused on research of the macro scale (global, multinational and national scales), and rarely consider the spatial differences in the micro scale or within the city. (2) Historically, the classification of social civilization forms has been mainly based on religious or cultural differences (Toynbee, 1987; Braudel, 1995; Huntington, 1997), and the identification of spatial heterogeneity was not detailed. Likewise, Morris' methodology for measuring social development only considered the overall difference between the Western and the Eastern world but ignored the more detailed measurement of spatial differentiation (Morris, 2013). In addition, due to international concerns about global warming, land use/cover change (LUCC) and carbon cycling have been two popular research topics of global change since the 1990s; research considering both these topics has focused on the following: (1) The impact of LUCC on the carbon cycle of the ecosystem. LUCC is a crucial factor affecting the carbon cycle of terrestrial ecosystems. Howard et al. (1995) examined the consequences of changes in the patterns of land use for soil organic carbon stores and the net flux of carbon between the land and atmosphere using a Markov model. Scott and Tate (1999) believed that land use could change the microclimate and physical and chemical characteristics of ecosystems, thus affecting the quality and rate of litter decomposition and soil carbon, nitrogen and water contents. Bolin & Sukumar (2000) thought that land cover type was an important factor in determining the carbon storage of the terrestrial ecosystem and determined that a change in land cover type was accompanied by a large amount of carbon exchange.

1553

Miles and Kapos (2008) discussed the impact of global LUCC on greenhouse gas emissions and proposed to reduce greenhouse gas emissions by reducing deforestation and forest degradation. Yu et al. (2018) examined long-term carbon storage change in response to LUCC and agricultural management in the Midwest United States from 1850 to 2015; they observed that LUCC led to a reduction in the vegetation carbon pool, yet agricultural management barely affected its change; LUCC reduced soil carbon, while agricultural management practices increased soil carbon stock. This finding also implied that LUCC plays a vital role in the regional carbon balance. (2) The estimation methods of land use carbon emission and carbon storage. Scholars have used a variety of methods to quantify the impact of LUCC on carbon emissions and carbon storage. At present, land use carbon emission accounting methods can be summarized into five types: ① Model estimation, including the book-keeping model, process model and space model. For example, Houghton and his colleagues (1991; 1999a; 1999b) established a book-keeping model to calculate and analyse the dynamic relationship between the change in land use and the emissions of carbon in Latin America, the United States and Asia. In particular, they studied the change in surface coverage caused by land use change, such as deforestation, returning farmland to forest and farmland management impacts on carbon emissions. However, because the time and space representation of model parameters is difficult to define, this method has certain limitations. ② Sample land inventory method, based on the analysis of land use types. The distribution and carbon density of different terrestrial ecosystems are estimated according to field surveys and statistics, which is the mainstream method to calculate the carbon reserves of the forest ecosystem. ③ Estimation method based on remote sensing and map interpretation data. In general, land use transfer matrix or land use dynamic charts are used to estimate the carbon storage based on the differences in carbon density among land use types. ④ Vorticity and box observation. Vorticity is the vertical flux of a substance, that is, the covariance of its concentration and velocity. The chamber method is used to measure the change rate of carbon dioxide between the ground and atmosphere in the chamber by placing a specific volume of the chamber on the surface to be measured. For instance, Houghton and Magnitude (2002) estimated that land use change and the resulting carbon emissions during 1850e1998 account for one-third of the total emissions from human activities. ⑤ Comprehensive estimation methods. For example, using inventory and modelling methods, Ni (2013) estimated carbon storage in Chinese terrestrial ecosystems. He pointed out that further studies require a comprehensive methodology that combines the national inventory, field measurement, eddy covariance technique, remote sensing, and model simulation in a single framework. (3) The relationship between urbanization and carbon emission has become a global issue with the rapid industrialization and urbanization. Newman and Kenworthy (1989) found that the variation in gasoline consumption per capita in ten large cities of the United States depended primarily on land use and transportation modes, rather than price or income variations. Among these cities, urban density, urban size, urban spatial form and spatial structure all influenced the energy consumption of transportation, thus affecting the level of carbon emission. Through investigation and analysis in a wider space-time context, Coevering and Schwanen

1554

Y. Shi et al. / Journal of Cleaner Production 228 (2019) 1552e1563

(2006) concluded that “the influence of urban form and spatial structure is more significant, while urban density is not universal”. Chen et al. (2008) estimated the relationship between compactness and environmental performance using panel data of 45 Chinese cities and found that the explanation power of density variables may be overwhelmed. Glaeser and Kahn (2010) found that there was a strong negative association between CO2 emissions and land use regulations. Sun et al. (2013) analysed the correlation between the different stages of urbanization and the dynamic evolution of the CO2 emissions based on the STIRPAT (stochastic impacts by regression on population, affluence and technology) model (York et al., 2003). By introducing a city development-stage framework, Shen et al. (2018) adopted the method of the logarithmic mean Divisia index to study the factors affecting carbon emission. Using a traffic assignment model, Zhang et al. (2018) observed that land use and landscape patterns were significantly correlated with transport-related carbon emissions in Changzhou, China.

(heterogeneous urban functional areas) to reveal more detailed spatial differences in the carbon emission intensity of different social civilization forms. (3) This study includes new research content and quantifies the relationship between the social civilization forms and carbon emission intensity in the Shanghai metropolitan area.

The city is a special form of human agglomeration that gradually formed with the development of social productivity. The city is not only the crystallization of human civilization but also continuously evolves with the progression of human civilization. Cities are the areas where human activities have the most profound impact on the surface of the earth, and more than 80% of CO2 emissions come from urban areas with strong economic activities (Churkina, 2008). A large number of existing research results have discussed the complex and diverse relationships between urbanization, economic growth, energy consumption and CO2 emissions on the macroscopic and mesoscopic scales based on statistical data, specifically taking into account the differences in development stages and income levels, including the global (Mardani et al., 2019; Liu and Hao, 2018; Pao and Chen, 2019; Dong et al., 2019; Shuai et al., 2019), national (Saidi and Hammami, 2015; Wang et al., 2018; Ma et al., 2019), regional (Dong et al., 2018) and urban scales (Cai et al., 2017; Fry et al., 2018). These studies found that the main factors influencing carbon emissions are usually energy structure, energy intensity, industrial structure, economic output and population scale (Shen et al., 2018). Overall, this study also identified some drawbacks in the extant literature associated with carbon emissions. (1) The previous data are mainly obtained from the statistical department or interpreted from remote sensing images (Feng, 2013; Cai et al., 2017), which results in relatively higher error. (2) Little work has been performed on carbon emissions research based on land use functional types within cities. (3) Previous studies have examined the relationship between carbon emissions and socio-economic factors such as energy consumption, industrial structure, economic output, population size, land use patterns and regulations, transportation modes, urban morphology and urban spatial structure (Newman and Kenworthy, 1989; Coevering and Schwanen, 2006; Glaeser and Kahn, 2010; Zhang et al., 2018; Huo et al., 2019). However, the relationship between the social civilization forms and carbon emission intensity is rarely considered. To fill these gaps, Shanghai is chosen as the case city in this study, the heterogeneous spaces within the city are identified, and the relationship between different types of social civilization and carbon emissions is examined based on data from land use patches and geographic information system (GIS) technology. The contribution of this study is mainly as follows: (1) This study uses the data of land use patches, which helps to improve the accuracy of the data. (2) This study includes a new research scale, and more detailed spatial heterogeneity is considered. This paper integrates the micro-scale (land use patches) with the meso-scale

In this article, data of land use patches of Shanghai in 2009 were obtained from the second national land survey, which was provided by Shanghai Institute of Geological Surveys. The energy balance sheet and demographic, economic and social data were obtained from “Shanghai Statistical Yearbook”, “Shanghai Statistical Yearbook on Industry, Energy and Transport”, “Shanghai Energy Statistics Yearbook” and Statistical Yearbooks of relevant districts and counties in 2010.

2. Data preparation 2.1. Study area The research area is Shanghai, which is one of the economic centres of China and an increasingly global city. Shanghai covers a total area of 8133.39 square kilometres. The city has jurisdiction over seventeen districts and one county, of which nine districts are located in the central city, which had a permanent resident population of 24.15 million by the end of 2009 (Table 1). 2.2. Data sources

2.3. Data processing In the Shanghai energy balance sheet (real energy), “Column” is a variety of primary energy and secondary energy, and “Row” is divided into six items, i.e., the energy available for consumption in the region, processing conversion input (
) and output (þ), loss, terminal consumption, balanced differences, and total consumption. The steps of data processing are as follows: 2.3.1. Pretreatment of energy balance sheet First, all types of energy under the “loss” indicator in the energy balance sheet are excluded from energy consumption for combustion. Second, the “balanced differences” are processed. If the balanced difference is large, it shall be apportioned according to the proportion of various energy consumed by each terminal department. Third, “processing the converted input (
) and output (þ) quantity”. In the calculation of carbon emissions (range 1), carbon

Table 1 Land use area of each district and county in Shanghai in 2009. Regions

Land area (hm2)

Zones

Land area (hm2)

Central city* Pudong new area Minhang district Jiading district Baoshan district Chongming county Fengxian district Jinshan district Songjiang district Qingpu district Shanghai

28885 153139 37281 46317 36533 249389 73339 61155 60449 66852 813339

Central urban area Inner suburbs

28885 273270

Outer suburbs

511184

Note: Central city includes Huangpu, Jingan, Luwang, Hongkou, Zhabei, Putuo, Xuhui, Changning, Yangpu districts.

Y. Shi et al. / Journal of Cleaner Production 228 (2019) 1552e1563

emissions are calculated at the production end, and the carbon emissions of power generation and heating input fuel are calculated. The power and heat consumption in the terminal consumption is no longer calculated (in other words, the emission coefficient is calculated at 0 value). In the conversion process of coal washing and preparation, coking, oil refining and gas generation, the secondary energy output is included in the calculation of carbon emissions based on terminal consumption, and the energy input in these processes does not participate in the calculation of carbon emissions. Fourth, bf gas and bof gas are by-products of smelting iron and steelmaking respectively. The carbon emissions are not calculated repeatedly, that is, the carbon emissions are calculated as 0. Fifth, the industrial structure and energy structure of carbon emissions are calculated. In the calculation of carbon emissions, it is often necessary to calculate the emissions of the industrial structure of primary industry, secondary industry, tertiary industry and living consumption, as well as the energy consumption structure of coal, oil and natural gas. The energy consumption structure clearly belongs to range 1 carbon emission. To maintain that the total carbon emissions of range 1 industry are equal to the total energy carbon emissions, the carbon emissions generated by power and heat production are included in the secondary industry of terminal consumption, while the carbon emissions of power and heat are not included in the primary industry, tertiary industry and living consumption. When calculating the range 2 of industrial carbon emissions, the carbon emissions generated by electric power and thermal power production are not directly calculated, and the product of the electricity consumption and heat consumption in the primary industry, secondary industry, tertiary industry and living consumption with the emission coefficient of electric power and thermal power in the current year is used for consumption end accounting. Sixth, in the calculation of energy consumption structure, the carbon emissions of various fuel varieties produced by coal washing, coking and gas generation are included in coal, and the carbon emissions of fuel varieties produced by oil refining are included in oil. The classification of fossil fuels involved in the calculation is shown in Table 2. Seventh, industry in terminal consumption is used as raw materials and is excluded from the calculation of carbon emissions.

1555

vehicles is not included in the statistics of the transportation sector but scattered in the energy statistics of agriculture, various service industries and living consumption. From small traffic to large traffic, non-operating vehicles, private cars and agricultural transport machinery consumption need to be accounted for. Wang (2009), Qi (2011), and Wang et al. (2018) successively adopted the “from top to bottom” method to study oil product allocation by using the energy balance sheet. In addition, in the book “2005 China Greenhouse Gas Inventory Study” (2014), the Department of Climate Change Response (DCCR) of the National Development and Reform Commission used both “from bottom to top” and “from top to bottom” methods to study the oil product allocation. Table 3 is the specific proportion of transportation oil products from the above methods. From method 1 to method 3, the division of which energy carbon emissions belong to the transportation sector and which do not belong to the transportation sector is gradually improved. However, the allocation proportion is determined roughly. The allocation ratio of gasoline and diesel in method 4 is determined on the basis of the known data of the whole large transportation activity level. Therefore, the division of climate change response calculates the extraction ratio of gasoline and diesel in each department more carefully. In addition, method 4 was derived from the technical report of the greenhouse gas inventory task force of the Second National Bulletin of the People's Republic of China on Climate Change approved by the State Council, which was recognized at the national level. In this paper, method 4 data are preferred, and the missing data are supplemented by method 3. Sectoral carbon emissions are also divided into two types: range 1 and range 2. The calculation principle is the same as industrial carbon emissions. When range 1 is calculated, the electric power and thermal power department shall calculate the production carbon emissions separately, and the electric power and thermal power emission coefficient of other departments shall be calculated as 0 to avoid double calculation. When range 2 is calculated, excluding the power and thermal departments, other departments calculate the consumed power and thermal carbon emissions at the consumer end according to the current power and thermal carbon emission coefficient. The general sectoral division of energy and carbon emissions based on the energy balance sheet is shown in Table 4 (Huo et al., 2018). 2.4. Identification of social civilization forms

2.3.2. Sectoral division of energy consumption In international energy and carbon emissions accounting, the intermediate consumption and terminal sectors of energy are usually divided into six sectors: power production, thermal production, agriculture, industry, transportation and building. Here, transportation is referred to as “large traffic”. China's statistics on “transportation, warehousing and postal services” only include energy consumption by transportation operations or “small traffic”. Thus, the fuel consumption of a large number of non-operating

Coal

Petroleum

Natural gas

Combining the results of our recent research (Shi et al., 2019) and the latest land use condition classification of China (Ministry of Land and Resources of the People's Republic of China, 2017), Shanghai's land space was divided into seven level II types, including urban construction subspace, industrial development subspace, agricultural production subspace, rural living subspace, green ecological subspace, other ecological subspaces and other subspaces (Table 5). Urban land subspace is the result of the merging and reorganization of land use types based on the initial classification of urban land use. The corresponding social civilization forms of various land use subspaces are shown in Table 5 and Fig. 1. The land space area of all kinds of districts and counties in Shanghai in 2009 are shown in Table 6.

raw coal Cleaned coal Other coal washing Briquette coal Coke Coke oven gas Other gas Other coking products

crude oil Gasoline Kerosene Diesel Fuel oil Petroleum coke Liquefied petroleum gas Refinery dry gas Other petroleum products

Natural gas Liquefied natural gas

3. Methodology

Table 2 Classification of fossil fuels involved in the calculation.

Land use carbon emissions include both direct and indirect carbon emissions (Newman and Kenworthy, 1989; Houghton and Hackler, 1999b; Bolin & Sukumar, 2000; Miles and Kapos, 2008; Chen et al., 2008; Sun et al., 2013; Mei and Chen, 2017; Yu et al., 2018; Zhang et al., 2018). The former includes the carbon

1556

Y. Shi et al. / Journal of Cleaner Production 228 (2019) 1552e1563

Table 3 Share of fuel consumption in the transport sector. Oil product allocation department

Method 1a

Method 2

b

Industry (including building) Agriculture Living consumption Other services Transportation, warehousing and postal services

95% gasoline, 35% diesel 100% gasoline All gasoline, 95% diesel 95% gasoline, 35% diesel All of the energy

95% gasoline, 35% diesel All gasoline, 25% diesel All gasoline, 95% diesel 95% gasoline, 35% diesel All energy except 15% electricity

Method 3c

Method 4d

95% gasoline, 35% diesel All gasoline, 25% diesel All gasoline, 95% diesel 95% gasoline, 35% diesel Deduct coal and coke, deduct 15% electricity and all heat

79% gasoline, 26% diesel 80% gasoline, 10% diesel 99% gasoline, 95% diesel 98% gasoline and diesel is unknown 100% gasoline, 100% diesel, others unknown

Notes. a Wang, Q. Y.,2009. b Qi, 2011. c Wang and Zhou, 2014. d DCCR, 2014.

Table 4 General sectoral division of energy consumption and consumption adjustment table. Sector

Subsector

Corresponding departments in the energy balance sheet

Accounting method

Power generation Power generation

All energy

Heat production

All energy

Agriculture Industry Transportation

Buildings

Processing conversion input (
) output (þ) quantity - thermal power generation Heat production Processing conversion input (
) output (þ) quantity - supply of heat Agriculture Terminal consumption - agriculture, forestry, animal husbandry, fishing and water resources Industry Terminal consumption - industry Terminal consumption - building Operation of the traffic Terminal consumption - transportation, warehousing and postal services Non-operating traffic Terminal consumption - agriculture, forestry, animal husbandry, fishing and water resources Terminal consumption - industry Terminal consumption - building Terminal consumption - wholesale, retail and accommodation and catering Terminal consumption - others Terminal consumption - living consumption Residential buildings Terminal consumption - living consumption Public buildings Terminal consumption - wholesale, retail and accommodation and catering Terminal consumption - others Terminal consumption - transportation, warehousing and postal services

All energy except for 80% gasoline and 10% diesel All energy except 79% gasoline and 26% diesel after raw materials All energy except 79% gasoline and 26% diesel Deduct coal and coke, deduct 15% electricity and all energy except all heat 80% gasoline and 10% diesel After raw materials are deducted, 79% gasoline and 26% diesel 79% gasoline and 26% diesel 98% gasoline, 35% diesel

99% gasoline, 95% diesel All energy except for 99% gasoline and 95% diesel All energy except for 98% gasoline and 35% diesel

All coal and coke, 15% electricity and all heat

Table 5 Land function types and social civilization forms in Shanghai. LevelⅠtypes

LevelⅡtypes

Level Ⅲ types

Social civilization forms

Urban subspace

Urban construction subspace

Urban civilization form

Agricultural subspace

Industrial development subspace Agricultural production subspace Rural living subspace

Ecological subspace

Green ecological subspace

Land for commercial services, urban residence, organizations, science, education, press, publication, health, charity, recreations, sports etc. Land for industries and warehouse Cultivated land, garden plot, other agricultural land etc. Rural residential land, rural public facilities and services land etc. Forests, grassland, urban garden and green space, water area, wet land, continental sea etc. Saline and alkaline land, vacant land, sand land, bare land etc. Land for infrastructure such as energy, transportation and communications, water conservancy facilities and land for special use such as military and religion

Other ecological subspace Other subspace

/

emissions from land use change (such as the conversion of agricultural land to construction land and the conversion of forest land to farmland or grassland) and land use type maintenance (such as the change in farmland management measures, deforestation and grassland grazing, and the change in secondary and tertiary

Industrial civilization form Agricultural civilization form Rural civilization form Ecological civilization form Wilderness form (Uncivilized natural form) Public civilization form

industrial land allocation). The latter includes land carrying carbon emissions caused by human activities, such as industrial energy consumption carbon emissions loaded by industrial and mining land, energy consumption carbon emissions loaded by transportation land, and residential heating carbon emissions (Mei and

Y. Shi et al. / Journal of Cleaner Production 228 (2019) 1552e1563

1557

Fig. 1. Classification of social civilization forms based on land use functional types in Shanghai metropolitan area.

Table 6 Land space area of all kinds of districts and counties in Shanghai in 2009 (Unit: hm2). Regions

Urban construction subspace

Industrial development subspace

Agricultural production subspace

Rural living subspace

Green ecological subspace

Other ecological subspace

Other subspace

Total

Shanghai Central city Pudong new area Minhang district Jiading district Baoshan district Chongming county Fengxian district Jinshan district Songjiang district Qingpu district

71864 15265 17110 9174 5003 5841 1724 3548 2871 6834 4494

82155 3875 16704 8117 9653 9126 3647 8740 6718 8629 6946

211465 25 34078 4441 12019 3570 67621 26035 27405 17947 18324

55774 337 13293 3094 4984 2024 11927 5810 4792 4615 4897

311157 3114 50025 6489 8906 11255 150990 23699 13983 15792 26905

13205 12 6885 1552 1341 268 624 416 562 1079 468

67719 6257 15044 4415 4411 4448 12857 5091 4824 5553 4819

813339 28885 153139 37281 46317 36533 249389 73339 61155 60449 66852

Chen, 2017). The carbon emissions of construction land are calculated indirectly according to the carbon emission coefficient of energy consumption carried by it, and the other land types are calculated by area. According to the 2006 IPCC (Intergovernmental Panel on Climate Change) Guidelines for National Greenhouse Gas

Inventories, carbon emissions and carbon sinks mainly include five types: energy sources, industrial processes and products, agriculture, forestry and other land use, waste and others. This article focuses on the first three categories. The carbon emissions are calculated by the following formula:

1558

Y. Shi et al. / Journal of Cleaner Production 228 (2019) 1552e1563

Table 7 Carbon sink coefficient of Shanghai land use types (Unit: t$a-1$hm-2). Land use type

Cultivated land

Forest land

Shrubland

Grassland

Greenbelt

Inland water

Wetland

Carbon sink coefficient


5.6655


9.225


4.9275


0.49275


5.99


0.5667


2.3562

Notes: ①Carbon sink coefficient of Shanghai cultivated land ¼ Net crop biomass  Crop carbon content coefficient ¼ 12.59  0.45 ¼ 5.6655. ②Carbon sink coefficient of Shanghai forest land ¼(Average net yield of tree layer þAverage net yield of shrub layer)  plant carbon content coefficient¼(9.55 þ 10.95)  0.45 ¼ 9.225. ③Carbon sink coefficient of Shanghai shrubland ¼ 10.95  0.45 ¼ 4.9275. ④Carbon sink coefficient of Shanghai grassland was 10% of the carbon sink capacity of shrubby woodland. ⑤Carbon sink coefficient of Shanghai greenbelt was derived from Guan et al.'s research results (Guan et al., 1998). ⑥Carbon sink coefficient of Shanghai inland water and wetland was derived from Duan et al.'s research results (Duanet al., 2008).

E¼

X X X ai *di þ bj *dj þ ck *dk

(1)

Ej ¼

X X bj * d j ¼ bj *hj *pj *oj

(2)

where E is the total carbon emissions; ai is the land area corresponding to land type I, except the construction land; di is the carbon emission coefficient of the i-th land type; bj is the j-th energy consumption loaded by construction land; dj is the carbon emission coefficient of the j-th energy source; ck is the k-th industrial processes and products loaded by construction land; and dk is the carbon emission coefficient of the k-th industrial process and product (this paper mainly considers cement, iron and steel industrial processes and products).

where Ej represents the total carbon emissions from various energy consumption, hj represents the average low heat value of the j-th energy source, pj represents the potential emission factor of the jth energy source; and oj represents oxidation rate of the j-th energy source. The average low heat value, potential emission factor and oxidation rate of various energy sources are shown in Table 8. The product of the three terms is the carbon emission coefficient of various energy sources.

3.1. Determination of carbon emission coefficient of various land use types

3.2.2. Carbon emission from electric power and thermal production First, the power discharge coefficient and thermal discharge coefficient are calculated according to the energy balance sheet. The formula is as follows:

The carbon emission coefficients of land use types other than construction land are primarily considered here. Combined with the research literature and various empirical data, the carbon emission coefficients of various land use types are determined and presented in Table 7. 3.2. Determination of carbon emission coefficient of various energy resources 3.2.1. Carbon emissions from energy resources The formula for carbon emissions from various energy resources is as follows:

Pd ¼ Cp / (Tp þ Pp)

(3)

Td ¼ Ch / (Hl þ Rh)

(4)

where Pd denotes the power discharge coefficient, Cp represents the carbon emissions from power generation, Tp represents the thermal power generation, Pp represents the primary power generation, Td denotes the thermal discharge coefficient, Ch represents the carbon emissions from heating, Hl represents the heating load, and Rh represents the recycled heat energy.

Table 8 Carbon emission coefficient of various energy sources. Energy types Solid fuels: Power coal Other raw coal Cleaned coal Other coal washing Briquette coal Coke Other coking products Liquid fuels: Raw petroleum Gasoline Kerosene Diesel Fuel oil (Heavy oil) Petroleum coke Liquefied petroleum gas Refinery dry gas Other petroleum products Liquefied natural gas Gas fuels: Natural gas Coke oven gas Other gas

Average low heat value(kJ/kg)

Potential emission factor (kgC/GJ)

Oxidation rate

Carbon emission coefficient(kg/kg)

20254.8 21501.4 26344 15373 17460 28446 33820

26.46718 26.56677 25.41 25.41 30.2 29.5 29.5

0.934273 0.934049 0.94 0.94 1 0.93 0.93

0.500852 0.533550 0.629237 0.367190 0.527292 0.780416 0.927852

42620 44800 44750 43330 40190 31000 47310 46050 40190 41868

19.67 18.52 19.6 19.81 20.73 27.5 17.01 18.02 19.60 15.17

1 1 0.98 1 1 0.98 1 1 1 1

0.838335 0.829696 0.859558 0.858367 0.833139 0.835450 0.804743 0.829821 0.787724 0.635138

38931 17385.4 15890

15.17 13.44 12

1 1 1

0.590583 0.233660 0.190680

Source: Department of Climate Change Response, National Development and Reform Commission. (2014). 2005 China Greenhouse Gas Inventory. Beijing: China Environment Press. (In Chinese).

Y. Shi et al. / Journal of Cleaner Production 228 (2019) 1552e1563

The calculated results show that the power emission coefficient and thermal emission coefficient of Shanghai in 2009 are 0.03919 tC/MkJ and 2.32858 tC/MW ▪ h, respectively. Second, carbon emissions from electricity consumption and heat consumption are calculated. The formula is as follows: Cec ¼ Ec  Pd

(5)

Chc ¼ Hc  Td

(6)

where Cec denotes the carbon emissions from electricity consumption, Ec represents the electricity consumption, Chc denotes the carbon emissions from heat consumption, and Hc represents the heat consumption.

3.3. Determination of carbon emission coefficient of industrial processes and products To best represent the actual situation in Shanghai, in the calculation of carbon emissions from industrial processes, the cement industry, iron industry and steel industry are considered. The carbon emission coefficients of the cement, iron, and steel industries were determined to be 0.106364, 0.011736, and 0.038522, respectively. The carbon emissions of the cement, iron and steel industries in Shanghai in 2009 were 80.22 million t, 209,800 t and 89,900 t, respectively. There are two decomposition schemes for energy carbon emissions based on the energy statistics table: industry decomposition scheme and sector decomposition scheme for energy carbon emissions. In this paper, the industrial decomposition scheme of energy carbon emissions and industrial process carbon emissions is first implemented on various urban land use patches according to industrial land use types, and then, the carbon emissions of land use, energy and industrial processes are summarized according to districts and counties. Second, the sectoral decomposition scheme of energy carbon emissions is implemented on land use patches and summarized according to the division of urban land spatial patterns. In addition, ArcGIS10.2.2 software (Environment System Research Institute, California, USA) is used to create Thiessen polygons to decompose the land of public facilities and land for commercial services that lack the spatial attributes of urban and rural areas. Finally, the analysis results are visualized.

1559

4. Result analysis 4.1. Disparity of the carbon emissions of various social civilization forms The estimated results show that the carbon emission space includes the industrial development subspace (industrial civilization form, ICF), urban construction subspace (urban civilization form, UCF), rural living subspace (rural civilization form, RCF) and other subspaces (public civilization form, PCF) (Table 9). The total carbon emission of Shanghai in 2009 was 73.654 million tons, of which the ICF was the highest contributor, with 55.613 million tons, accounting for 75.51%; the UCF ranked second, with 10.799 million tons, accounting for 14.66%. Together, ICF and UCF accounted for approximately 90.17% of the total carbon emissions, suggesting that they were major contributors to carbon emissions. Together, the RCF and PCF accounted for only 12.19% of the total carbon emissions. In terms of social civilization form, the emphasis of carbon emission reduction should be placed on the ICF and UCF. 4.2. Disparity of the carbon sink of various social civilization forms In terms of land use space, carbon sink space includes agricultural production subspace (agricultural civilization form, ACF), green ecological subspace (ecological civilization form, ECF) and other ecological subspaces (wilderness form, WF). Table 9 shows that the total carbon sink of Shanghai in 2009 was 1.739 million tons, of which the ACF and ECF were major contributors to the carbon sink, which together accounted for approximately 99% of the total carbon sink. The amount of carbon sequestration in the WF was negligible. Therefore, the priority space of the increasing carbon sink should be placed on the ACF and ECF. 4.3. District differences in carbon emissions and carbon sinks From the perspective of carbon emission of land use space, Shanghai not only has obvious district differences but also has distinct characteristics of high emissions in inner suburbs, secondary emissions in outer suburbs and low emissions in central urban areas. In terms of individual districts and counties, Pudong new area was the highest, with 16.852 million tons accounting for 22.88% of the city's total carbon emissions; Jiading, Baoshan, Minhang and Songjiang all exceeded 7 million tons; Fengxian district emitted 6.802 million tons; Qingpu and Jinshan all exceeded 5

Table 9 Carbon emissions of various social civilization forms in Shanghai in 2009 (Unit: ten thousand t). Urban subspaces

Urban construction Industrial development Agricultural subspace subspace production subspace

Rural living subspace

Green ecological Other ecological Other subspace subspace subspace

Social civilization forms Shanghai Central city Pudong new area Minhang district Jiading district Baoshan district Chongming county Fengxian district Jinshan district Songjiang district Qingpu district

Urban civilization form 1079.9 216.9 272.0 133.7 76.0 85.2 32.7 49.2 45.0 104.3 64.8

Rural civilization form 468.1 2.8 112.7 18.8 53.1 13.4 94.4 60.3 40.0 28.3 44.4

Ecological Wilderness civilization form form
81.5
1.7
1.2
0.2
14.2
0.4
2.9
0.1
3.9
0.1
2.6
0.1
28.0
0.3
7.2
0.1
5.8
0.1
7.7
0.1
8.0
0.1

Industrial civilization form 5561.3 310.6 1215.2 542.1 629.2 623.3 239.9 560.6 428.2 560.2 452.0

Agricultural civilization form
90.7 0.0
14.5
1.9
5.2
1.5
29.2
11.1
11.8
7.7
7.8

Total

Public civilization / form 430.0 7365.4 75.8 604.7 114.5 1685.2 44.3 734.0 39.7 788.8 29.0 746.7 16.8 326.3 28.5 680.2 19.7 515.1 33.7 711.0 28.0 573.3

Notes: Land types of other ecological space include saline-alkali land, idle land, sand land, bare land and other natural spaces. Its carbon emission coefficient is estimated by half of the grassland (
0.24637 t/a˖hm2).

1560

Y. Shi et al. / Journal of Cleaner Production 228 (2019) 1552e1563

million tons; and the entire central city combined had only 6.047 million tons (Table 9). The net carbon emissions of the four inner suburbs (Pudong new area and Jiading, Baoshan and Minhang districts) are 39.548 million tons, which accounts for 53.69% of the city's total carbon emissions. The net carbon emissions of the five outer suburbs (Songjiang, Qingpu, Jinshan and Fengxian districts and Chongming County) is 28.059 million tons, which accounts for 38.1% of the total carbon emissions of the entire city. The net carbon emissions of the central city account for only 8.21% of the total carbon emissions of the entire city. Over approximately the past 20 years, Shanghai's population and manufacturing industries have moved and concentrated mainly in the inner suburbs, which has become a major area of carbon emissions. The central city is mainly the developing service industry, so its intensity and total amount of carbon emissions are relatively low. In terms of carbon sink of land use space, there are also obvious district differences in Shanghai, but its spatial differentiation characteristics are different from carbon emissions, showing that the outer suburbs are high, the inner suburbs are second, and the central urban area is the lowest. Table 9 shows that Chongming County was the highest, with 0.575 million tons accounting for 33.06% of the city's total carbon sink; and Pudong new area was second, with 0.291 million tons accounting for 16.73% of the city's total carbon sink. Fengxian, Jinshan, Qingpu and Songjiang districts ranged from 0.155 to 0.184 million tons. Jiading district was 0.092 million tons, and both Baoshan and Minhang had less than 0.05 million tons. The entire central city combined had only 0.014 million tons, where the carbon sink is scarcest. 4.4. Spatial variation in carbon emission and carbon sink intensity The average carbon emission intensity of various urban subspaces is also calculated. The results show the following (Table 10): The carbon emission intensity of the industrial development subspace is the highest, up to 676.9 t/hm2; second, the urban construction subspace is 150.3 t/hm2. The carbon emission intensity of rural living subspace ranks third at 83.9 t/hm2; other subspace ranks fourth at 63.5 t/hm2. In other words, in Shanghai, the carbon emission intensity of the ICF is 4.5 times that of the UCF, 8 times that of the RCF and 10 times that of the PCF. The carbon intensity of the UCF is approximately 1.8 times that of the RCF and 2.4 times that of the PCF. The carbon emission intensity of the RCF is 1.3 times that of the PCF. This analysis is consistent with the results of the carbon emission total analysis. The estimated results also show that the carbon emission intensity of the agricultural production subspace is
4.289 t/hm2, the green ecological subspace is
2.619 t/hm2, and the other ecological subspace is
0.2464 t/hm2 (Table 10). In other words, in Shanghai, the carbon sink capacity per unit area of the ACF is 1.6 times that of the ECF and 17.4 times that of the WF. The carbon sink capacity per unit area of the ECF is approximately 10.6 times that of the WF. Therefore, the carbon sink capacity per unit area of the ACF is higher than that of the ECF, and that of the ECF is higher than that of the WF.

4.5. Comparison with the results of other related studies The previous research on Shanghai's carbon emissions mainly focused on a single type, such as carbon emissions from different industries (Shao et al., 2010; Shi et al., 2016), agricultural carbon emissions (Zhang, 2017), transport carbon emissions (Zhu and Shao, 2014; Wei and Pan, 2017; Sun et al., 2017), and carbon emissions of residential areas (Zhang et al., 2014; Wang and Li, 2017). However, their results cannot be directly compared with ours due to the different perspectives and time of estimation. In general, in the estimation of industrial carbon emissions, the differences between industrial processes and products are not taken into account (Shao et al., 2010); the role of agricultural carbon sink is ignored in the estimation of agricultural carbon emissions (Zhang, 2017); and in the estimation of residential carbon emissions, there is no difference between urban residents and rural residents (Zhang et al., 2014). Additionally, Tian et al. (2017) evaluated the effect of carbon tax rates on the sectoral output and competitiveness in Shanghai. Xuan (2018) estimated the urban low-carbon economic development level based on the matterelement extension model and concluded that Shanghai was a city with a low-carbon level. However, in another study, Cai et al. (2017) analysed the regional differences in total carbon emissions among cities and the relationship between per capita CO2 emissions and urban functional types, and concluded that Shanghai was a city with higher CO2 emissions in China. Obviously, their conclusions were contradictory. Cai et al.'s conclusion may be relatively more credible. In Shanghai, there is little research on carbon emissions based on land use (Shan et al., 2011; Feng, 2013; Guo, 2016). The research scope of Shan et al. (2011) was Zhangjiang High-tech Park rather than the whole city of Shanghai, and the land use data were derived from the interpretation of remote sensing images; thus, their research results cannot be directly compared with ours. The results of the other two studies are compared and analysed with our results below. In the book “The Theory and Practice of Land Use Planning in Mega-cities in the Transitional Period”, Feng, (2013) used sectoral estimation methods to calculate the carbon source and carbon sink of land use in Shanghai. Their calculations showed that in Shanghai, carbon sources from the secondary industry were the highest in 2009, followed by those from the tertiary industry, and those from the primary industry were the lowest; in terms of regional distribution, Pudong new area is the highest, followed by Minhang, Songjiang and Jiading districts. In 2009, soil carbon sequestration in Shanghai was the largest, followed by wetland carbon sequestration and agricultural crop carbon sequestration, and forest carbon sequestration was the lowest; in terms of regional distribution, Chongming County was the highest, followed by Jinshan, Qingpu, Songjiang and Fengxian districts. In general, the above calculation results are basically consistent with the estimation results in this article. The main differences between the two are as follows: (1) Estimations in this paper take into account finer spatial heterogeneity, including two levels of both district (county) and land use

Table 10 Carbon emission intensity of various social civilization forms in Shanghai in 2009. Urban subspaces

Social civilization forms

Urban construction Industrial subspace development subspace Urban civilization Industrial civilization form form

Carbon emission 150.3 intensity (t/hm2)

676.9

Agricultural production Rural living subspace Green ecological subspace subspace Agricultural civilization Rural civilization form form
4.289

83.9

Other ecological subspace

Other subspace

Ecological civilization Wilderness form Public form civilization form
2.619
0.2464 63.5

Y. Shi et al. / Journal of Cleaner Production 228 (2019) 1552e1563

patches. (2) The carbon sink of Pudong new area is higher in our estimations, ranking second in Shanghai, which is more in line with the reality of Shanghai. The types of ecological resources in Shanghai are divided into four categories: green land, garden land and forest, arable land and wetland. According to statistics from the Shanghai Municipal Planning and Land & Resources Administration, the scale of land use for ecological resources was 4057 km2 in 2008, accounting for 59.8% of the land area of Shanghai, which was concentrated in Chongming County, Pudong new area, and Qingpu, Songjiang, Jinshan, and Fengxian districts. The land use of ecological resources in 6 districts (county) accounted for 88.7% of that in the whole city. Chongming County and Pudong new area accounted for 29.2% and 17.8% of the city's ecological land use, respectively. Therefore, there is a close relationship between carbon sink and ecological land use. (3) Our estimations also take into consideration the spatial differences in carbon emission intensity but not spatial differences in per capita carbon emissions. In his master's thesis, Guo (2016) analysed the relationship between carbon emissions and land use. He pointed out that (1) among the carbon emissions of terminal energy consumption and power in Shanghai, the industrial sector contributed the most, accounting for 57.91%. As a result, the total carbon emissions of industrial and mining land were the highest among all land types, while the carbon emissions of transportation land grew the fastest, and transportation land was also the land type with the highest carbon intensity per unit area. Therefore, industrial and transportation lands will be the entry point of carbon emission reduction in the future. (2) The agricultural land area in Shanghai continued to shrink, but it was still the main land type of the carbon sink. The carbon sink function of urban green space increased rapidly, but the carbon absorption effect was still weak due to the small size. (3) From the perspective of watershed, Shanghai has become a highintensity carbon emission point source, which brings great pressure to the surrounding ecological environment. (4) The carbon emission increasing effect of economic development and construction land expansion is significant. Overall, our estimates are similar to Guo's. In our estimation, the differences are as follows: (1) The carbon emission intensity in UCF, ranking second in Shanghai, is larger than that in PCF (including transportation land); (2) In Shanghai, the carbon sink in ECF in 2009 was 0.815 million tons, accounting for 46.87% of the total carbon sink, which is slightly less than the 0.907 million tons in ACF. Comparatively, our estimates are more realistic and more credible. 5. Discussion From the perspective of cultural factors, the study of contemporary social civilization forms focuses on the relationship between culture and civilization and the establishment of a new civilization model. This study expands the traditional research field and puts forward a new perspective to understand the forms of social civilization from the functional types of land use. This study not only refines the understanding of the spatial differences in social civilization forms but also points out the direction for the establishment of a new civilization model. In other words, by optimizing the spatial structure and changing the way of land use, a better form of social civilization can be expanded and cultural conflicts can be avoided at the same time. The social civilization forms based on land use functional types are not fixed but can be transformed dynamically. From the perspective of carbon cycle, the transition from industrial civilization to ecological civilization, from wilderness civilization to agricultural civilization or ecological civilization, from rural civilization to agricultural civilization or ecological civilization, from public civilization to ecological civilization, and from urban civilization to ecological civilization is conducive to the

1561

reduction in carbon emission intensity, which is conducive to the establishment of sustainable development models. In a sense, the construction of low-carbon production mode and lifestyle depends on the combination of urban and rural land use modes and industrial growth modes aiming at low-carbon development. Therefore, this study has important theoretical and practical significance for the transformation of social civilization and the establishment of ecological civilization values. Adhering to the path of ecological civilization and green development should be a longterm strategy for the sustainable development of human society. Industrial civilization is an important form of social civilization in the history of human society. There have always been two different views on industrial civilization: optimism and pessimism (Kassiola, 1990). In fact, both views are one-sided. Although development of industrial civilization has led to a serious environmental crisis and trust crisis, it still has three obvious advantages: First, the new employment capacity created by industrial civilization has effectively alleviated the employment difficulty caused by rapid population growth. Second, industrial civilization has accelerated technological innovation and progress, effectively enhancing the carrying capacity of urban land space. Third, the agglomeration economy and scale economy formed by industrial civilization have effectively improved the utilization efficiency of resources and elements. Therefore, ignoring the advantages of industrial civilization and overstating its harm is unfair. In recent years, the return of developed countries from “deindustrialization” to “reindustrialization” is enough to prove that the new industrial civilization in the post-industrialization era is still indispensable. Therefore, in Shanghai's Overall Urban Planning (2017e2035), the planned area for industrial and warehouse land will be kept between 320 and 480 km2, accounting for 10e15% of the planned construction land. Of course, in the era of ecological civilization, it is necessary to attach importance to the interactive adjustment and optimization of the energy structure, industrial structure and land use structure, support the development of green energy, encourage the development of low-carbon industry, and build an environment-friendly land use mode. Additionally, it is important to improve labour productivity, continually reduce carbon emission intensity, support research and development of low-carbon technologies, strive to raise the level of technology and energy efficiency, and take a new road to industrialization that is intensive, green, smart and low-carbon. Based on the data of land use patches, this paper examines the spatial correlation between social civilization forms and carbon emission intensity. This study determines the limitation of assessing the relation between urbanization and the carbon cycle according to the stage of development and income level and overcomes the restraint from statistical data and remote sensing image data to refine the research scale and increase the credibility of the study's results. Carbon emission research based on statistical data is not conducive to the spatial decomposition of total carbon emissions, and the regional differences in carbon emissions within statistical units cannot be obtained. The research results based on remote sensing data may have higher errors. However, this paper does not conduct a comprehensive spatio-temporal dynamic investigation on the relationship between the social civilization forms and carbon emission intensity, and further work can be carried out to analyse the dynamic changes in different time series to better reveal the evolution characteristics of temporal-spatial interaction between the two. 6. Conclusions and policy implications Previous studies mostly focused on the relationship between the spatial difference in urban development stages or income levels

1562

Y. Shi et al. / Journal of Cleaner Production 228 (2019) 1552e1563

and carbon emissions on a macro or meso-scale, but seldom examined the spatial correlation between the social civilization forms and carbon emission intensity. Based on the land use patches data, this article estimated the spatial differences in the carbon emission intensity of different social civilization forms (or heterogeneous functional types of land use) on the urban scale. The estimated results integrate the micro-scale (land use patches) with the meso-scale (heterogeneous urban functional areas) to reveal more detailed spatial differences in the carbon emission intensity of different social civilization forms. From the point of view of carbon cycling, in the process of industrialization and urbanization, the transformation from an agricultural civilization to an industrial civilization and from a rural civilization to an urban civilization is the process of increasing carbon emissions. The transformation of human society from barbarism to an agricultural civilization and from an industrial civilization to an ecological civilization is the process of carbon emission reduction. Therefore, one should advocate for the ecological civilization, promote the agricultural civilization, and preserve historical, cultural and natural landscapes with a more long-term strategic vision. It must be noted that in recent years, Shanghai has actively implemented the reduction in construction land and increased ecological land use, which is indeed an effective strategy to reduce carbon emissions and increase the carbon sink. In the Shanghai metropolitan area, the regional disparity of carbon source and sink is significant. Because different districts and counties are at different stages of industrialization and urbanization, their functional orientation, economic development level, industrial structure and land use structure are greatly different. Therefore, in the process of Shanghai's low-carbon city development, one should pay attention to the implementation of differentiated policies of reducing sources and increasing sinks. For example, in the ICF, efforts should be made to both reduce inefficient industrial land use and guide industrial concentration in industrial parks and optimize the industrial structure and promote industrial low-carbon development. In the UCF, on the one hand, one should expand the scale of green space and forest land; on the other hand, the green space structure and layout should be optimized to increase the carbon sink. In the ECF, one should strengthen the protection of arable land, wetlands and water bodies, reverse their steady decline, and increase crop and soil carbon sinks. By 2035, Shanghai will basically be an excellent global city, a city of innovation, humanity and ecology, and a modern international metropolis with global influence. To achieve the goal of building a low-carbon economy and a low-carbon society in Shanghai, one needs to reduce the carbon emission space wisely and continue to expand the carbon sink space. Better strategies should be adopted to promote the concentration, clustering and intensive development of industrial civilization and urban civilization, especially optimizing the population and industrial layout of the inner suburbs and to accelerate industrial transformation and upgrading. More effective measures should be taken to reduce the concentration of greenhouse gases emitted by human activities, thus reducing the frequency and harm of various extreme weather events (rainstorms, hurricanes, extreme high temperatures, etc.) caused by global warming. Acknowledgments This paper is funded by the one of key projects of the state key research and development program (2017YFA0603102) and the one of key projects for Shanghai General Land Use Planning Revision (2015(D)-002(F)-11). The authors would like to thank the editor and two anonymous reviewers for their valuable comments and

suggestions. References Baidu, 2018. Social Civilization. June 27th. Available from: https://baike.baidu.com/ item/%E7%.A4%BE%E4%.BC%9A%E6%96%87%E6%98%8E/5930707. In Chinese). Bolin, B., Sukumar, R., 2000. Land Use, Land Use Change and Forestry. Cambridge University Press, Cambridge. Braudel, F., 1995. A History of Civilizations. Translated by Mayne R. Penguin Group, London. Cai, B., Wang, J., Yang, S., Mao, X., Cao, L., 2017. Carbon dioxide emissions from cities in China based on high resolution emission gridded data. Chin. J. Popul. Resour. Environ. 15 (1), 58e70. Chen, H., Jia, B., Lau, S.S.Y., 2008. Sustainable urban form for Chinese compact cities: challenges of a rapid urbanized economy. Habitat Int. 32 (1), 28e40. Churkina, G., 2008. Modeling the carbon cycle of urban systems. Ecol. Model. 216 (2), 107e113. Coevering, P., Schwanen, T., 2006. Re-evaluating the impact of urban form on travel patterns in Europe and North-America. Transport Pol. 13, 229e239. Dong, K., Jiang, H., Sun, R., Dong, X., 2019. Driving forces and mitigation potential of global CO2 emissions from 1980 through 2030: evidence from countries with different income levels. Sci. Total Environ. 649, 335e343. Dong, K., Sun, R., Hochman, G., Li, H., 2018. Energy intensity and energy conservation potential in China: a regional comparison perspective. Energy 155, 782e795. Duan, X., Wang, X., Lu, F., Ouyang, Z., 2008. Carbon sequestration and its potential by wetland ecosystems in China. Acta Ecol. Sin. 28 (2), 463e469 (In Chinese). Editor in Chief Feng, J., 2013. The Theory and Practice of Land Use Planning in MegaCities in the Transitional Period. Tongji University Press, Shanghai (In Chinese). Fry, J., Lenzen, M., Jin, Y., Wakiyama, T., Baynes, T., Wiedmann, T., Malik, A., Chen, G., Wang, Y., Geschke, A., 2018. Assessing carbon footprints of cities under limited information. J. Clean. Prod. 176, 1254e1270. Gabitov, T., Alikbayeva, M., Omirbekova, A., Aljanova, N., Saparova, D., 2018. Sustainable development as a model and artifact of modern civilization. Eur. J. Sustain. Dev. 7 (4), 253e261. Glaeser, E.L., Kahn, M.E., 2010. The greenness of cities: carbon dioxide emissions and urban development. J. Urban Econ. 67, 404e418. Graaf, R.J., Wann, D., Naylor, T.H., 2014. Affluenza: How Overconsumption Is Killing Us and How to Fight Back, third ed. Berrett-koehler Publishers, Oakland, California. Guan, D., Chen, Y., Huang, F., 1998. The storage and distribution of carbon in urban vegetation and its roles in balance of carbon and oxygen in Guangzhou. China Environ. Sci. 18 (5), 437e441 (In Chinese). Guo, Z., 2016. Measurement and Trend Predict of Carbon Emission Effect of Land Use in Shanghai. Master's Degree Thesis of Hefei University of Technology. Hefei: Anhui, P. R. China. Houghton, R.A., Skole, D.L., Lefkowitz, D.S., 1991. Changes in the landscape of Latin America between 1850 and 1985 (II). Net release of CO2 to the atmosphere. For. Ecol. Manag. 38, 173e199. Houghton, R.A., Hackler, J.L., Lawrence, K.T., 1999a. The U.S. Carbon budget: contributions from land-use change. Science 285 (5427), 574e578. Houghton, R.A., Hackler, J.L., 1999b. Emissions of carbon from forestry and land-use change in tropical Asia. Glob. Chang. Biol. 5 (4), 481e492. Houghton, R.A., Magnitude, 2002. Distribution and causes of terrestrial carbon sinks and some implications for policy. Clim. Policy 2 (1), 71e88. Howard, D.M., Howard, P.J.A., Howard, D.C., 1995. A Markov model projection of soil organic carbon stores following land use changes. J. Environ. Manag. 45, 287e302. Huntington, S.P., 1997. The Clash of Civilizations and the Remaking of World Order. Simon & Schuster, New York. Huo, T.F., Cai, W.G., Ren, H., Feng, W., Zhu, M.L., Lang, N.N., Gao, J.X., 2019. China's building stock estimation and energy intensity analysis. J. Clean. Prod. 207 (1), 801e813. Huo, T.F., Ren, H., Zhang, X.L., Cai, W.G., Feng, W., Zhou, N., Wang, X., 2018. China's energy consumption in the building sector: a Statistical Yearbook-Energy Balance Sheet based splitting method. J. Clean. Prod. 185 (6), 665e679. Kassiola, J.J., 1990. The Death of Industrial Civilization: the Limits to Economic Growth and the Repoliticization of Advanced Industrial Society. State University of New York Press, New York. Liu, Y., Hao, Y., 2018. The dynamic links between CO2 emissions, energy consumption and economic development in the countries along “the Belt and Road”. Sci. Total Environ. 645, 674e683. Ma, X., Wang, C., Dong, B., Gu, G., Chen, R., Li, Y., Zou, H., Zhang, W., Li, Q., 2019. Carbon emissions from energy consumption in China: its measurement and driving factors. Sci. Total Environ. 648, 1411e1420. Mardani, A., Streimikiene, D., Cavallaro, F., Loganathan, N., Khoshnoudi, M., 2019. Carbon dioxide (CO2) emissions and economic growth: a systematic review of two decades of research from 1995 to 2017. Sci. Total Environ. 649, 31e49. Mei, y., Chen, Y., 2017. Study on Evaluation and Control of Low-Carbon Intensive Utilization of Urban Land. Science Press, Beijing (In Chinese). Miles, L., Kapos, V., 2008. Reducing greenhouse gas emissions from deforestation and forest degradation: global land-use implications. Science 320 (5882), 1454e1455. November 13 Ministry of Land, Resources of the People’s Republic of China, 2017.

Y. Shi et al. / Journal of Cleaner Production 228 (2019) 1552e1563 Current Land Use Condition Classification for Standard (GB/T21010-2017). 201711-13 (In Chinese). http://www.tdzyw.com/2017/1113/45597.html. Morris, I., 2013. The Measure of Civilization: How Social Development Decides the Fate of Nations? Princeton University Press, New Jersey. Newman, P.W.G., Kenworthy, J.R., 1989. Gasoline consumption and cities: a comparison of U.S. cities with a global survey. J. Am. Plan. Assoc. 55 (1), 24e37. Ni, J., 2013. Carbon storage in Chinese terrestrial ecosystems: approaching a more accurate estimate. Clim. Change 119, 905e917. Pao, H.T., Chen, C.C., 2019. Decoupling strategies: CO2 emissions, energy resources, and economic growth in the Group of Twenty. J. Clean. Prod. 206, 907e919. Qi, Y., 2011. 2010 China Low-Carbon Development Report0. Science Press, Beijing (In Chinese). Quigley, C., 1979. The Evolution of Civilization: an Introduction to Historical Analysis. Liberty Fund, Inc, Carmel. Saidi, K., Hammami, S., 2015. The impact of CO2 emissions and economic growth on energy consumption in 58 countries. Energy Rep. 1, 62e70. Sakaeva, L.R., Sabirova, D.R., Yahin, M.A., Kuznetsova, E.V., 2018. Culture and civilization: functional and methodological aspects. Rev. Publicando 5 (16), 435e442 (Subcollection 1). Scott, N.A., Tate, K.R., 1999. Soil carbon storage in plantation forests and pastures: land-use change implication. Tellus 51, 326e335. Shan, F., Yu, J., Zhao, J., Qian, G., 2011. Land use change in the suburb area and its impacts on carbon emissions in the process of industrialization: a case study of Zhangjiang Hi-Tech Park, Shanghai. Resour. Sci. 33 (8), 1600e1607 (In Chinese). Shao, S., Yang, L., Cao, J., 2010. Study on influencing factors of CO2 emissions from industrial energy consumption: an empirical analysis based on STIRPAT model and industrial sectors' dynamic panel data in Shanghai. J. Financ. Econ. 36 (11), 16e27 (In Chinese). Shen, L., Wu, Y., Lou, Y., Zeng, D., Shuai, C., Song, X., 2018. What drives the carbon emission in the Chinese cities? A case of pilot low carbon city of Beijing. J. Clean. Prod. 174, 343e354. Shi, Y., Shi, S., Wang, H., 2019. Reconsideration of the methodology for estimation of land population carrying capacity in Shanghai metropolis. Sci. Total Environ. 2019 (652), 367e381. Shi, Y., Tang, Q., Tian, L., 2016. Analysis of changing characteristics and impacting factors of industrial carbon emissions of Shanghai development zone, 2016 China Sci. Pap. 11 (9), 961e969 (In Chinese). Shuai, C., Chen, X., Wu, Y., Zhang, Y., Tan, Y., 2019. A three-step strategy for decoupling economic growth from carbon emission: empirical evidences from 133 countries. Sci. Total Environ. 646, 524e543. Sun, C., Jin, N., Zhang, X., Du, H., 2013. The impact of urbanization on the co2 emission in the various development stages. Sci. Geogr. Sin. 33 (3), 266e272 (In Chinese). Sun, G., 2014. Global Development. Guizhou People's Publishing House, Guiyang (In Chinese). Sun, J., Zhang, Y., Xue, R., Zhang, Y., 2017. Formation mechanism of urban traffic

1563

carbon emissions based on mobile monitoring: case study of Shanghai. China J. Highw. Transp. 30 (5), 123e132 (In Chinese). The Department of Climate Change Response of the National Development and Reform Commission, 2014. 2005 China Greenhouse Gas Inventory Study. China Environmental Science Press, Beijing (In Chinese). Tian, X., Dai, H., Geng, Y., Huang, Z., Masui, T., Fujita, T., 2017. The effects of carbon reduction on sectoral competitiveness in China: a case of Shanghai. Appl. Energy 197, 270e278. Toynbee, A.J., 1987. A Study of History, Revised ed. edition. Oxford University Press, London. Wang, K., Zhou, J., 2014. Guidelines for the Preparation of Greenhouse Gas Inventories in Chinese Cities. China Environmental Science Press, Beijing (In Chinese). Wang, Q.Y., 2009. China's end-use energy consumption and energy efficiency in 2007. Energy Conserv. Environ. Protect. (2), 14e17 (In Chinese). Wang, S., Li, G., Fang, C., 2018. Urbanization, economic growth, energy consumption, and CO2 emissions: empirical evidence from countries with different income levels. Renew. Sustain. Energy Rev. 81, 2144e2159. Wang, W., Li, J., 2017. Correlation between residential and carbon emissions caused by household energy consumption: a case study on Caoyang Xincun. Shanghai Urban Plann. Rev. 41 (6), 83e91 (In Chinese). Wei, P., Pan, H., 2017. Research on individual carbon dioxide emissions of commuting in peri-urban area of metropolitan citiesdan empirical study in Shanghai. Transport. Res. Procedia 25, 3459e3478. Wikipedia, June 27th 2018. Civilization. Available from: https://en.wikipedia.org/ wiki/Civilization. Xuan, Y., 2018. Investigating low-carbon city: empirical study of Shanghai. Sustainability 10 (4), 1054. York, R., Rosa, E.A., Dietz, T., 2003. STIRPAT, IPAT and ImPACT: analytic tools for unpacking the driving forces of environmental impacts. Ecol. Econ. 46, 351e365. Yu, Z., Lu, C., Cao, P., Tian, H., 2018. Long-term terrestrial carbon dynamics in the Midwestern United States during 1850e2015: roles of land use and cover change and agricultural management. Glob. Chang. Biol. 24, 2673e2690. Zhang, G., Li, L., Huang, H., Chen, M., Chen, C., Zhou, N., 2014. Carbon emissions of the household living in Shanghai using Urban-RAM model. Acta Sci. Circumstantiae 34 (2), 457e465 (In Chinese). Zhang, R., Matsushima, K., Kobayashi, K., 2018. Can land use planning help mitigate transport-related carbon emissions? A case of Changzhou. Land Use Pol. 74, 32e40. Zhang, Z., 2017. Empirical analysis of agricultural economics and agricultural emissions: a case study of Shanghai suburb agriculture. Ecol. Econ. 33 (10), 29e33 (In Chinese). Zhu, H., Shao, D., 2014. Shanghai urban transport carbon emissions assessment and control strategy study. Shanghai Urban Plann. Rev. 115 (2), 33e36 (In Chinese).