Abstract
As the centre of human activity and being under the threat of climate change, cities are considered to be major components in the implementation of climate change mitigation and CO2 emission reduction strategies. Inventories of cities’ emissions serve as the foundation for the analysis of emissions characteristics and policymaking. China is the world’s top energy consumer and CO2 emitter, and it is facing great potential harm from climate change. Consequently, China is taking increasing responsibility in the fight against global climate change. Many energy/emissions control policies have been implemented in China, most of which are designed at the national level. However, cities are at different stages of industrialization and have distinct development pathways; they need specific control policies designed based on their current emissions characteristics. This study is the first to construct emissions inventories for 182 Chinese cities. The inventories are constructed using 17 fossil fuels and 47 socioeconomic sectors. These city-level emissions inventories have a scope and format consistent with China’s national/provincial inventories. Some socioeconomic data of the cities, such as GDP, population, industrial structures, are included in the datasets as well. The dataset provides transparent, accurate, complete, comparable, and verifiable data support for further city-level emissions studies and low-carbon/sustainable development policy design. The dataset also offers insights for other countries by providing an emissions accounting method with limited data.
Subject terms: Climate-change policy, Climate-change mitigation, Environmental economics
Background & Summary
Cities are considered to be major components in the implementation of climate change mitigation and CO2 emission reduction strategies1. Although a mention of “city” is lacking in the Paris Agreement or the Sustainable Development Goals, as all submissions focused on the national level, climate change actions should be taken at the city level2.
Cities are the basic units for human activity3 and the main consumers of energy and emitters of CO2 throughout the world4,5. The CO2 emissions from energy use in cities will grow by 1.8% per year between 2006 and 2030, with the share of global CO2 emissions rising from 71 to 76%6. In China, urban energy use accounts for 85% of total emissions, which is higher than its share in the USA (80%) or Europe (69%)7,8. The high energy demand and high CO2 emissions of cities not only increase climate change concerns and environmental pressure but also increase residents’ health problems through air pollution9. Both coastal and interior cities are facing danger from extreme weather, geological hazards, urban waterlogging, etc. Thus, cities are motivated to fight against climate change.
Although climate policies are usually designed at the national level, they are implemented at the city level. Without support from local city governments, national policies cannot be effectively executed. Considering that cities have different natural resource endowments and development pathways, each should have specific emission reduction actions that are designed based on that city’s unique emission characteristics. In China, this is particularly true. There are over 330 cities in China, and they are at different stages of industrialization, with distinct development pathways. Therefore, cities are the key components in climate change policymaking, and many low-carbon projects and actions have been taken at the city level, such as the Local Governments for Sustainability (ICLEI) and the C40 Cities Climate Leadership Group (C40).
Understanding the emissions characteristics of cities is the foundation of any further city-level climate change actions. Compared to studies focused on national and provincial emissions accounts, far fewer have focused on city-level emissions, and those that do have methods limitations and geographical restrictions.
First, previous studies on city-level emissions have severe methodological weaknesses and limitations. Most previous city-level greenhouse gas emissions inventories were calculated using a bottom-up approach, i.e., by using energy consumption data from surveys of several sectors10–12. The sectors were set differently between studies, making the cities’ CO2 emissions inconsistent and not comparable across studies, as well as inconsistent with the national and regional emission inventories. In addition, some studies used spatial and geographical analysis13,14, night-time light imagery15,16, or economic models17,18 to account for city emissions. These models can only estimate the overall CO2 emissions of a city. They cannot exactly determine the contributions of emission sources (i.e., energy types or socioeconomic sectors).
Second, most of the previous studies on city-level emissions focused on megacities from developed countries with consistent and transparent data sources, especially US cities19–23. Currently, city-level emissions are being studied from a more international perspective by analysing more global cities, especially cities from developing countries24–30. Restricted by data availability, the CO2 emissions from Chinese cities are far behind in their documentation. Sugar, et al.31 reported emissions for Beijing, Tianjin, and Shanghai in 2006 and compared the three cities’ emissions with those of ten other global cities. Wang, et al.10 discussed the CO2 emissions from 12 Chinese megacities, most of which are provincial capital cities. Dhakal8 examined the energy consumption and CO2 emissions of all Chinese provincial cities. Zhou, et al.32 and Xu, et al.33 account for the CO2 emissions of specific city clusters, such as the Guangdong Bay cities and cities in the central plain. Ramaswami, et al.34 in the cited study and a follow-up study developed a comprehensive emission database including the scope 1 and scope 2 CO2 emissions of 233 prefecture-level and 637 county-level cities in China35.
Thus, the previous assessments of city-level emissions either focused on total emissions (or combined emissions for several sectors) or on megacities with consistent and systematic energy statistics. Previous analyses of the bottom-up sector-based emissions of cities are inconsistent with national and regional emission inventories, making multi-scale emission studies unavailable. Additionally, such general emission data cannot support detailed city-level emission analysis and related emission reduction policy making.
The dataset in this study provides detailed emissions inventories for 182 Chinese cities. The inventories are constructed for 17 types of fossil fuel and 47 socioeconomic sectors that are consistent with the System of National Accounts. Additional socioeconomic indexes for the cities are included in the dataset. The dataset has been re-used in our latest study1 and will facilitate further city-level emissions studies and low-carbon/sustainable development policy design.
Methods
City boundaries and emission scopes
This dataset provides the emissions and socioeconomic inventories of 182 Chinese cities; these cities cover 82% (33,880 billion yuan) of the country’s GDP (41,303 billion yuan), 64% (860 million) of the population (1,341 million), and 35% (3.4 million km2) of the land area (9.6 million km2) in 201036. Most of the studied cities are located east of the Heihe-Tengchong line, where 96% of China’s population lives on 43% of the land. The 182 cities are selected based on data availability.
The term ‘city’ here refers to administrative prefecture-level city rather than to a built-up city. Accordingly, the CO2 emissions calculated in this dataset are Intergovernmental Panel on Climate Change (IPCC) administrative territorial CO2 emissions, referring to emissions “taking place within national (including administered) territories and offshore areas over which the country has jurisdiction (page overview.5)”37. We exclude the emissions induced by international aviation and shipping38. Unlike production- or consumption-based emissions17, the administrative territorial scope quantifies the direct emissions induced by human activities within a regional boundary. That is, territorial emissions provide the data baseline for emission-related studies and regional carbon control.
The emission inventories include two components: CO2 emitted from fossil fuel combustion (energy-related emissions) and CO2 emitted from industrial production (process-related emissions). Process-related emissions refers to CO2 emitted from industrial raw materials during chemical reactions, such as CO2 escaping during calcium carbonate (CaCO3) calcination in cement production.
The cities’ emissions inventories are uniform with China’s national and provincial emission inventories in scope, format, and data sources39, making them comparable.
Emissions calculation and inventory construction
The energy-related emissions are calculated based on 17 fuels (shown in Table 1) and 47 socioeconomic sectors (shown in Table 2). The 17 types of fossil fuels are selected based on China’s official energy statistical system36. There are 29 energy types used in the system: 26 are fossil fuels, one is electricity, one is heat, and one is other energy. As our study only accounts for the direct emissions from fossil fuel burning within one city boundary (the IPCC administrative territorial scope), the inventories exclude the indirect emissions induced by electricity and heat use. The CO2 emissions related to electricity and heat generation, therefore, are calculated based on fuel inputs and allocated to the power plants. We also assume that there is no, or little, CO2 emitted from other energy uses. Some of the fossil fuels share similar carbon content and have very low consumption volumes; we merge them in the emission accounts39. The 47 socioeconomic sectors are set according to the System of National Accounts40.
Table 1. Fossil fuels in the city-level emissions inventories and emissions factors.
No. (i) Unit | Fuels in China’s Energy Statistics | Fuels in this study | NCVi | CCi |
---|---|---|---|---|
PJ/104 tonnes, 108 m3 | tonne C/TJ | |||
1 | Raw coal | Raw coal | 0.21 | 26.32 |
2 | Cleaned coal | Cleaned coal | 0.26 | 26.32 |
3 | Other washed coal | Other washed coal | 0.15 | 26.32 |
4 | Briquettes | Briquette | 0.18 | 26.32 |
Gangue | ||||
5 | Coke | Coke | 0.28 | 31.38 |
6 | Coke oven gas | Coke over gas | 1.61 | 21.49 |
7 | Blast furnace gas | Other gas | 0.83 | 21.49 |
Converter gas | ||||
Other gas | ||||
8 | Other coking products | Other coking products | 0.28 | 27.45 |
9 | Crude Oil | Crude oil | 0.43 | 20.08 |
10 | Gasoline | Gasoline | 0.44 | 18.9 |
11 | Kerosene | Kerosene | 0.44 | 19.6 |
12 | Diesel oil | Diesel oil | 0.43 | 20.2 |
13 | Fuel oil | Fuel oil | 0.43 | 21.1 |
14 | Naphtha | Other petroleum products | 0.51 | 17.2 |
Lubricants | ||||
Paraffin | ||||
White spirit | ||||
Bitumen asphalt | ||||
Petroleum coke | ||||
Other petroleum products | ||||
15 | Liquefied petroleum gas (LPG) | LPG | 0.47 | 20 |
16 | Refinery gas | Refinery gas | 0.43 | 20.2 |
17 | Nature gas | Nature gas | 3.89 | 15.32 |
Table 2. Sectors’ definition of the emission inventories.
No. (j) | Socioeconomic sectors | Category | |
---|---|---|---|
1 | Farming, Forestry, Animal Husbandry, Fishery and Water Conservancy | The primary industry |
|
2 | Coal Mining and Dressing | Energy production | Manufacturing industries |
3 | Petroleum and Natural Gas Extraction | Energy production | |
4 | Ferrous Metals Mining and Dressing | Energy production | |
5 | Nonferrous Metals Mining and Dressing | Energy production | |
6 | Non-metal Minerals Mining and Dressing | Energy production | |
7 | Other Minerals Mining and Dressing | Energy production | |
8 | Logging and Transport of Wood and Bamboo | Light manufacturing | |
9 | Food Processing | Light manufacturing | |
10 | Food Production | Light manufacturing | |
11 | Beverage Production | Light manufacturing | |
12 | Tobacco Processing | Light manufacturing | |
13 | Textile Industry | Light manufacturing | |
14 | Garments and Other Fibre Products | Light manufacturing | |
15 | Leather, Furs, Down and Related Products | Light manufacturing | |
16 | Timber Processing, Bamboo, Cane, Palm Fibre & Straw Products | Light manufacturing | |
17 | Furniture Manufacturing | Light manufacturing | |
18 | Papermaking and Paper Products | Light manufacturing | |
19 | Printing and Record Medium Reproduction | Light manufacturing | |
20 | Cultural, Educational and Sports Articles | Light manufacturing | |
21 | Petroleum Processing and Coking | Energy production | |
22 | Raw Chemical Materials and Chemical Products | Heavy manufacturing | |
23 | Medical and Pharmaceutical Products | Light manufacturing | |
24 | Chemical Fibre | Heavy manufacturing | |
25 | Rubber Products | Heavy manufacturing | |
26 | Plastic Products | Heavy manufacturing | |
27 | Non-metal Mineral Products | Heavy manufacturing | |
28 | Smelting and Pressing of Ferrous Metals | Heavy manufacturing | |
29 | Smelting and Pressing of Nonferrous Metals | Heavy manufacturing | |
30 | Metal Products | Heavy manufacturing | |
31 | Ordinary Machinery | Heavy manufacturing | |
32 | Equipment for Special Purposes | Heavy manufacturing | |
33 | Transportation Equipment manufacturing | Heavy manufacturing | |
34 | Electric Equipment and Machinery | High-tech industry | |
35 | Electronic and Telecommunications Equipment | High-tech industry | |
36 | Instruments, Meters, Cultural and Office Machinery | High-tech industry | |
37 | Other Manufacturing Industry | High-tech industry | |
38 | Scrap and waste | High-tech industry | |
39 | Production and Supply of Electric Power, Steam and Hot Water | Energy production | |
40 | Production and Supply of Gas | Energy production | |
41 | Production and Supply of Tap Water | Heavy manufacturing | |
42 | Construction | Construction |
|
43 | Transportation, Storage, Post and Telecommunication Services | Services sectors |
|
44 | Wholesale, Retail Trade and Catering Services | ||
45 | Other Service Sectors | ||
46 | Urban Resident Energy Usage | Household |
|
47 | Rural Resident Energy Usage |
Energy-related CO2 emissions are calculated based on the mass balance theory;41 see Equation 1.
where CEij represents the CO2 emissions induced by the combustion of fuel i in sector j, ADij (activity data) represents fossil fuel combustion by fuel and sector. The emission factor (ton CO2/ton) is composed of a specific heat value factor- NCVi (J/ton) multiplied by the carbon content per unit heat value-CCi (ton CO2/J) and oxygenation efficiency-Oij (quantified as percentage). Specifically, NCVi refers to the heat value produced per physical unit of fossil fuel i combusted, CCi is the carbon content emitted per unit heat value when combusting per physical unit of fossil fuel i, while Oij stands for the oxidation ratio of the fossil fuel combusted.
The emission factors (NCVi, CCi, and Oij) have been published by international institutions, including the IPCC and the United Nations (UN; governmental agencies in China such as the National Bureau of Statistics of China (NBS) and the National Development and Reform Commission of China (NDRC);42 and previous studies such as the Multi-resolution Emission Inventory for China (MEIC)43, Liu, et al.44. Liu, et al.44 re-evaluated the carbon content of raw coal samples from 4,243 state-owned Chinese coal mines and found that the emission factors for Chinese coal are, on average, 40% lower than the default values recommended by the IPCC. After comparing Liu, et al.44 emissions factors with eight different sources, our previous study finds that Liu, et al.44 emission factors are relatively lower than others (shown in Table 3 (available online only)). The seven sets of emission factors are collected from IPCC, NBS, NDRC, NC1994, NC2005, MEIC, UN-China, and UN-average. Generally, coal-related fuels have a larger range than oil- and gas-related fuels. Liu, et al.44’s re-evaluated emission factors have already been widely used by many studies and institutions to calculate China’s emission inventory, including China’s third official emission inventory 201245 . Thus, this study uses the above-mentioned updated emission factors. Table 1 gives the net caloric value (NCVi) and carbon content (CCi). Table 4 (available online only) shows the sector-specific oxygenation efficiency (Oij), which considers sector discrepancies in technical level39.
Table 3. Fuel’s emission factors from other sources.
IPCC | NBS | NDRC | NC1994 | NC2005 | MEIC | UN-China | UN average | Liu et al.’s nature | ||
---|---|---|---|---|---|---|---|---|---|---|
Net caloric value (PJ/10 thousand tonnes, 100 million cu.m.) | Raw Coal | 0.28 | 0.21 | 0.21 | 0.21 | 0.22 | 0.19 | 0.21 | 0.29 | 0.21 |
Cleaned Coal | 0.27 | 0.26 | 0.23 | 0.24 | 0.23 | 0.26 | 0.21 | 0.29 | 0.26 | |
Other Washed Coal | 0.27 | 0.15 | 0.23 | 0.21 | 0.23 | 0.15 | 0.21 | 0.29 | 0.15 | |
Briquettes | 0.26 | 0.18 | 0.17 | 0.20 | 0.17 | 0.18 | 0.21 | 0.29 | 0.18 | |
Coke | 0.28 | 0.28 | 0.28 | 0.28 | 0.28 | 0.28 | 0.26 | 0.26 | 0.28 | |
Coke Oven Gas | 1.88 | 1.63 | 1.74 | 1.63 | 1.74 | 1.67 | 1.88 | 1.88 | 1.61 | |
Other Gas | 1.88 | 0.84 | 1.58 | 0.84 | 1.58 | 0.52 | 1.88 | 1.88 | 0.83 | |
Other Coking Products | 0.43 | 0.28 | 0.28 | 0.28 | 0.28 | 0.42 | 0.43 | 0.43 | 0.28 | |
Crude Oil | 0.42 | 0.42 | 0.43 | 0.42 | 0.43 | 0.42 | 0.42 | 0.42 | 0.43 | |
Gasoline | 0.44 | 0.43 | 0.45 | 0.45 | 0.45 | 0.43 | 0.45 | 0.45 | 0.44 | |
Kerosene | 0.44 | 0.43 | 0.45 | 0.45 | 0.45 | 0.43 | 0.43 | 0.43 | 0.44 | |
Diesel Oil | 0.43 | 0.43 | 0.43 | 0.45 | 0.43 | 0.43 | 0.42 | 0.42 | 0.43 | |
Fuel Oil | 0.40 | 0.42 | 0.40 | 0.40 | 0.40 | 0.42 | 0.40 | 0.40 | 0.43 | |
LPG | 0.47 | 0.50 | 0.47 | 0.47 | 0.47 | 0.50 | 0.46 | 0.46 | 0.51 | |
Refinery Gas | 0.50 | 0.46 | 0.46 | 0.40 | 0.46 | 0.46 | 0.42 | 0.42 | 0.47 | |
Other Petroleum Products | 0.40 | 0.42 | 0.45 | 0.40 | 0.45 | 0.42 | 0.42 | 0.42 | 0.43 | |
Natural Gas | 3.44 | 3.89 | 3.89 | 3.90 | 3.89 | 3.89 | 3.44 | 3.44 | 3.89 | |
Carbon content (C/TJ) | Raw Coal | 25.80 | 26.37 | 26.37 | 24.26 | 25.83 | 25.80 | 25.80 | 25.80 | 26.32 |
Cleaned Coal | 26.80 | 25.41 | 25.41 | 26.35 | 27.82 | 25.80 | 26.80 | 26.80 | 26.32 | |
Other Washed Coal | 26.80 | 25.41 | 25.41 | 24.26 | 27.82 | 25.80 | 26.80 | 26.80 | 26.32 | |
Briquettes | 25.80 | 33.56 | 33.56 | 24.26 | 33.56 | 25.80 | 25.80 | 25.80 | 26.32 | |
Coke | 29.20 | 29.42 | 29.42 | 29.50 | 28.84 | 25.52 | 29.20 | 29.20 | 31.38 | |
Coke Oven Gas | 12.10 | 13.58 | 13.58 | 20.00 | 14.00 | 15.16 | 12.10 | 12.10 | 21.49 | |
Other Gas | 12.10 | 12.20 | 12.20 | 12.10 | 12.20 | 15.16 | 12.10 | 12.10 | 21.49 | |
Other Coking Products | 25.80 | 29.50 | 29.50 | 25.80 | 20.00 | 19.91 | 25.80 | 25.80 | 27.45 | |
Crude Oil | 20.00 | 20.08 | 20.08 | 20.00 | 20.08 | 19.91 | 20.00 | 20.00 | 20.08 | |
Gasoline | 18.90 | 18.90 | 18.90 | 18.90 | 18.90 | 19.91 | 18.90 | 18.90 | 18.90 | |
Kerosene | 19.50 | 19.60 | 19.60 | 19.60 | 19.60 | 19.91 | 19.50 | 19.50 | 19.60 | |
Diesel Oil | 20.20 | 20.20 | 20.20 | 20.20 | 20.20 | 19.91 | 20.20 | 20.20 | 20.20 | |
Fuel Oil | 21.10 | 21.10 | 21.10 | 21.10 | 21.10 | 19.91 | 21.10 | 21.10 | 21.10 | |
LPG | 17.20 | 17.20 | 17.20 | 17.20 | 17.20 | 19.91 | 17.20 | 17.20 | 17.20 | |
Refinery Gas | 15.70 | 18.20 | 18.20 | 15.70 | 18.20 | 19.91 | 15.70 | 15.70 | 20.00 | |
Other Petroleum Products | 20.00 | 20.00 | 20.00 | 20.00 | 20.00 | 19.91 | 20.00 | 20.00 | 20.20 | |
Natural Gas | 15.30 | 15.32 | 15.32 | 15.30 | 15.32 | 15.16 | 15.30 | 15.30 | 15.32 | |
Oxygenation efficiency | Raw Coal | 0.98 | 0.94 | 0.94 | 0.90 | 0.92 | 1.00 | 1.00 | 1.00 | 0.92 |
Cleaned Coal | 0.98 | 0.98 | 0.98 | 0.90 | 0.92 | 1.00 | 1.00 | 1.00 | 0.92 | |
Other Washed Coal | 0.98 | 0.98 | 0.98 | 0.90 | 0.92 | 1.00 | 1.00 | 1.00 | 0.92 | |
Briquettes | 0.98 | 0.90 | 0.90 | 0.90 | 0.90 | 1.00 | 1.00 | 1.00 | 0.92 | |
Coke | 0.98 | 0.93 | 0.93 | 0.97 | 0.93 | 1.00 | 1.00 | 1.00 | 0.92 | |
Coke Oven Gas | 0.99 | 0.99 | 0.99 | 0.99 | 0.99 | 1.00 | 1.00 | 1.00 | 0.92 | |
Other Gas | 0.99 | 0.99 | 0.99 | 0.99 | 0.99 | 1.00 | 1.00 | 1.00 | 0.92 | |
Other Coking Products | 0.99 | 0.93 | 0.93 | 0.97 | 0.93 | 1.00 | 1.00 | 1.00 | 0.92 | |
Crude Oil | 0.99 | 0.98 | 0.98 | 0.98 | 0.98 | 1.00 | 1.00 | 1.00 | 0.98 | |
Gasoline | 0.99 | 0.98 | 0.98 | 0.98 | 0.98 | 1.00 | 1.00 | 1.00 | 0.98 | |
Kerosene | 0.99 | 0.98 | 0.98 | 0.98 | 0.98 | 1.00 | 1.00 | 1.00 | 0.98 | |
Diesel Oil | 0.99 | 0.98 | 0.98 | 0.98 | 0.98 | 1.00 | 1.00 | 1.00 | 0.98 | |
Fuel Oil | 0.99 | 0.98 | 0.98 | 0.98 | 0.98 | 1.00 | 1.00 | 1.00 | 0.98 | |
LPG | 0.99 | 0.99 | 0.99 | 0.99 | 0.99 | 1.00 | 1.00 | 1.00 | 0.98 | |
Refinery Gas | 0.99 | 0.99 | 0.99 | 0.99 | 0.99 | 1.00 | 1.00 | 1.00 | 0.98 | |
Other Petroleum Products | 0.99 | 0.98 | 0.98 | 0.98 | 0.98 | 1.00 | 1.00 | 1.00 | 0.98 | |
Natural Gas | 0.99 | 0.99 | 0.99 | 0.99 | 0.99 | 1.00 | 1.00 | 1.00 | 0.99 | |
Emission factor (ton CO2/ton) | Raw Coal | 2.61 | 1.90 | 1.90 | 1.67 | 1.94 | 1.80 | 1.98 | 2.77 | 1.83 |
Cleaned Coal | 2.57 | 2.41 | 2.12 | 2.13 | 2.17 | 2.49 | 2.05 | 2.88 | 2.31 | |
Other Washed Coal | 2.57 | 1.41 | 2.12 | 1.66 | 2.17 | 1.46 | 2.05 | 2.88 | 1.33 | |
Briquettes | 2.39 | 1.97 | 1.93 | 1.60 | 1.93 | 1.68 | 1.98 | 2.77 | 1.60 | |
Coke | 2.96 | 2.85 | 2.85 | 2.99 | 2.79 | 2.66 | 2.82 | 2.82 | 2.96 | |
Coke Oven Gas | 8.26 | 8.04 | 8.55 | 11.84 | 8.85 | 9.30 | 8.34 | 8.34 | 11.67 | |
Other Gas | 8.26 | 3.73 | 6.98 | 3.70 | 6.98 | 2.91 | 8.34 | 8.34 | 6.02 | |
Other Coking Products | 4.03 | 2.86 | 2.86 | 2.61 | 1.94 | 3.05 | 4.07 | 4.07 | 2.59 | |
Crude Oil | 3.07 | 3.02 | 3.08 | 3.01 | 3.08 | 3.05 | 3.10 | 3.10 | 3.10 | |
Gasoline | 3.05 | 2.93 | 3.04 | 3.04 | 3.04 | 3.14 | 3.11 | 3.11 | 2.99 | |
Kerosene | 3.10 | 3.04 | 3.15 | 3.15 | 3.15 | 3.14 | 3.09 | 3.09 | 3.10 | |
Diesel Oil | 3.15 | 3.10 | 3.15 | 3.25 | 3.15 | 3.11 | 3.15 | 3.15 | 3.12 | |
Fuel Oil | 3.09 | 3.17 | 3.05 | 3.05 | 3.05 | 3.05 | 3.13 | 3.13 | 3.26 | |
LPG | 2.95 | 3.13 | 2.95 | 2.95 | 2.95 | 3.66 | 2.87 | 2.87 | 3.15 | |
Refinery Gas | 2.82 | 3.04 | 3.04 | 2.29 | 3.04 | 3.36 | 2.41 | 2.41 | 3.38 | |
Other Petroleum Products | 2.90 | 3.01 | 3.24 | 2.89 | 3.24 | 3.05 | 3.12 | 3.12 | 3.12 | |
Natural Gas | 1.91 | 2.17 | 2.17 | 2.17 | 2.17 | 2.16 | 1.93 | 1.93 | 2.16 |
Table 4. Oxygenation efficiency of fossil fuels combusted in sectors.
Sectors | Raw Coal | Cleaned Coal | Other Washed Coal | Briquettes | Coke | Coke Oven Gas | Other Gas | Other Coking Products | Crude Oil | Gasoline | Kerosene | Diesel Oil | Fuel Oil | LPG | Refinery Gas | Other Petroleum Products | Natural Gas |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Farming, Forestry, Animal Husbandry, Fishery and Water Conservancy | 83% | 83% | 83% | 83% | 89% | 91% | 91% | 89% | 96% | 96% | 96% | 96% | 96% | 97% | 97% | 96% | 98% |
Coal Mining and Dressing | 82% | 82% | 82% | 82% | 89% | 91% | 91% | 89% | 96% | 96% | 96% | 96% | 96% | 97% | 97% | 96% | 98% |
Petroleum and Natural Gas Extraction | 82% | 82% | 82% | 82% | 89% | 91% | 91% | 89% | 96% | 96% | 96% | 96% | 96% | 97% | 97% | 96% | 98% |
Ferrous Metals Mining and Dressing | 82% | 82% | 82% | 82% | 89% | 91% | 91% | 89% | 96% | 96% | 96% | 96% | 96% | 97% | 97% | 96% | 98% |
Nonferrous Metals Mining and Dressing | 82% | 82% | 82% | 82% | 89% | 91% | 91% | 89% | 96% | 96% | 96% | 96% | 96% | 97% | 97% | 96% | 98% |
Non-metal Minerals Mining and Dressing | 82% | 82% | 82% | 82% | 89% | 91% | 91% | 89% | 96% | 96% | 96% | 96% | 96% | 97% | 97% | 96% | 98% |
Other Minerals Mining and Dressing | 82% | 82% | 82% | 82% | 89% | 91% | 91% | 89% | 96% | 96% | 96% | 96% | 96% | 97% | 97% | 96% | 98% |
Logging and Transport of Wood and Bamboo | 82% | 82% | 82% | 82% | 89% | 91% | 91% | 89% | 96% | 96% | 96% | 96% | 96% | 97% | 97% | 96% | 98% |
Food Processing | 80% | 80% | 80% | 80% | 89% | 91% | 91% | 89% | 96% | 96% | 96% | 96% | 96% | 97% | 97% | 96% | 98% |
Food Production | 80% | 80% | 80% | 80% | 89% | 91% | 91% | 89% | 96% | 96% | 96% | 96% | 96% | 97% | 97% | 96% | 98% |
Beverage Production | 80% | 80% | 80% | 80% | 89% | 91% | 91% | 89% | 96% | 96% | 96% | 96% | 96% | 97% | 97% | 96% | 98% |
Tobacco Processing | 80% | 80% | 80% | 80% | 89% | 91% | 91% | 89% | 96% | 96% | 96% | 96% | 96% | 97% | 97% | 96% | 98% |
Textile Industry | 82% | 82% | 82% | 82% | 89% | 91% | 91% | 89% | 96% | 96% | 96% | 96% | 96% | 97% | 97% | 96% | 98% |
Garments and Other Fibre Products | 82% | 82% | 82% | 82% | 89% | 91% | 91% | 89% | 96% | 96% | 96% | 96% | 96% | 97% | 97% | 96% | 98% |
Leather, Furs, Down and Related Products | 82% | 82% | 82% | 82% | 89% | 91% | 91% | 89% | 96% | 96% | 96% | 96% | 96% | 97% | 97% | 96% | 98% |
Timber Processing, Bamboo, Cane, Palm Fibre & Straw Products | 80% | 80% | 80% | 80% | 89% | 91% | 91% | 89% | 96% | 96% | 96% | 96% | 96% | 97% | 97% | 96% | 98% |
Furniture Manufacturing | 80% | 80% | 80% | 80% | 89% | 91% | 91% | 89% | 96% | 96% | 96% | 96% | 96% | 97% | 97% | 96% | 98% |
Papermaking and Paper Products | 80% | 80% | 80% | 80% | 89% | 91% | 91% | 89% | 96% | 96% | 96% | 96% | 96% | 97% | 97% | 96% | 98% |
Printing and Record Medium Reproduction | 80% | 80% | 80% | 80% | 89% | 91% | 91% | 89% | 96% | 96% | 96% | 96% | 96% | 97% | 97% | 96% | 98% |
Cultural, Educational and Sports Articles | 80% | 80% | 80% | 80% | 89% | 91% | 91% | 89% | 96% | 96% | 96% | 96% | 96% | 97% | 97% | 96% | 98% |
Petroleum Processing and Coking | 83% | 83% | 83% | 83% | 89% | 91% | 91% | 89% | 96% | 96% | 96% | 96% | 96% | 97% | 97% | 96% | 98% |
Raw Chemical Materials and Chemical Products | 85% | 85% | 85% | 85% | 89% | 91% | 91% | 89% | 96% | 96% | 96% | 96% | 96% | 97% | 97% | 96% | 98% |
Medical and Pharmaceutical Products | 85% | 85% | 85% | 85% | 89% | 91% | 91% | 89% | 96% | 96% | 96% | 96% | 96% | 97% | 97% | 96% | 98% |
Chemical Fibre | 85% | 85% | 85% | 85% | 89% | 91% | 91% | 89% | 96% | 96% | 96% | 96% | 96% | 97% | 97% | 96% | 98% |
Rubber Products | 85% | 85% | 85% | 85% | 89% | 91% | 91% | 89% | 96% | 96% | 96% | 96% | 96% | 97% | 97% | 96% | 98% |
Plastic Products | 85% | 85% | 85% | 85% | 89% | 91% | 91% | 89% | 96% | 96% | 96% | 96% | 96% | 97% | 97% | 96% | 98% |
Non-metal Mineral Products | 90% | 90% | 90% | 90% | 89% | 91% | 91% | 89% | 96% | 96% | 96% | 96% | 96% | 97% | 97% | 96% | 98% |
Smelting and Pressing of Ferrous Metals | 84% | 84% | 84% | 84% | 89% | 91% | 91% | 89% | 96% | 96% | 96% | 96% | 96% | 97% | 97% | 96% | 98% |
Smelting and Pressing of Nonferrous Metals | 84% | 84% | 84% | 84% | 89% | 91% | 91% | 89% | 96% | 96% | 96% | 96% | 96% | 97% | 97% | 96% | 98% |
Metal Products | 82% | 82% | 82% | 82% | 89% | 91% | 91% | 89% | 96% | 96% | 96% | 96% | 96% | 97% | 97% | 96% | 98% |
Ordinary Machinery | 82% | 82% | 82% | 82% | 89% | 91% | 91% | 89% | 96% | 96% | 96% | 96% | 96% | 97% | 97% | 96% | 98% |
Equipment for Special Purposes | 82% | 82% | 82% | 82% | 89% | 91% | 91% | 89% | 96% | 96% | 96% | 96% | 96% | 97% | 97% | 96% | 98% |
Transportation Equipment | 82% | 82% | 82% | 82% | 89% | 91% | 91% | 89% | 96% | 96% | 96% | 96% | 96% | 97% | 97% | 96% | 98% |
Electric Equipment and Machinery | 82% | 82% | 82% | 82% | 89% | 91% | 91% | 89% | 96% | 96% | 96% | 96% | 96% | 97% | 97% | 96% | 98% |
Electronic and Telecommunications Equipment | 82% | 82% | 82% | 82% | 89% | 91% | 91% | 89% | 96% | 96% | 96% | 96% | 96% | 97% | 97% | 96% | 98% |
Instruments, Meters, Cultural and Office Machinery | 82% | 82% | 82% | 82% | 89% | 91% | 91% | 89% | 96% | 96% | 96% | 96% | 96% | 97% | 97% | 96% | 98% |
Other Manufacturing Industry | 82% | 82% | 82% | 82% | 89% | 91% | 91% | 89% | 96% | 96% | 96% | 96% | 96% | 97% | 97% | 96% | 98% |
Scrap and Waste | 87% | 87% | 87% | 87% | 89% | 91% | 91% | 89% | 96% | 96% | 96% | 96% | 96% | 97% | 97% | 96% | 98% |
Production and Supply of Electric Power, Steam and Hot Water | 87% | 87% | 87% | 87% | 89% | 91% | 91% | 89% | 96% | 96% | 96% | 96% | 96% | 97% | 97% | 96% | 98% |
Production and Supply of Gas | 82% | 82% | 82% | 82% | 89% | 91% | 91% | 89% | 96% | 96% | 96% | 96% | 96% | 97% | 97% | 96% | 98% |
Production and Supply of Tap Water | 82% | 82% | 82% | 82% | 89% | 91% | 91% | 89% | 96% | 96% | 96% | 96% | 96% | 97% | 97% | 96% | 98% |
Construction | 83% | 83% | 83% | 83% | 89% | 91% | 91% | 89% | 96% | 96% | 96% | 96% | 96% | 97% | 97% | 96% | 98% |
Transportation, Storage, Post and Telecommunication Services | 74% | 74% | 74% | 74% | 89% | 91% | 91% | 89% | 96% | 96% | 96% | 96% | 96% | 97% | 97% | 96% | 98% |
Wholesale, Retail Trade and Catering Services | 74% | 74% | 74% | 74% | 89% | 91% | 91% | 89% | 96% | 96% | 96% | 96% | 96% | 97% | 97% | 96% | 98% |
Others | 74% | 74% | 74% | 74% | 89% | 91% | 91% | 89% | 96% | 96% | 96% | 96% | 96% | 97% | 97% | 96% | 98% |
Urban Household Energy Use | 74% | 74% | 74% | 74% | 89% | 91% | 91% | 89% | 96% | 96% | 96% | 96% | 96% | 97% | 97% | 96% | 98% |
Rural Household Energy Use | 74% | 74% | 74% | 74% | 89% | 91% | 91% | 89% | 96% | 96% | 96% | 96% | 96% | 97% | 97% | 96% | 98% |
The process-related CO2 emissions (CEt) are calculated in Equation 241. We include seven industrial processes, including cement production (for approximately 70% of the total process-related emissions in China45,46), lime production (the 2nd largest emissions source47), ammonia production, soda ash production, ferrochromium production, silicon metal production, and unclassified ferro-production. The process-related emissions are allocated to the corresponding sectors in the emission inventory. Cement and lime-related emissions are allocated to the sector “Non-metal Mineral Products”; ammonia and soda ash-related emissions are allocated to the sector “Raw Chemical Materials and Chemical Products”; Ferrochromium, silicon metal, and unclassified ferro-related emissions are allocated to the sector “Smelting and Pressing of Ferrous Metals”.
ADt and EFt in the equation refer to industrial production (activity data) and emission factors, respectively. The emission factors of industrial processes are collected from IPCC41 and NDRC42, as shown in Table 5.
Table 5. 7 industrial processes and emissions factors.
No. | Industry process | Emission factors | Allocation sectors |
---|---|---|---|
1 | Ammonia production | 1.5000 | Raw Chemical Materials and Chemical Products |
2 | Soda Ash production | 0.4150 | Raw Chemical Materials and Chemical Products |
3 | Cement production | 0.4985 | Non-metal Mineral Products |
4 | Lime production | 0.6830 | Non-metal Mineral Products |
5 | Ferrochromium production | 1.3000 | Smelting and Pressing of Ferrous Metals |
6 | Silicon metal production | 4.3000 | Smelting and Pressing of Ferrous Metals |
7 | Ferro-unclassified production | 4.0000 | Smelting and Pressing of Ferrous Metals |
The cities’ CO2 emissions matrices (namely, inventories) are created as 19 columns and 48 rows. Seventeen fossil fuel-related emissions, process-related emissions and total emissions are represented by 19 columns, while 47 rows correspond to the 47 socioeconomic sectors. Each element of the matrices is identified as the CO2 emissions from fossil fuel combustion/industrial production in the corresponding sector. An inventory of Beijing is given in Table 6 (available online only) as an example.
Table 6. Emissions inventory of Beijing in 2010.
Unit: million tonnes | Raw Coal | Cleaned Coal | Other Washed Coal | Briquettes | Coke | Coke Oven Gas | Other Gas | Other Coking Products | Crude Oil | Gasoline | Kerosene | Diesel Oil | Fuel Oil | LPG | Refinery Gas | Other Petroleum Products | Natural Gas | Non-fossil Heat | Non-fossil Electricity | Other Energy | Process | Total |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Total Consumption | 39.6 | 0.0 | 0.1 | 0.5 | 6.3 | 0.9 | 0.0 | 0.0 | 0.0 | 10.9 | 11.9 | 7.3 | 0.5 | 1.3 | 2.5 | 0.4 | 15.2 | 0.0 | 0.0 | 0.0 | 5.2 | 102.6 |
Farming, Forestry, Animal Husbandry, Fishery and Water Conservancy | 0.8 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.1 | 0.0 | 0.2 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 1.1 |
Coal Mining and Dressing | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
Petroleum and Natural Gas Extraction | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
Ferrous Metals Mining and Dressing | 1.2 | 0.0 | 0.0 | 0.0 | 1.0 | 0.1 | 0.0 | 0.0 | 0.0 | 0.1 | 0.0 | 0.2 | 0.0 | 0.0 | 0.3 | 0.0 | 0.3 | 0.0 | 0.0 | 0.0 | 0.0 | 3.3 |
Nonferrous Metals Mining and Dressing | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
Nonmetal Minerals Mining and Dressing | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
Other Minerals Mining and Dressing | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
Logging and Transport of Wood and Bamboo | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
Food Processing | 0.1 | 0.0 | 0.0 | 0.0 | 0.1 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.2 |
Food Production | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.1 |
Beverage Production | 0.1 | 0.0 | 0.0 | 0.0 | 0.1 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.4 |
Tobacco Processing | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
Textile Industry | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.1 |
Garments and Other Fiber Products | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.1 |
Leather, Furs, Down and Related Products | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
Timber Processing, Bamboo, Cane, Palm Fiber & Straw Products | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
Furniture Manufacturing | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
Papermaking and Paper Products | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.1 |
Printing and Record Medium Reproduction | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
Cultural, Educational and Sports Articles | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
Petroleum Processing and Coking | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
Raw Chemical Materials and Chemical Products | 0.4 | 0.0 | 0.0 | 0.0 | 0.3 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.1 | 0.0 | 0.1 | 0.0 | 0.0 | 0.0 | 0.0 | 0.9 |
Medical and Pharmaceutical Products | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.1 |
Chemical Fiber | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
Rubber Products | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
Plastic Products | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.1 |
Nonmetal Mineral Products | 0.8 | 0.0 | 0.0 | 0.0 | 0.6 | 0.1 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.1 | 0.0 | 0.0 | 0.2 | 0.0 | 0.2 | 0.0 | 0.0 | 0.0 | 5.2 | 7.2 |
Smelting and Pressing of Ferrous Metals | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
Smelting and Pressing of Nonferrous Metals | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
Metal Products | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.1 |
Ordinary Machinery | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.1 |
Equipment for Special Purposes | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.1 |
Transportation Equipment | 0.1 | 0.0 | 0.0 | 0.0 | 0.1 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.3 |
Electric Equipment and Machinery | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.1 |
Electronic and Telecommunications Equipment | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
Instruments, Meters, Cultural and Office Machinery | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
Other Manufacturing Industry | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
Scrap and waste | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
Production and Supply of Electric Power, Steam and Hot Water | 27.4 | 0.0 | 0.1 | 0.4 | 3.9 | 0.6 | 0.0 | 0.0 | 0.0 | 0.3 | 0.0 | 0.8 | 0.4 | 0.0 | 1.6 | 0.4 | 6.8 | 0.0 | 0.0 | 0.0 | 0.0 | 42.7 |
Production and Supply of Gas | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
Production and Supply of Tap Water | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
Construction | 0.3 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.3 | 0.0 | 1.2 | 0.0 | 0.0 | 0.0 | 0.0 | 0.1 | 0.0 | 0.0 | 0.0 | 0.0 | 1.9 |
Transportation, Storage, Post and Telecommunication Services | 0.3 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 1.2 | 11.9 | 3.9 | 0.0 | 0.0 | 0.0 | 0.0 | 0.5 | 0.0 | 0.0 | 0.0 | 0.0 | 17.9 |
Wholesale, Retail Trade and Catering Services | 0.6 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.6 | 0.0 | 0.3 | 0.0 | 0.4 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 | 0.0 | 2.9 |
Others | 3.0 | 0.0 | 0.0 | 0.1 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 1.4 | 0.0 | 0.5 | 0.0 | 0.1 | 0.0 | 0.0 | 3.9 | 0.0 | 0.0 | 0.0 | 0.0 | 8.9 |
Urban | 1.1 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 6.5 | 0.0 | 0.0 | 0.0 | 0.5 | 0.0 | 0.0 | 2.1 | 0.0 | 0.0 | 0.0 | 0.0 | 10.2 |
Rural | 3.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.2 | 0.0 | 0.0 | 0.0 | 0.2 | 0.0 | 0.0 | 0.1 | 0.0 | 0.0 | 0.0 | 0.0 | 3.5 |
These methods on emission inventory construction are expanded version of descriptions in our related work39. MATLAB R2014a is used to construct the cities’ emission inventories. We provided the MATLAB code in the Supplementary Information. We also provided the activity data of the cities for additional data transparency and verifiability (see “China city-level Energy inventory, 2010”, Data Citation 1). Researchers will be able to use the MATLAB code and energy inventories to recalculate the emission inventories for the cities or replicate to other cities.
Activity data collection
Fossil fuel combustion, i.e., the activity data for energy-related emission accounts, includes two parts: the energy inputs for electricity/heat generation and the total final consumption. Other inputs for energy transformation, such as coal cleaning or petroleum refineries, transfer the carbon element from one fuel to another. These processes emit little CO2. Following our previous emissions inventories constructed for China and its provinces39, fossil fuel combustion can be collected from a region’s energy balance table (EBT) and final energy consumption can be captured by the industrial sector (Energyij). The EBT provides each fossil fuel’s transformation and final consumption in farming, industry, construction, three service sectors, and households (rural and urban). As the entire industry sector consists of 40 sub-sectors, Energyij presents the sectoral consumption of fossil fuel for the industry sector.
Generally, the EBT and Energyij can be found in a city’s statistical yearbook. However, due to the poor data quality of city-level statistics, not all cities’ yearbooks publish the EBT or Energyij. We developed a series of methods in our previous study to estimate missing data48:
EBT: Very few cities have EBT in their statistical yearbooks. We scale down the corresponding provincial EBT to obtain the city table. We use each sector’s GDP to estimate farming, construction, and three service sectors, assuming that the city has the same farming/construction/service energy intensity as its province. We also use the urban/rural population to estimate the urban/rural household energy estimation on the premise that the city has the same per capita residential energy consumption as its province. The GDP and population data are collected from statistical yearbooks for the cities and their corresponding provinces.
Energyij: Some cities only provide Energyij from enterprises of above-designated-size (ADS). ADS enterprises are defined as enterprises with prime operating revenue over 20 or 5 million yuan for different cities. ADS enterprises account for 50 to 90% (roughly) of one city’s total industrial output. We use the ADS industrial output ratio (calculated as the whole-industry output divided by the ADS enterprises’ output) to scale up ADS Energyij and obtain sectoral fossil fuel consumption at the whole-industry scale.
As for cement production, the cities’ statistical yearbooks provide total cement production or production from ADS enterprises. We then scaled up the ADS cement production by the ADS industrial output ratio to obtain the total cement production.
The raw activity data are collected through a “crowd-sourcing” working mode implemented in the Applied Energy Summer School 2017 and 2018. Over 100 students joined the summer school and participated in data collection. The summer school will be held annually in the future, and more researchers will contribute to and update city-level data collection.
These methods on city-level data estimation and collection are expanded version of descriptions in our related work48.
Socioeconomic indexes
This study collects several socioeconomic indexes for the 182 cities from the “China City Statistical Yearbook”49, including:
population, in 10 thousand;
employed population, in 10 thousand;
employed population in sectors (primary industry; mining; manufacturing, electric power, gas and water production and supply; construction; transport, storage and post; information transmission, computer services and software industry; wholesale and retail trade; hotel and catering services; financial intermediation; real estate; leasing and business services; scientific research, technical services and geological exploration; water, environmental and public facilities management; resident services and other services; education; health, social security and social welfare; culture, sports and entertainment; public administration and social organization), in 10 thousand;
area, in square kilometres;
built up area, in square kilometres;
gross domestic product (GDP), in 10 thousand yuan;
primary industry, secondary industry, and tertiary industry’s share in GDP, in %;
industrial output, in 10 thousand yuan.
The socioeconomic indexes (as shown in Table 7 (available online only) and “China city-level socioeconomic inventory, 2010”, Data Citation 1) can be used to explore the drivers and characteristics of cities’ emissions.
Table 7. Emissions-socioeconomic indexes of the cities.
City-Ename | City-Cname | CO2 emissions | Population | Employed population | Employed population in “primary industry” | Employed population in "mining" | Employed population in "manufacturing" | Employed population in "electric power, gas and water production and supply" | Employed population in "construction" | Employed population in "transport, storage and post" | Employed population in "information transmission, computer services and software industry" | Employed population in "wholesale and retail trade" | Employed population in "hotel and catering services" | Employed population in "financial intermediation" | Employed population in "real estate" | Employed population in "leasing and business services" | Employed population in "scientific research, technical services and geological exploration" | Employed population in "water, environmental and public facilities management" | Employed population in "resident services and other services" | Employed population in "education" | Employed population in "health, social security and social welfare" | Employed population in "culture, sports and entertainment" | Employed population in "public administration and social organization" | Area | Built up area | GDP | Primay industry's share in GDP | Secondary industry's share in GDP | Tertiary industry's share in GDP | Industrial output |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
million tonnes | 10 thousand | 10 thousand | 10 thousand | 10 thousand | 10 thousand | 10 thousand | 10 thousand | 10 thousand | 10 thousand | 10 thousand | 10 thousand | 10 thousand | 10 thousand | 10 thousand | 10 thousand | 10 thousand | 10 thousand | 10 thousand | 10 thousand | 10 thousand | 10 thousand | Square kilometers | Square kilometers | 10 thousand yuan | % | % | % | 10 thousand yuan | ||
Beijing | 北京 | 102.6 | 1257.80 | 646.63 | 3.23 | 4.54 | 100.55 | 6.80 | 39.35 | 51.00 | 41.73 | 55.38 | 27.96 | 27.24 | 31.54 | 77.81 | 45.74 | 8.76 | 7.44 | 40.56 | 20.67 | 15.25 | 41.08 | 16411 | 1186 | 141135800 | 0.88 | 24.01 | 75.11 | 136998388 |
Tianjin | 天津 | 132.0 | 984.85 | 205.65 | 0.71 | 8.96 | 75.30 | 3.27 | 10.21 | 12.50 | 2.24 | 12.39 | 4.83 | 6.95 | 3.61 | 6.95 | 6.47 | 3.55 | 6.86 | 16.43 | 8.98 | 1.75 | 13.69 | 11760 | 687 | 92244600 | 1.58 | 52.47 | 45.95 | 167518155 |
Shijiazhuang | 河北石家庄 | 119.5 | 989.16 | 84.15 | 0.41 | 0.67 | 23.28 | 2.66 | 4.50 | 5.67 | 0.91 | 6.09 | 1.28 | 4.44 | 0.41 | 0.85 | 2.23 | 1.69 | 0.32 | 12.34 | 3.91 | 1.42 | 11.07 | 15848 | 203 | 34010186 | 10.87 | 48.63 | 40.51 | 56553423 |
Tangshan | 河北唐山 | 194.0 | 735.00 | 84.02 | 3.03 | 10.42 | 24.81 | 2.76 | 5.14 | 3.87 | 0.65 | 4.12 | 0.62 | 3.77 | 0.56 | 0.84 | 0.40 | 1.66 | 0.12 | 9.07 | 3.43 | 0.48 | 8.27 | 13472 | 234 | 44691588 | 9.44 | 58.14 | 32.42 | 75450331 |
Handan | 河北邯郸 | 129.6 | 963.50 | 56.57 | 0.25 | 6.59 | 9.22 | 2.71 | 3.79 | 2.42 | 0.53 | 2.31 | 0.36 | 2.48 | 0.31 | 0.47 | 0.95 | 1.49 | 0.04 | 10.75 | 2.87 | 0.52 | 8.51 | 12062 | 111 | 23615569 | 13.04 | 54.21 | 32.75 | 41073243 |
Zhangjiakou | 河北张家口 | 53.2 | 465.97 | 33.72 | 0.55 | 2.32 | 6.54 | 1.33 | 1.74 | 1.08 | 0.46 | 1.47 | 0.28 | 1.35 | 0.63 | 0.28 | 0.41 | 0.75 | 0.08 | 5.80 | 1.95 | 0.23 | 6.47 | 36873 | 84 | 9664158 | 15.83 | 42.96 | 41.21 | 8934836 |
Taiyuan | 山西太原 | 87.0 | 365.50 | 84.64 | 0.33 | 8.40 | 24.69 | 1.57 | 8.09 | 7.71 | 1.10 | 3.51 | 1.91 | 2.56 | 0.51 | 1.44 | 3.16 | 1.60 | 0.39 | 7.54 | 2.94 | 1.47 | 5.72 | 6963 | 245 | 17780539 | 1.70 | 44.91 | 53.39 | 19946527 |
Yangquan | 山西阳泉 | 33.0 | 130.78 | 23.74 | 0.04 | 11.29 | 2.16 | 0.84 | 0.96 | 0.83 | 0.10 | 0.88 | 0.11 | 0.67 | 0.16 | 0.39 | 0.16 | 0.23 | 0.02 | 1.88 | 0.69 | 0.14 | 2.19 | 4570 | 52 | 4293774 | 1.53 | 59.46 | 39.01 | 5426118 |
Changzhi | 山西长治 | 57.4 | 331.54 | 37.30 | 0.31 | 9.39 | 7.95 | 1.04 | 1.32 | 1.06 | 0.29 | 1.38 | 0.20 | 1.48 | 0.10 | 0.54 | 0.30 | 0.67 | 0.01 | 4.64 | 1.44 | 0.39 | 4.79 | 13896 | 59 | 9202336 | 4.37 | 65.38 | 30.24 | 14332828 |
Jincheng | 山西晋城 | 34.6 | 216.23 | 27.15 | 0.15 | 11.79 | 2.31 | 0.72 | 0.62 | 0.67 | 0.24 | 1.61 | 0.21 | 1.00 | 0.07 | 0.26 | 0.15 | 0.43 | 0.04 | 2.71 | 1.05 | 0.20 | 2.92 | 9425 | 41 | 7305428 | 4.20 | 63.60 | 32.19 | 8511244 |
Shuozhou | 山西朔州 | 27.2 | 159.07 | 17.89 | 0.87 | 3.88 | 1.36 | 0.80 | 1.38 | 0.31 | 0.23 | 1.21 | 0.17 | 0.56 | 0.12 | 0.55 | 0.10 | 0.20 | 0.08 | 1.95 | 0.51 | 0.11 | 3.50 | 11066 | 36 | 6701476 | 6.05 | 56.56 | 37.39 | 8285136 |
Jinzhong | 山西晋中 | 60.7 | 320.96 | 35.06 | 0.18 | 7.33 | 6.75 | 1.12 | 2.11 | 0.85 | 0.34 | 1.50 | 0.42 | 1.73 | 0.19 | 0.43 | 0.56 | 0.54 | 0.02 | 4.52 | 1.62 | 0.32 | 4.53 | 16392 | 39 | 7638366 | 8.50 | 54.76 | 36.74 | 10302374 |
Xinzhou | 山西忻州 | 24.2 | 307.55 | 22.70 | 0.33 | 2.64 | 1.81 | 0.44 | 1.25 | 0.77 | 0.39 | 1.50 | 0.29 | 0.89 | 0.07 | 0.31 | 0.22 | 0.45 | 0.04 | 4.33 | 1.47 | 0.20 | 5.30 | 25117 | 30 | 4374561 | 11.25 | 44.59 | 44.15 | 3930339 |
Hohhot | 内蒙古呼和浩特 | 69.0 | 229.56 | 31.50 | 0.36 | 0.02 | 5.84 | 1.47 | 1.50 | 1.44 | 0.84 | 1.13 | 0.70 | 1.99 | 0.21 | 0.93 | 1.37 | 1.55 | 0.30 | 4.68 | 1.68 | 0.93 | 4.56 | 17224 | 166 | 18657116 | 4.90 | 36.39 | 58.71 | 11885198 |
Baotou | 内蒙古包头 | 119.2 | 219.80 | 32.59 | 0.31 | 0.95 | 12.62 | 1.28 | 2.10 | 1.37 | 0.61 | 1.22 | 0.69 | 1.89 | 0.25 | 0.28 | 0.65 | 0.87 | 0.11 | 3.12 | 1.27 | 0.32 | 2.68 | 27768 | 183 | 24608100 | 2.70 | 54.11 | 43.19 | 24122422 |
Wuhai | 内蒙古乌海 | 21.6 | 53.00 | 9.52 | 0.05 | 2.80 | 1.30 | 0.48 | 1.58 | 0.23 | 0.13 | 0.13 | 0.02 | 0.32 | 0.10 | 0.06 | 0.09 | 0.28 | 0.01 | 0.70 | 0.30 | 0.07 | 0.87 | 1754 | 63 | 3911235 | 0.95 | 71.72 | 27.33 | 5453200 |
Chifeng | 内蒙古赤峰 | 38.1 | 457.74 | 30.50 | 1.95 | 3.49 | 3.64 | 1.30 | 1.77 | 0.89 | 0.34 | 0.76 | 0.19 | 1.16 | 0.14 | 0.41 | 0.40 | 0.52 | 0.07 | 6.55 | 1.98 | 0.31 | 4.63 | 90021 | 81 | 10862293 | 16.33 | 51.24 | 32.43 | 12658407 |
Tongliao | 内蒙古通辽 | 61.2 | 318.70 | 24.34 | 5.82 | 1.35 | 2.15 | 0.79 | 1.60 | 0.60 | 0.35 | 0.57 | 0.16 | 0.74 | 0.25 | 0.12 | 0.32 | 0.73 | 0.03 | 4.27 | 1.29 | 0.24 | 2.96 | 59535 | 66 | 11766183 | 15.15 | 58.62 | 26.23 | 18097485 |
Ordos | 内蒙古鄂尔多斯 | 131.6 | 152.38 | 17.31 | 0.47 | 3.05 | 2.93 | 0.82 | 0.07 | 0.32 | 0.29 | 0.25 | 0.05 | 0.79 | 0.03 | 0.13 | 0.19 | 0.85 | 0.01 | 2.53 | 0.79 | 0.24 | 3.50 | 86752 | 113 | 26432300 | 2.68 | 58.69 | 38.63 | 26810700 |
Ulanqab | 内蒙古乌兰察布 | 43.1 | 287.02 | 14.62 | 0.36 | 0.18 | 1.06 | 0.90 | 0.66 | 0.71 | 0.30 | 0.63 | 0.12 | 0.75 | 0.10 | 0.12 | 0.29 | 0.56 | 0.02 | 2.82 | 0.85 | 0.24 | 3.95 | 54492 | 35 | 5676016 | 16.55 | 52.28 | 31.17 | 6774228 |
Shenyang | 辽宁沈阳 | 63.4 | 719.60 | 110.42 | 0.99 | 2.17 | 30.40 | 3.22 | 5.48 | 10.70 | 1.52 | 5.66 | 2.06 | 4.28 | 2.27 | 4.61 | 4.59 | 3.15 | 1.37 | 11.45 | 6.04 | 1.68 | 8.78 | 12980 | 412 | 50175427 | 4.64 | 50.42 | 44.94 | 96125255 |
Dalian | 辽宁大连 | 73.9 | 586.44 | 94.20 | 0.97 | 0.22 | 40.88 | 1.71 | 4.76 | 5.30 | 2.42 | 3.61 | 2.33 | 5.03 | 2.88 | 1.93 | 1.59 | 1.41 | 0.36 | 7.82 | 4.11 | 0.82 | 6.05 | 12574 | 390 | 51581621 | 6.69 | 50.88 | 42.43 | 77018355 |
Benxi | 辽宁本溪 | 67.0 | 154.60 | 23.21 | 0.19 | 1.73 | 8.36 | 0.83 | 2.01 | 1.00 | 0.24 | 0.65 | 0.06 | 0.91 | 0.50 | 0.16 | 0.23 | 0.65 | 0.04 | 2.10 | 1.33 | 0.19 | 2.03 | 8411 | 107 | 8603675 | 5.04 | 62.30 | 32.66 | 15113534 |
Dandong | 辽宁丹东 | 10.1 | 241.36 | 21.22 | 0.30 | 0.51 | 4.90 | 0.90 | 1.70 | 1.14 | 0.42 | 0.98 | 0.32 | 0.67 | 0.65 | 0.21 | 0.72 | 0.80 | 0.06 | 2.51 | 1.28 | 0.25 | 2.90 | 15290 | 53 | 7288908 | 13.73 | 51.20 | 35.07 | 8650865 |
Fuxin | 辽宁阜新 | 34.8 | 192.38 | 17.54 | 0.44 | 4.58 | 1.67 | 0.81 | 0.89 | 0.42 | 0.25 | 0.58 | 0.08 | 0.75 | 0.15 | 0.22 | 0.26 | 0.40 | 0.04 | 2.45 | 1.10 | 0.24 | 2.21 | 10355 | 76 | 3788656 | 24.46 | 41.82 | 33.72 | 4477708 |
Changchun | 吉林长春 | 61.2 | 758.89 | 92.81 | 1.33 | 1.24 | 28.33 | 2.64 | 5.24 | 3.40 | 2.13 | 4.49 | 1.88 | 3.38 | 2.15 | 3.10 | 4.04 | 2.60 | 0.41 | 12.59 | 4.68 | 1.65 | 7.53 | 20604 | 394 | 33290329 | 7.59 | 51.66 | 40.74 | 58841613 |
Jilin | 吉林吉林 | 41.3 | 434.03 | 33.25 | 1.64 | 1.06 | 10.28 | 1.44 | 1.48 | 0.99 | 0.27 | 0.82 | 0.17 | 1.40 | 0.19 | 0.13 | 0.47 | 0.86 | 0.05 | 5.12 | 2.18 | 0.37 | 4.33 | 27126 | 166 | 18006376 | 10.80 | 49.76 | 39.44 | 21041551 |
Siping | 吉林四平 | 21.8 | 340.55 | 21.14 | 1.03 | 0.27 | 5.85 | 0.68 | 0.58 | 0.57 | 0.32 | 0.35 | 0.06 | 0.88 | 0.27 | 0.06 | 0.54 | 0.85 | 0.03 | 3.84 | 1.87 | 0.32 | 2.77 | 14080 | 51 | 7795527 | 27.13 | 42.75 | 30.12 | 10364734 |
Liaoyuan | 吉林辽源 | 18.4 | 123.75 | 8.83 | 0.36 | 2.44 | 0.81 | 0.40 | 0.13 | 0.21 | 0.09 | 0.12 | 0.02 | 0.41 | 0.08 | 0.07 | 0.13 | 0.19 | 0.02 | 1.35 | 0.70 | 0.11 | 1.19 | 5140 | 46 | 4101426 | 10.43 | 56.18 | 33.39 | 5856489 |
Baicheng | 吉林白城 | 7.6 | 202.64 | 18.51 | 3.40 | 0.01 | 1.55 | 0.42 | 0.53 | 0.54 | 0.23 | 0.74 | 0.17 | 0.58 | 0.14 | 0.17 | 0.34 | 1.46 | 0.39 | 3.16 | 1.36 | 0.25 | 3.07 | 25745 | 38 | 4451802 | 18.74 | 45.26 | 36.00 | 2592456 |
Yanbian | 吉林延边 | 14.3 | ||||||||||||||||||||||||||||
Harbin | 黑龙江哈尔滨 | 68.9 | 992.02 | 135.17 | 5.50 | 0.71 | 33.70 | 3.79 | 11.47 | 10.83 | 2.29 | 10.21 | 2.45 | 4.76 | 2.73 | 2.70 | 4.29 | 2.89 | 1.95 | 16.09 | 6.36 | 1.76 | 10.69 | 53068 | 359 | 36648538 | 11.26 | 37.78 | 50.96 | 23047364 |
Qiqihaer | 黑龙江齐齐哈尔 | 45.6 | 568.11 | 41.97 | 7.65 | 0.02 | 9.46 | 1.47 | 1.80 | 4.54 | 0.50 | 0.96 | 0.05 | 1.50 | 0.45 | 0.24 | 0.60 | 1.33 | 0.10 | 4.85 | 2.25 | 0.37 | 3.83 | 42469 | 135 | 8804569 | 21.81 | 40.63 | 37.55 | 8320561 |
Jixi | 黑龙江鸡西 | 44.1 | 189.20 | 27.66 | 6.70 | 7.74 | 1.89 | 0.86 | 1.03 | 0.89 | 0.20 | 1.04 | 0.25 | 0.67 | 0.07 | 0.09 | 0.26 | 0.49 | 0.24 | 1.93 | 0.78 | 0.16 | 2.37 | 22531 | 79 | 4194931 | 25.55 | 42.31 | 32.14 | 2889386 |
Hegang | 黑龙江鹤岗 | 43.2 | 109.10 | 25.89 | 8.55 | 8.01 | 1.39 | 0.47 | 0.44 | 0.63 | 0.14 | 0.96 | 0.33 | 0.42 | 0.05 | 0.01 | 0.08 | 0.55 | 0.31 | 1.35 | 0.63 | 0.14 | 1.43 | 14659 | 43 | 2509870 | 26.45 | 46.60 | 26.95 | 2844194 |
Shuangyashan | 黑龙江双鸭山 | 18.8 | 151.58 | 32.81 | 14.61 | 5.07 | 1.67 | 0.83 | 0.64 | 1.10 | 0.23 | 1.12 | 0.38 | 0.48 | 0.11 | 0.12 | 0.11 | 0.60 | 0.69 | 1.76 | 0.81 | 0.16 | 2.32 | 23209 | 59 | 3963504 | 30.34 | 44.78 | 24.88 | 3618786 |
Daqing | 黑龙江大庆 | 106.8 | 279.80 | 51.89 | 0.34 | 11.85 | 7.18 | 2.33 | 5.98 | 1.57 | 0.56 | 1.70 | 0.21 | 1.35 | 0.47 | 0.05 | 5.06 | 0.34 | 2.99 | 4.09 | 1.94 | 0.35 | 3.53 | 21219 | 207 | 29000642 | 3.28 | 82.24 | 14.48 | 32879601 |
Yichunhlj | 黑龙江伊春 | 8.4 | 126.95 | 18.21 | 10.23 | 0.26 | 2.28 | 0.64 | 0.62 | 0.33 | 0.26 | 0.14 | 0.04 | 0.40 | 0.09 | 0.04 | 0.15 | 0.21 | 0.01 | 0.65 | 0.42 | 0.10 | 1.34 | 32759 | 161 | 2024407 | 30.34 | 39.25 | 30.41 | 1945740 |
Jiamusi | 黑龙江佳木斯 | 11.5 | 253.78 | 27.63 | 9.56 | 0.06 | 1.94 | 0.81 | 1.79 | 1.30 | 0.20 | 1.34 | 0.33 | 0.77 | 0.10 | 0.19 | 0.30 | 0.91 | 0.52 | 2.95 | 1.38 | 0.18 | 3.00 | 32704 | 94 | 5124563 | 28.58 | 26.13 | 45.30 | 3139380 |
Heihe | 黑龙江黑河 | 6.6 | 174.21 | 29.46 | 16.30 | 0.33 | 0.86 | 0.69 | 0.66 | 1.14 | 0.08 | 1.00 | 0.44 | 0.66 | 0.05 | 0.28 | 0.20 | 0.61 | 0.47 | 1.92 | 0.89 | 0.15 | 2.73 | 82164 | 19 | 2610994 | 44.78 | 17.12 | 38.09 | 946708 |
Shanghai | 上海 | 187.5 | 1412.32 | 392.87 | 1.54 | 0.09 | 141.32 | 5.41 | 11.34 | 36.30 | 6.71 | 26.43 | 11.67 | 23.63 | 11.15 | 18.64 | 23.25 | 5.86 | 3.29 | 26.14 | 16.72 | 4.71 | 18.67 | 6340 | 866 | 171659800 | 0.66 | 42.05 | 57.28 | 301144067 |
Nanjing | 江苏南京 | 141.1 | 632.42 | 125.64 | 0.41 | 0.32 | 47.41 | 1.78 | 10.08 | 8.87 | 2.43 | 8.71 | 3.90 | 3.12 | 2.03 | 3.97 | 4.36 | 1.77 | 0.33 | 11.54 | 4.72 | 1.73 | 8.16 | 6587 | 619 | 51306500 | 2.77 | 45.37 | 51.85 | 86094998 |
Wuxi | 江苏无锡 | 73.8 | 466.56 | 82.95 | 0.24 | 45.43 | 1.44 | 5.29 | 2.36 | 0.82 | 3.36 | 1.64 | 2.64 | 0.63 | 1.65 | 1.20 | 0.92 | 0.17 | 6.36 | 3.30 | 0.57 | 4.93 | 4627 | 231 | 57933000 | 1.81 | 55.39 | 42.80 | 129710811 | |
Xuzhou | 江苏徐州 | 78.6 | 972.89 | 61.82 | 1.86 | 8.10 | 12.25 | 1.77 | 1.73 | 6.09 | 0.60 | 2.52 | 0.30 | 2.31 | 0.35 | 0.28 | 0.81 | 1.31 | 0.05 | 10.34 | 3.99 | 0.42 | 6.74 | 11259 | 239 | 29421394 | 9.61 | 50.67 | 39.71 | 51129660 |
Changzhou | 江苏常州 | 48.2 | 360.80 | 38.16 | 0.12 | 0.04 | 16.88 | 0.72 | 1.19 | 1.76 | 0.40 | 1.32 | 0.59 | 1.62 | 0.42 | 0.61 | 0.75 | 0.82 | 0.03 | 4.72 | 2.42 | 0.32 | 3.43 | 4372 | 153 | 30448900 | 3.28 | 55.30 | 41.43 | 73960851 |
Suzhoujs | 江苏苏州 | 173.0 | 637.66 | 130.87 | 0.15 | 90.06 | 1.43 | 3.20 | 2.29 | 1.09 | 2.93 | 1.77 | 3.69 | 0.90 | 0.91 | 0.73 | 1.27 | 0.10 | 7.67 | 4.69 | 0.70 | 7.29 | 8488 | 329 | 92289100 | 1.69 | 56.93 | 41.38 | 246516665 | |
Nantong | 江苏南通 | 42.0 | 762.92 | 63.11 | 1.13 | 30.26 | 1.00 | 5.13 | 1.88 | 0.61 | 1.54 | 0.35 | 2.89 | 0.47 | 0.80 | 0.43 | 0.75 | 0.05 | 7.12 | 3.46 | 0.36 | 4.88 | 8001 | 125 | 34656700 | 7.68 | 55.07 | 37.25 | 73831630 | |
Lianyungang | 江苏连云港 | 20.0 | 497.73 | 34.00 | 1.87 | 0.98 | 8.78 | 0.92 | 2.99 | 2.04 | 0.40 | 1.19 | 0.23 | 1.47 | 0.46 | 0.24 | 0.51 | 0.75 | 0.03 | 5.18 | 1.92 | 0.20 | 3.84 | 7500 | 120 | 11933100 | 15.30 | 45.68 | 39.02 | 19362814 |
Huaian | 江苏淮安 | 23.7 | 538.74 | 39.28 | 1.13 | 0.37 | 14.67 | 0.79 | 2.99 | 1.04 | 0.26 | 1.11 | 0.19 | 1.41 | 0.33 | 0.63 | 0.21 | 1.23 | 0.01 | 5.94 | 1.94 | 0.27 | 4.76 | 10072 | 120 | 13880700 | 14.12 | 46.62 | 39.26 | 24391100 |
Yancheng | 江苏盐城 | 24.9 | 816.12 | 51.91 | 2.42 | 0.61 | 16.37 | 0.87 | 6.52 | 1.23 | 0.53 | 1.79 | 0.41 | 2.54 | 0.36 | 1.25 | 0.41 | 0.89 | 0.07 | 7.38 | 2.65 | 0.37 | 5.24 | 16972 | 89 | 23327600 | 16.04 | 47.01 | 36.95 | 39383400 |
Yangzhou | 江苏扬州 | 31.4 | 459.12 | 40.09 | 0.09 | 1.80 | 13.78 | 0.41 | 6.99 | 0.91 | 0.52 | 0.95 | 0.44 | 1.24 | 0.31 | 0.45 | 0.47 | 0.66 | 0.07 | 4.91 | 2.04 | 0.21 | 3.84 | 6591 | 82 | 22294884 | 7.24 | 55.14 | 37.62 | 57533421 |
Zhenjiang | 江苏镇江 | 44.2 | 270.71 | 37.26 | 0.15 | 0.18 | 18.35 | 0.79 | 1.88 | 1.44 | 0.23 | 1.41 | 0.42 | 1.63 | 0.47 | 0.36 | 0.52 | 0.75 | 0.05 | 3.47 | 1.85 | 0.22 | 3.09 | 3847 | 109 | 19876400 | 4.10 | 56.38 | 39.52 | 41904178 |
Taizhoujs | 江苏泰州 | 25.4 | 504.65 | 37.15 | 0.24 | 15.30 | 0.62 | 2.00 | 1.01 | 0.51 | 1.71 | 0.36 | 1.71 | 0.48 | 0.86 | 0.36 | 0.58 | 0.06 | 5.02 | 2.33 | 0.18 | 3.82 | 5787 | 65 | 20487200 | 7.40 | 54.95 | 37.64 | 49160775 | |
Suqian | 江苏宿迁 | 6.5 | 546.28 | 21.51 | 0.11 | 0.16 | 5.93 | 0.37 | 2.36 | 0.32 | 0.25 | 0.50 | 0.03 | 0.62 | 0.09 | 0.02 | 0.10 | 0.57 | 0.01 | 5.85 | 1.02 | 0.12 | 3.08 | 8555 | 65 | 10640900 | 17.58 | 45.03 | 37.39 | 11373660 |
Hangzhou | 浙江杭州 | 84.6 | 689.12 | 232.71 | 0.15 | 0.27 | 75.91 | 2.19 | 47.49 | 8.78 | 6.49 | 12.85 | 8.10 | 7.52 | 6.05 | 7.42 | 8.52 | 4.38 | 0.83 | 14.41 | 7.76 | 2.03 | 11.56 | 16596 | 413 | 59491687 | 3.50 | 47.81 | 48.69 | 110796496 |
Ningbo | 浙江宁波 | 102.1 | 574.08 | 140.19 | 0.13 | 0.02 | 63.67 | 1.76 | 26.62 | 4.65 | 0.92 | 5.03 | 1.79 | 5.25 | 1.87 | 5.24 | 1.33 | 1.29 | 0.31 | 7.42 | 4.58 | 1.01 | 7.30 | 9816 | 272 | 51630017 | 4.24 | 55.60 | 40.15 | 106187425 |
Wenzhou | 浙江温州 | 29.5 | 786.80 | 107.92 | 0.11 | 0.22 | 50.70 | 1.38 | 20.21 | 2.97 | 0.54 | 2.33 | 1.26 | 2.90 | 1.53 | 1.33 | 0.67 | 0.44 | 0.08 | 8.71 | 4.03 | 0.80 | 7.71 | 11786 | 175 | 29250426 | 3.20 | 52.43 | 44.37 | 44948701 |
Jiaxing | 浙江嘉兴 | 37.4 | 341.60 | 80.40 | 0.09 | 52.20 | 1.53 | 2.75 | 1.17 | 0.45 | 2.50 | 0.94 | 1.94 | 1.34 | 2.12 | 0.73 | 0.73 | 0.12 | 4.84 | 2.51 | 0.41 | 4.04 | 3915 | 85 | 23002027 | 5.52 | 58.24 | 36.24 | 51028508 | |
Huzhou | 浙江湖州 | 24.4 | 259.98 | 37.91 | 0.02 | 0.64 | 19.17 | 0.59 | 4.66 | 0.72 | 0.29 | 0.98 | 0.38 | 1.30 | 0.40 | 0.32 | 0.40 | 0.53 | 0.02 | 2.81 | 1.52 | 0.19 | 2.94 | 5818 | 78 | 13017294 | 8.01 | 54.93 | 37.07 | 26665326 |
Shaoxing | 浙江绍兴 | 39.1 | 438.91 | 107.13 | 0.03 | 0.22 | 37.50 | 1.16 | 46.14 | 1.44 | 0.49 | 2.20 | 0.76 | 1.95 | 0.51 | 1.20 | 0.56 | 0.76 | 0.04 | 5.20 | 2.71 | 0.35 | 3.89 | 8279 | 100 | 27952029 | 5.35 | 56.05 | 38.60 | 67971868 |
Jinhua | 浙江金华 | 43.1 | 466.65 | 52.50 | 0.10 | 0.05 | 15.39 | 0.92 | 10.11 | 1.72 | 0.62 | 1.90 | 0.82 | 2.23 | 0.47 | 1.41 | 0.46 | 1.24 | 0.03 | 5.43 | 2.87 | 0.44 | 6.29 | 10941 | 72 | 21100441 | 5.12 | 51.47 | 43.41 | 34117890 |
Zhoushan | 浙江舟山 | 6.1 | 96.77 | 16.80 | 0.05 | 0.15 | 4.40 | 0.51 | 1.57 | 1.23 | 0.23 | 0.44 | 0.41 | 0.68 | 0.50 | 1.29 | 0.25 | 0.28 | 0.03 | 1.38 | 0.91 | 0.18 | 2.31 | 1440 | 52 | 6443170 | 9.63 | 45.52 | 44.85 | 9790517 |
Taizhouzj | 浙江台州 | 30.8 | 583.14 | 69.78 | 0.48 | 0.02 | 22.87 | 1.05 | 18.86 | 1.34 | 0.60 | 2.00 | 0.80 | 3.40 | 0.76 | 1.39 | 0.65 | 0.71 | 0.14 | 5.92 | 3.07 | 0.37 | 5.35 | 9411 | 116 | 24264533 | 6.61 | 51.69 | 41.69 | 36307993 |
Lishui | 浙江丽水 | 9.0 | 259.65 | 16.81 | 0.24 | 0.09 | 3.37 | 0.80 | 0.92 | 0.65 | 0.31 | 0.38 | 0.17 | 0.99 | 0.11 | 0.41 | 0.26 | 0.38 | 0.02 | 2.74 | 1.42 | 0.25 | 3.30 | 17298 | 32 | 6632932 | 9.49 | 49.54 | 40.97 | 11390737 |
Hefei | 安徽合肥 | 34.1 | 494.95 | 71.10 | 0.10 | 17.79 | 0.89 | 14.25 | 4.52 | 0.97 | 4.62 | 1.28 | 2.40 | 1.59 | 1.03 | 2.60 | 1.05 | 0.18 | 7.66 | 3.25 | 1.04 | 5.88 | 7047 | 326 | 27016100 | 4.91 | 53.92 | 41.17 | 37990180 | |
Wuhu | 安徽芜湖 | 28.6 | 229.50 | 26.71 | 0.04 | 0.04 | 11.34 | 0.49 | 3.69 | 1.78 | 0.19 | 0.56 | 0.30 | 0.85 | 0.19 | 0.09 | 0.51 | 0.42 | 0.02 | 2.53 | 1.27 | 0.09 | 2.31 | 3317 | 135 | 11085924 | 4.44 | 65.18 | 30.38 | 22510125 |
Bengbu | 安徽蚌埠 | 12.4 | 362.23 | 17.25 | 0.21 | 4.46 | 0.57 | 0.86 | 1.13 | 0.15 | 0.57 | 0.11 | 0.90 | 0.16 | 0.19 | 0.60 | 0.47 | 0.03 | 3.17 | 1.30 | 0.15 | 2.22 | 5941 | 105 | 6368877 | 18.84 | 47.25 | 33.91 | 7726842 | |
Huainan | 安徽淮南 | 53.3 | 243.99 | 32.70 | 0.32 | 12.99 | 3.46 | 1.55 | 2.90 | 0.96 | 0.13 | 0.64 | 0.24 | 0.84 | 0.89 | 1.24 | 0.34 | 0.54 | 0.02 | 2.63 | 1.16 | 0.13 | 1.72 | 2585 | 97 | 6035491 | 7.78 | 64.42 | 27.80 | 7889304 |
Maanshan | 安徽马鞍山 | 55.4 | 129.10 | 15.42 | 0.03 | 1.48 | 6.93 | 0.38 | 0.89 | 0.34 | 0.07 | 0.36 | 0.04 | 0.61 | 0.04 | 0.32 | 0.26 | 0.17 | 0.01 | 1.38 | 0.62 | 0.12 | 1.37 | 1686 | 78 | 8110148 | 3.51 | 69.49 | 27.00 | 13575819 |
Huaibei | 安徽淮北 | 38.6 | 219.56 | 20.84 | 11.31 | 2.82 | 0.50 | 0.27 | 0.41 | 0.10 | 0.16 | 0.06 | 0.36 | 0.03 | 0.07 | 0.11 | 0.12 | 0.02 | 2.03 | 0.81 | 0.06 | 1.60 | 2741 | 63 | 4616043 | 8.76 | 64.64 | 26.61 | 8814120 | |
Tongling | 安徽铜陵 | 18.9 | 74.01 | 11.67 | 0.38 | 0.12 | 5.00 | 0.29 | 2.05 | 0.20 | 0.08 | 0.24 | 0.07 | 0.32 | 0.10 | 0.07 | 0.13 | 0.13 | 0.82 | 0.38 | 0.08 | 1.21 | 1113 | 48 | 4667000 | 2.07 | 72.74 | 25.19 | 11042303 | |
Anqing | 安徽安庆 | 18.2 | 615.62 | 24.24 | 1.69 | 0.07 | 3.09 | 1.26 | 1.21 | 0.77 | 0.37 | 0.94 | 0.19 | 1.05 | 0.24 | 0.18 | 0.52 | 0.38 | 0.04 | 5.82 | 1.86 | 0.34 | 4.22 | 15318 | 77 | 9881100 | 15.74 | 53.03 | 31.23 | 13159401 |
Huangshan | 安徽黄山 | 1.6 | 148.05 | 9.41 | 0.10 | 0.01 | 1.11 | 0.20 | 1.11 | 0.41 | 0.20 | 0.20 | 0.51 | 0.64 | 0.12 | 0.08 | 0.16 | 0.25 | 0.02 | 1.43 | 0.72 | 0.10 | 2.04 | 9807 | 44 | 3093198 | 12.72 | 44.09 | 43.20 | 3306897 |
Chuzhou | 安徽滁州 | 11.4 | 450.80 | 17.85 | 0.85 | 0.34 | 3.18 | 0.32 | 1.26 | 0.97 | 0.19 | 0.52 | 0.10 | 0.76 | 0.09 | 0.09 | 0.19 | 0.46 | 0.02 | 3.86 | 1.34 | 0.09 | 3.22 | 13523 | 60 | 6956502 | 21.34 | 49.16 | 29.50 | 10529700 |
Fuyang | 安徽阜阳 | 13.0 | 1011.84 | 29.11 | 0.31 | 1.60 | 3.66 | 0.74 | 3.03 | 1.33 | 0.27 | 1.46 | 0.15 | 1.61 | 0.27 | 0.26 | 0.18 | 0.48 | 0.02 | 6.83 | 2.11 | 0.17 | 4.63 | 9775 | 76 | 7218144 | 27.34 | 39.17 | 33.49 | 7037012 |
Suzhouah | 安徽宿州 | 20.7 | 642.07 | 22.05 | 0.58 | 3.13 | 2.25 | 0.52 | 1.91 | 0.62 | 0.24 | 0.82 | 0.07 | 0.94 | 0.17 | 0.07 | 0.33 | 0.34 | 0.04 | 5.35 | 1.43 | 0.17 | 3.07 | 9787 | 53 | 6505700 | 27.89 | 37.88 | 34.23 | 6769181 |
Chaohu | 安徽巢湖 | 20.1 | 460.51 | 16.89 | 0.21 | 0.17 | 2.16 | 0.40 | 2.75 | 0.30 | 0.16 | 0.75 | 0.20 | 0.56 | 0.20 | 0.35 | 0.20 | 0.30 | 0.02 | 3.82 | 1.28 | 0.10 | 2.96 | 9394 | 39 | 6297332 | 18.64 | 49.46 | 31.90 | 8261743 |
Luan | 安徽六安 | 8.4 | 704.82 | 22.17 | 0.84 | 0.03 | 2.14 | 0.68 | 4.06 | 0.69 | 0.22 | 0.69 | 0.07 | 0.75 | 0.17 | 0.07 | 0.21 | 0.66 | 0.04 | 5.28 | 1.66 | 0.21 | 3.70 | 17976 | 61 | 6761209 | 23.56 | 42.26 | 34.18 | 8327000 |
Bozhou | 安徽亳州 | 6.9 | 600.76 | 16.07 | 0.05 | 0.69 | 2.38 | 0.25 | 1.22 | 0.29 | 0.23 | 1.09 | 0.09 | 0.94 | 0.17 | 0.06 | 0.11 | 0.26 | 0.04 | 4.47 | 1.16 | 0.16 | 2.41 | 8374 | 57 | 5127800 | 26.75 | 37.36 | 35.90 | 3243341 |
Xuancheng | 安徽宣城 | 11.7 | 278.36 | 12.34 | 0.29 | 0.01 | 3.20 | 0.38 | 0.28 | 0.30 | 0.15 | 0.19 | 0.06 | 0.57 | 0.32 | 0.27 | 0.14 | 0.12 | 0.01 | 2.21 | 1.07 | 0.11 | 2.66 | 12323 | 43 | 5257000 | 16.83 | 47.21 | 35.95 | 10677643 |
Fuzhoufj | 福建福州 | 38.0 | 645.90 | 105.47 | 0.73 | 0.14 | 39.80 | 1.62 | 14.34 | 3.92 | 1.19 | 3.91 | 2.10 | 3.06 | 2.67 | 6.11 | 3.06 | 1.34 | 0.28 | 9.51 | 3.88 | 1.47 | 6.34 | 13066 | 220 | 31234092 | 9.05 | 44.88 | 46.06 | 45454115 |
Xiamen | 福建厦门 | 11.8 | 180.21 | 95.33 | 0.28 | 0.01 | 52.41 | 0.78 | 12.82 | 4.04 | 1.13 | 3.32 | 2.60 | 1.39 | 3.68 | 2.00 | 0.54 | 0.66 | 0.82 | 3.71 | 1.81 | 0.57 | 2.76 | 1573 | 230 | 20600737 | 1.12 | 49.73 | 49.15 | 36889483 |
Putian | 福建莆田 | 6.9 | 323.54 | 28.78 | 0.13 | 0.12 | 15.72 | 0.56 | 2.04 | 0.45 | 0.22 | 0.55 | 0.30 | 0.86 | 0.27 | 0.58 | 0.15 | 0.17 | 0.03 | 3.78 | 0.98 | 0.20 | 1.67 | 4119 | 55 | 8503257 | 10.33 | 56.11 | 33.56 | 12665314 |
Quanzhou | 福建泉州 | 39.5 | 685.27 | 142.17 | 0.38 | 1.19 | 92.56 | 1.36 | 21.26 | 1.71 | 0.60 | 2.24 | 1.30 | 1.87 | 0.92 | 0.55 | 0.23 | 0.26 | 0.09 | 8.62 | 2.04 | 0.32 | 4.67 | 11015 | 160 | 35649739 | 3.71 | 60.16 | 36.13 | 62604054 |
Nanping | 福建南平 | 7.5 | 313.90 | 23.54 | 1.09 | 0.21 | 7.68 | 0.89 | 0.83 | 0.68 | 0.37 | 0.74 | 0.37 | 1.13 | 0.37 | 0.35 | 0.35 | 0.54 | 0.05 | 3.45 | 1.48 | 0.28 | 2.68 | 26308 | 26 | 7286525 | 21.89 | 41.83 | 36.28 | 7774451 |
Longyan | 福建龙岩 | 33.6 | 314.37 | 30.55 | 0.52 | 2.13 | 7.72 | 0.86 | 5.12 | 0.65 | 0.30 | 0.82 | 0.25 | 1.30 | 0.26 | 1.75 | 0.34 | 0.24 | 0.03 | 3.70 | 1.47 | 0.20 | 2.89 | 19063 | 38 | 9908973 | 13.01 | 53.25 | 33.74 | 11773399 |
Ningde | 福建宁德 | 10.3 | 339.37 | 16.55 | 0.33 | 0.06 | 1.84 | 1.11 | 1.34 | 0.72 | 0.28 | 0.63 | 0.21 | 1.01 | 0.14 | 0.18 | 0.24 | 0.34 | 0.05 | 3.72 | 1.33 | 0.16 | 2.86 | 13452 | 19 | 7386099 | 18.50 | 42.95 | 38.56 | 9174539 |
Nanchang | 江西南昌 | 39.9 | 502.25 | 67.83 | 1.73 | 17.16 | 1.62 | 13.33 | 7.86 | 0.67 | 1.49 | 0.32 | 2.23 | 0.43 | 0.74 | 1.48 | 1.61 | 0.16 | 7.23 | 3.03 | 1.25 | 5.49 | 7402 | 208 | 22001059 | 5.48 | 53.27 | 41.25 | 27732021 | |
Jingdezhen | 江西景德镇 | 12.1 | 163.16 | 17.44 | 1.02 | 0.61 | 6.53 | 0.44 | 1.22 | 0.41 | 0.12 | 1.08 | 0.10 | 0.50 | 0.20 | 0.15 | 0.33 | 0.13 | 0.03 | 1.79 | 0.66 | 0.18 | 1.94 | 5256 | 73 | 4615001 | 8.25 | 60.78 | 30.97 | 6871690 |
Pingxiang | 江西萍乡 | 26.0 | 188.09 | 14.11 | 0.07 | 2.52 | 3.33 | 0.43 | 0.57 | 0.27 | 0.18 | 0.14 | 0.06 | 0.58 | 0.10 | 0.04 | 0.14 | 0.35 | 0.01 | 2.00 | 0.79 | 0.08 | 2.45 | 3824 | 42 | 5203900 | 8.13 | 63.31 | 28.56 | 11979370 |
Jiujiang | 江西九江 | 24.9 | 497.91 | 33.95 | 0.89 | 0.24 | 8.91 | 1.26 | 5.03 | 0.84 | 0.35 | 0.61 | 0.25 | 1.12 | 0.24 | 0.99 | 0.66 | 0.46 | 0.08 | 4.91 | 1.92 | 0.31 | 4.88 | 18823 | 89 | 10320647 | 9.50 | 56.17 | 34.33 | 14977463 |
Xinyu | 江西新余 | 29.4 | 118.01 | 10.07 | 0.09 | 0.38 | 4.48 | 0.47 | 0.46 | 0.21 | 0.11 | 0.13 | 0.04 | 0.33 | 0.02 | 0.02 | 0.10 | 0.21 | 0.03 | 1.24 | 0.43 | 0.06 | 1.26 | 3178 | 53 | 6312212 | 6.00 | 63.90 | 30.10 | 11987770 |
Yingtan | 江西鹰潭 | 4.8 | 121.92 | 10.09 | 1.43 | 0.02 | 3.21 | 0.37 | 0.76 | 0.13 | 0.04 | 0.18 | 0.15 | 0.29 | 0.06 | 0.06 | 0.28 | 0.23 | 1.12 | 0.37 | 0.13 | 1.26 | 3560 | 29 | 3448865 | 9.51 | 62.78 | 27.71 | 11802016 | |
Ganzhou | 江西赣州 | 15.2 | 907.27 | 42.43 | 0.76 | 1.33 | 12.16 | 1.39 | 1.86 | 0.90 | 0.35 | 0.63 | 0.23 | 1.59 | 0.33 | 0.27 | 0.51 | 0.87 | 0.03 | 9.01 | 2.77 | 0.31 | 7.13 | 39379 | 76 | 11197412 | 18.92 | 44.36 | 36.72 | 12746325 |
Jian | 江西吉安 | 9.6 | 495.04 | 20.15 | 1.39 | 0.57 | 1.19 | 1.02 | 1.09 | 1.01 | 0.34 | 0.61 | 0.16 | 0.92 | 0.21 | 0.20 | 0.32 | 0.51 | 0.01 | 4.51 | 1.66 | 0.22 | 4.21 | 25283 | 35 | 7205251 | 19.85 | 50.48 | 29.67 | 11349323 |
Yichunjx | 江西宜春 | 24.6 | 557.93 | 28.07 | 0.78 | 2.67 | 6.11 | 0.94 | 1.09 | 0.98 | 0.36 | 0.76 | 0.20 | 1.08 | 0.21 | 0.19 | 0.22 | 0.48 | 0.02 | 4.84 | 1.96 | 0.19 | 4.99 | 18669 | 50 | 8700005 | 18.96 | 56.58 | 24.47 | 12399826 |
Fuzhoujx | 江西抚州 | 4.1 | 403.96 | 21.17 | 0.98 | 0.10 | 4.18 | 0.72 | 2.38 | 0.50 | 0.29 | 0.79 | 0.04 | 0.71 | 0.09 | 0.07 | 0.18 | 0.38 | 0.01 | 4.26 | 1.15 | 0.17 | 4.17 | 18820 | 50 | 6300124 | 19.02 | 49.91 | 31.06 | 7347415 |
Shangrao | 江西上饶 | 17.2 | 740.33 | 29.96 | 2.95 | 0.55 | 4.32 | 0.83 | 1.62 | 0.62 | 0.50 | 1.45 | 0.14 | 1.16 | 0.23 | 0.21 | 0.15 | 0.36 | 0.13 | 6.79 | 2.02 | 0.25 | 5.68 | 22791 | 38 | 9010029 | 16.86 | 50.96 | 32.18 | 11759109 |
Jinan | 山东济南 | 64.9 | 604.08 | 127.80 | 0.11 | 1.85 | 30.76 | 1.89 | 27.43 | 8.88 | 1.73 | 7.03 | 2.60 | 5.94 | 2.83 | 2.84 | 2.50 | 1.32 | 1.14 | 10.35 | 4.64 | 1.64 | 12.32 | 8177 | 347 | 39105271 | 5.50 | 41.87 | 52.62 | 44856080 |
Qingdao | 山东青岛 | 84.7 | 763.64 | 124.11 | 0.52 | 0.20 | 66.60 | 2.12 | 6.69 | 6.31 | 0.62 | 3.91 | 2.06 | 3.92 | 1.94 | 1.56 | 1.78 | 1.52 | 0.16 | 11.05 | 4.39 | 1.06 | 7.70 | 10978 | 282 | 56661900 | 4.89 | 48.69 | 46.43 | 106628345 |
Zibo | 山东淄博 | 89.2 | 422.36 | 62.88 | 0.25 | 4.46 | 26.93 | 1.56 | 7.58 | 0.95 | 0.37 | 2.90 | 0.69 | 1.27 | 0.68 | 0.56 | 0.24 | 0.58 | 0.04 | 5.74 | 2.49 | 0.50 | 5.09 | 5965 | 225 | 28667500 | 3.67 | 61.62 | 34.70 | 77423440 |
Dongying | 山东东营 | 51.1 | 184.87 | 40.10 | 0.59 | 12.25 | 9.02 | 0.28 | 1.35 | 1.38 | 0.51 | 0.70 | 0.66 | 0.72 | 0.16 | 1.31 | 1.72 | 0.33 | 1.46 | 3.11 | 1.07 | 0.11 | 3.37 | 7923 | 108 | 23599400 | 3.70 | 72.55 | 23.74 | 60378601 |
Weifang | 山东潍坊 | 67.9 | 873.78 | 73.99 | 0.34 | 1.16 | 30.95 | 1.61 | 4.65 | 1.20 | 0.49 | 4.30 | 0.69 | 1.84 | 0.66 | 0.38 | 0.69 | 0.82 | 0.07 | 10.41 | 4.51 | 0.33 | 8.89 | 16140 | 140 | 30909200 | 10.69 | 55.66 | 33.65 | 75292226 |
Taian | 山东泰安 | 63.9 | 557.01 | 56.32 | 0.26 | 11.19 | 14.29 | 1.05 | 7.82 | 1.28 | 0.42 | 2.52 | 0.57 | 1.44 | 0.55 | 0.34 | 0.59 | 0.57 | 0.14 | 5.92 | 2.39 | 0.29 | 4.69 | 7762 | 107 | 20516800 | 9.52 | 53.59 | 36.89 | 37700720 |
Linyi | 山东临沂 | 59.5 | 1072.69 | 56.12 | 0.70 | 2.40 | 16.60 | 1.30 | 4.30 | 0.90 | 0.40 | 2.00 | 0.30 | 2.60 | 0.20 | 0.40 | 0.40 | 0.70 | 0.02 | 10.10 | 3.70 | 0.30 | 8.80 | 17191 | 166 | 23999900 | 11.00 | 50.26 | 38.74 | 45935251 |
Heze | 山东菏泽 | 20.7 | 958.80 | 37.21 | 0.42 | 0.66 | 6.19 | 1.25 | 1.82 | 1.10 | 0.15 | 1.30 | 0.24 | 1.35 | 0.10 | 0.34 | 0.22 | 0.94 | 0.03 | 8.60 | 3.29 | 0.27 | 8.94 | 12239 | 77 | 12270900 | 17.94 | 52.85 | 29.20 | 25320325 |
Zhengzhou | 河南郑州 | 76.3 | 963.00 | 108.50 | 0.23 | 7.17 | 20.54 | 3.11 | 19.57 | 3.00 | 1.00 | 5.14 | 2.89 | 4.15 | 2.42 | 2.13 | 2.96 | 1.75 | 0.31 | 12.81 | 5.28 | 2.08 | 11.96 | 7446 | 343 | 40408926 | 3.08 | 56.17 | 40.74 | 59137624 |
Luoyang | 河南洛阳 | 67.0 | 703.54 | 53.92 | 0.21 | 1.51 | 14.83 | 3.66 | 3.81 | 1.81 | 0.20 | 2.29 | 0.71 | 1.99 | 0.48 | 0.94 | 2.25 | 1.02 | 0.21 | 7.31 | 3.11 | 0.47 | 7.11 | 15200 | 181 | 23202460 | 8.09 | 60.18 | 31.74 | 41323063 |
Pingdingshan | 河南平顶山 | 59.7 | 539.59 | 48.45 | 0.12 | 11.83 | 11.05 | 1.08 | 2.82 | 1.12 | 0.15 | 2.10 | 0.47 | 1.66 | 0.30 | 0.96 | 0.40 | 0.73 | 0.03 | 5.40 | 2.04 | 0.34 | 5.85 | 7904 | 71 | 13108394 | 8.75 | 66.33 | 24.92 | 20031593 |
Hebi | 河南鹤壁 | 23.9 | 162.05 | 17.81 | 0.12 | 4.78 | 4.41 | 0.34 | 2.15 | 0.20 | 0.08 | 0.48 | 0.14 | 0.32 | 0.19 | 0.05 | 0.09 | 0.31 | 1.68 | 0.59 | 0.09 | 1.79 | 2182 | 51 | 4291193 | 11.38 | 70.37 | 18.26 | 9890970 | |
Xinxiang | 河南新乡 | 32.4 | 603.86 | 45.69 | 0.63 | 0.40 | 14.67 | 0.97 | 5.44 | 0.93 | 0.15 | 2.17 | 0.41 | 1.03 | 0.45 | 0.45 | 0.71 | 0.53 | 0.10 | 6.82 | 2.76 | 0.30 | 6.77 | 8169 | 97 | 11899408 | 13.21 | 57.69 | 29.10 | 21733234 |
Jiaozuo | 河南焦作 | 37.7 | 368.02 | 32.29 | 0.57 | 3.03 | 9.94 | 1.28 | 1.74 | 0.59 | 0.17 | 1.18 | 0.41 | 1.70 | 0.11 | 0.20 | 0.26 | 0.65 | 0.04 | 3.94 | 1.70 | 0.21 | 4.57 | 4071 | 90 | 12459260 | 8.13 | 68.65 | 23.22 | 28858779 |
Puyang | 河南濮阳 | 16.9 | 409.83 | 31.42 | 0.05 | 6.68 | 3.04 | 0.67 | 6.10 | 0.91 | 0.10 | 0.86 | 0.20 | 0.78 | 0.17 | 1.48 | 0.15 | 0.43 | 0.12 | 4.29 | 1.33 | 0.20 | 3.86 | 4266 | 51 | 7754037 | 13.88 | 66.46 | 19.66 | 13852696 |
Xuchang | 河南许昌 | 29.8 | 489.64 | 28.67 | 0.06 | 2.15 | 8.07 | 0.67 | 2.43 | 0.65 | 0.10 | 0.73 | 0.25 | 0.54 | 0.31 | 0.44 | 0.28 | 0.44 | 0.03 | 4.61 | 1.87 | 0.27 | 4.77 | 4996 | 80 | 13164870 | 11.39 | 68.51 | 20.10 | 24966545 |
Sanmenxia | 河南三门峡 | 112.7 | 230.30 | 24.22 | 0.14 | 5.97 | 3.77 | 0.80 | 2.32 | 0.63 | 0.13 | 1.68 | 0.28 | 0.80 | 0.08 | 0.35 | 0.22 | 0.19 | 0.03 | 2.80 | 0.97 | 0.16 | 2.90 | 10496 | 30 | 8744157 | 8.01 | 68.52 | 23.47 | 20398477 |
Nanyang | 河南南阳 | 155.7 | 1186.69 | 70.48 | 1.16 | 3.26 | 16.37 | 1.60 | 6.80 | 2.18 | 0.63 | 5.59 | 0.91 | 1.94 | 0.75 | 1.40 | 1.28 | 1.31 | 0.18 | 12.16 | 3.94 | 0.56 | 8.46 | 26509 | 92 | 19533562 | 20.54 | 52.07 | 27.39 | 24952130 |
Shangqiu | 河南商丘 | 46.9 | 918.01 | 39.72 | 0.28 | 3.55 | 3.23 | 0.77 | 4.06 | 1.05 | 0.27 | 1.93 | 0.28 | 1.02 | 0.13 | 0.13 | 0.25 | 0.60 | 0.04 | 9.52 | 3.10 | 0.20 | 9.31 | 10704 | 60 | 11437913 | 26.19 | 46.52 | 27.29 | 13594361 |
Xinyang | 河南信阳 | 19.8 | 870.22 | 43.19 | 0.88 | 0.54 | 5.98 | 1.40 | 5.59 | 1.58 | 0.40 | 3.12 | 0.54 | 1.23 | 0.58 | 0.52 | 0.92 | 0.93 | 0.06 | 9.28 | 2.48 | 0.41 | 6.75 | 18847 | 68 | 10918323 | 26.38 | 42.21 | 31.41 | 11175624 |
Zhoukou | 河南周口 | 44.6 | 1224.35 | 45.22 | 1.10 | 6.31 | 1.25 | 4.87 | 0.96 | 0.45 | 3.13 | 0.27 | 1.79 | 0.51 | 0.16 | 0.19 | 0.53 | 0.03 | 10.70 | 2.98 | 0.29 | 9.70 | 11959 | 51 | 12283024 | 29.77 | 45.42 | 24.81 | 15007129 | |
Zhumadian | 河南驻马店 | 71.5 | 886.10 | 41.16 | 0.52 | 0.19 | 6.88 | 1.28 | 6.06 | 1.14 | 0.31 | 2.70 | 0.35 | 0.97 | 0.97 | 0.34 | 0.56 | 0.63 | 0.16 | 8.17 | 2.94 | 0.34 | 6.65 | 15083 | 52 | 10537118 | 27.58 | 41.88 | 30.54 | 11377676 |
Wuhan | 湖北武汉 | 101.4 | 836.73 | 178.46 | 0.79 | 0.08 | 50.26 | 2.00 | 36.70 | 14.92 | 2.22 | 12.30 | 4.69 | 5.50 | 2.92 | 2.11 | 5.74 | 2.31 | 0.71 | 16.90 | 6.51 | 2.06 | 9.74 | 8494 | 500 | 55659300 | 3.06 | 45.51 | 51.44 | 64245900 |
Huangshi | 湖北黄石 | 30.0 | 260.14 | 43.85 | 0.44 | 5.65 | 16.67 | 0.65 | 6.73 | 0.98 | 0.19 | 1.55 | 1.05 | 0.52 | 0.83 | 0.21 | 0.50 | 0.51 | 0.40 | 2.86 | 1.08 | 0.52 | 2.51 | 4586 | 66 | 6901200 | 7.77 | 57.22 | 35.01 | 11975700 |
Shiyan | 湖北十堰 | 20.1 | 353.19 | 43.26 | 0.36 | 0.47 | 17.38 | 1.41 | 2.24 | 1.02 | 0.85 | 5.65 | 0.70 | 0.75 | 0.68 | 0.57 | 0.39 | 0.29 | 0.42 | 3.84 | 1.87 | 0.64 | 3.73 | 23680 | 62 | 7367800 | 10.56 | 54.57 | 34.87 | 12804019 |
Yichang | 湖北宜昌 | 25.0 | 398.55 | 55.73 | 0.28 | 2.31 | 18.74 | 2.95 | 7.27 | 3.03 | 0.53 | 4.46 | 1.17 | 0.95 | 1.26 | 1.12 | 0.80 | 0.52 | 0.31 | 3.79 | 2.27 | 0.51 | 3.46 | 21084 | 92 | 15473200 | 11.41 | 57.53 | 31.07 | 21187359 |
Xiangyang | 湖北襄阳 | 22.0 | 591.07 | 46.53 | 0.60 | 0.35 | 17.36 | 0.99 | 4.07 | 1.31 | 0.27 | 1.65 | 0.47 | 1.41 | 0.38 | 0.16 | 1.11 | 1.25 | 0.06 | 6.12 | 2.77 | 0.32 | 5.88 | 19724 | 107 | 15382700 | 15.26 | 51.89 | 32.85 | 22988086 |
Ezhou | 湖北鄂州 | 24.2 | 108.46 | 18.22 | 0.03 | 0.80 | 7.50 | 0.26 | 3.46 | 0.43 | 0.10 | 0.98 | 0.38 | 0.34 | 0.32 | 0.24 | 0.14 | 0.26 | 0.07 | 1.32 | 0.52 | 0.11 | 0.96 | 1594 | 52 | 3952900 | 13.02 | 58.53 | 28.46 | 6330996 |
Jinmen | 湖北荆门 | 26.1 | 300.40 | 31.44 | 1.08 | 1.12 | 11.83 | 0.65 | 2.34 | 0.84 | 0.46 | 2.22 | 0.42 | 0.78 | 0.61 | 0.70 | 0.36 | 0.44 | 0.47 | 3.10 | 1.38 | 0.20 | 2.44 | 12404 | 50 | 7300700 | 19.87 | 48.37 | 31.76 | 12001085 |
Jinzhou | 湖北荆州 | 11.9 | 658.17 | 42.72 | 3.97 | 0.16 | 14.82 | 0.80 | 3.07 | 1.01 | 0.28 | 0.85 | 0.35 | 1.11 | 0.33 | 0.26 | 0.52 | 0.70 | 0.06 | 5.18 | 3.18 | 0.32 | 5.75 | 14092 | 66 | 8371000 | 27.60 | 38.86 | 33.53 | 8975700 |
Xianning | 湖北咸宁 | 11.1 | 290.96 | 19.58 | 0.39 | 0.15 | 5.64 | 0.48 | 0.98 | 0.54 | 0.12 | 0.33 | 0.27 | 0.52 | 0.25 | 0.26 | 0.45 | 0.64 | 0.08 | 3.36 | 1.58 | 0.11 | 3.43 | 9861 | 63 | 5203300 | 19.41 | 45.70 | 34.90 | 6333953 |
Suizhou | 湖北随州 | 3.2 | 257.91 | 10.92 | 0.06 | 0.11 | 2.82 | 0.13 | 1.57 | 0.25 | 0.07 | 0.40 | 0.14 | 0.30 | 0.06 | 0.02 | 0.10 | 0.37 | 0.01 | 1.96 | 0.89 | 0.09 | 1.57 | 9636 | 43 | 4016600 | 21.55 | 45.23 | 33.22 | 5097629 |
Enshizhou | 湖北恩施州 | 8.2 | ||||||||||||||||||||||||||||
Changsha | 湖南长沙 | 53.0 | 652.40 | 110.56 | 0.05 | 0.04 | 30.51 | 1.61 | 16.99 | 2.84 | 1.69 | 6.65 | 4.40 | 5.31 | 4.16 | 2.13 | 3.81 | 1.72 | 0.52 | 10.98 | 5.72 | 1.92 | 8.55 | 11816 | 272 | 45470573 | 4.44 | 53.60 | 41.96 | 41654294 |
Xiangtan | 湖南湘潭 | 32.1 | 288.98 | 29.57 | 0.81 | 8.32 | 0.49 | 7.45 | 0.60 | 0.12 | 1.76 | 0.32 | 0.85 | 0.50 | 0.21 | 0.21 | 0.25 | 0.03 | 3.02 | 1.28 | 0.11 | 3.24 | 5015 | 73 | 8940050 | 10.74 | 55.86 | 33.40 | 14962525 | |
Hengyang | 湖南衡阳 | 15.4 | 791.62 | 51.69 | 0.03 | 0.12 | 9.69 | 1.01 | 10.95 | 1.30 | 0.41 | 1.06 | 0.73 | 1.90 | 0.74 | 0.58 | 0.70 | 0.75 | 0.07 | 7.71 | 3.26 | 0.42 | 8.07 | 15299 | 99 | 14203377 | 18.62 | 45.46 | 35.92 | 18832469 |
Shaoyang | 湖南邵阳 | 14.6 | 793.97 | 33.47 | 0.56 | 1.37 | 2.96 | 0.96 | 6.75 | 1.48 | 0.38 | 0.59 | 0.12 | 1.39 | 0.43 | 0.39 | 0.31 | 0.51 | 0.02 | 6.09 | 2.54 | 0.16 | 6.46 | 20830 | 49 | 7272893 | 23.89 | 38.23 | 37.89 | 7127007 |
Yueyang | 湖南岳阳 | 21.4 | 565.62 | 55.02 | 3.23 | 0.01 | 18.30 | 0.75 | 7.76 | 1.67 | 0.46 | 1.71 | 0.90 | 1.53 | 0.68 | 0.66 | 0.43 | 0.64 | 0.35 | 5.48 | 2.26 | 0.28 | 7.27 | 15087 | 82 | 15393576 | 14.00 | 54.19 | 31.80 | 27843253 |
Changde | 湖南常德 | 23.5 | 623.11 | 36.81 | 0.10 | 0.53 | 8.09 | 0.81 | 7.55 | 0.59 | 0.47 | 0.91 | 0.31 | 1.10 | 0.59 | 1.21 | 0.27 | 0.76 | 0.12 | 5.23 | 2.36 | 0.31 | 5.50 | 18190 | 76 | 14915686 | 18.78 | 45.94 | 35.28 | 12626665 |
Zhangjiajie | 湖南张家界 | 4.6 | 164.75 | 7.93 | 0.07 | 0.18 | 0.35 | 0.34 | 0.83 | 0.34 | 0.18 | 0.11 | 0.31 | 0.34 | 0.06 | 0.07 | 0.12 | 0.37 | 0.01 | 1.59 | 0.69 | 0.06 | 1.91 | 9516 | 28 | 2424785 | 12.88 | 24.77 | 62.35 | 1089444 |
Yiyang | 湖南益阳 | 15.3 | 476.36 | 24.07 | 0.15 | 4.30 | 0.40 | 3.19 | 0.35 | 0.24 | 0.40 | 0.26 | 1.30 | 0.41 | 0.67 | 0.30 | 0.47 | 0.08 | 3.90 | 1.84 | 0.14 | 5.53 | 12144 | 54 | 7122748 | 22.80 | 40.49 | 36.71 | 8080903 | |
Chenzhou | 湖南郴州 | 28.8 | 502.07 | 28.95 | 0.10 | 3.55 | 4.16 | 1.33 | 1.96 | 0.71 | 0.31 | 0.90 | 0.50 | 0.66 | 0.41 | 0.41 | 0.31 | 0.54 | 0.04 | 4.57 | 2.20 | 0.28 | 6.01 | 19699 | 62 | 10817632 | 11.72 | 54.95 | 33.33 | 14400825 |
Huaihua | 湖南怀化 | 15.3 | 509.72 | 25.62 | 0.40 | 2.61 | 1.39 | 1.58 | 1.27 | 0.40 | 0.37 | 0.28 | 1.04 | 0.30 | 0.45 | 0.40 | 0.70 | 0.11 | 5.06 | 2.27 | 0.26 | 6.35 | 27624 | 52 | 6749227 | 14.44 | 42.80 | 42.76 | 6998921 | |
Xiangxi | 湖南湘西 | 3.6 | ||||||||||||||||||||||||||||
Guangzhou | 广东广州 | 100.5 | 806.14 | 246.37 | 0.60 | 0.04 | 88.64 | 2.44 | 14.61 | 22.57 | 5.29 | 12.21 | 10.11 | 7.96 | 9.13 | 9.93 | 7.68 | 3.36 | 3.05 | 18.31 | 11.20 | 3.61 | 15.63 | 7434 | 952 | 107482828 | 1.75 | 37.24 | 61.01 | 138312477 |
Shaoguan | 广东韶关 | 22.7 | 328.10 | 30.61 | 0.46 | 0.89 | 10.24 | 1.38 | 2.82 | 1.30 | 0.26 | 0.52 | 0.38 | 0.88 | 0.33 | 0.71 | 0.34 | 0.63 | 0.04 | 3.86 | 1.57 | 0.15 | 3.85 | 18463 | 78 | 6831033 | 14.04 | 41.78 | 44.18 | 7733678 |
Shenzhen | 广东深圳 | 38.6 | 259.87 | 253.02 | 0.27 | 0.18 | 123.66 | 1.87 | 13.00 | 16.31 | 5.19 | 13.76 | 7.08 | 10.86 | 12.69 | 12.42 | 5.43 | 1.81 | 1.94 | 7.66 | 5.38 | 1.70 | 11.81 | 1992 | 830 | 95815101 | 0.07 | 47.21 | 52.72 | 185268200 |
Zhuhai | 广东珠海 | 13.0 | 104.74 | 63.15 | 0.74 | 0.03 | 41.76 | 0.45 | 1.93 | 1.45 | 0.88 | 2.13 | 1.27 | 1.85 | 1.39 | 0.93 | 0.42 | 0.81 | 0.18 | 2.25 | 1.09 | 0.45 | 3.14 | 1711 | 124 | 12085958 | 2.68 | 54.77 | 42.55 | 29761820 |
Shantou | 广东汕头 | 24.4 | 524.11 | 32.28 | 0.04 | 0.02 | 9.47 | 0.65 | 3.13 | 1.04 | 0.45 | 1.99 | 0.56 | 1.42 | 0.37 | 0.34 | 0.37 | 0.63 | 0.10 | 5.85 | 1.98 | 0.23 | 3.64 | 2064 | 182 | 12089743 | 5.34 | 56.10 | 38.56 | 18975666 |
Jiangmen | 广东江门 | 30.9 | 392.28 | 44.85 | 0.11 | 22.65 | 0.80 | 3.54 | 0.91 | 0.43 | 1.06 | 0.75 | 1.80 | 0.39 | 0.30 | 0.24 | 0.59 | 0.19 | 4.47 | 2.27 | 0.20 | 4.15 | 9568 | 129 | 15704191 | 7.45 | 55.54 | 37.01 | 38289057 | |
Maoming | 广东茂名 | 27.8 | 747.17 | 30.94 | 1.19 | 0.30 | 3.69 | 0.72 | 4.75 | 0.90 | 0.29 | 0.80 | 0.26 | 0.98 | 0.27 | 0.52 | 0.17 | 0.63 | 0.24 | 8.69 | 2.14 | 0.41 | 3.99 | 11458 | 70 | 14920857 | 18.40 | 39.59 | 42.01 | 13601493 |
Huizhou | 广东惠州 | 23.6 | 337.28 | 80.39 | 0.11 | 0.03 | 57.55 | 0.73 | 2.19 | 1.25 | 0.35 | 1.10 | 0.79 | 1.87 | 0.89 | 0.73 | 0.42 | 0.72 | 0.14 | 4.09 | 1.86 | 0.36 | 5.21 | 11343 | 215 | 17299543 | 5.92 | 58.94 | 35.15 | 39051731 |
Heyuan | 广东河源 | 9.4 | 358.39 | 24.68 | 0.13 | 0.28 | 11.02 | 0.71 | 1.09 | 0.64 | 0.16 | 0.46 | 0.30 | 0.58 | 0.24 | 0.42 | 0.21 | 0.24 | 0.05 | 3.69 | 1.19 | 0.15 | 3.09 | 15642 | 29 | 4751396 | 12.72 | 51.45 | 35.83 | 8327317 |
Yangjiang | 广东阳江 | 15.8 | 282.81 | 18.24 | 0.57 | 0.03 | 2.79 | 0.47 | 3.20 | 0.59 | 0.29 | 0.96 | 0.26 | 0.58 | 0.56 | 0.26 | 0.15 | 0.37 | 0.05 | 3.04 | 1.17 | 0.12 | 2.78 | 7946 | 44 | 6398389 | 21.92 | 42.45 | 35.62 | 6934570 |
Zhongshan | 广东中山 | 17.2 | 149.18 | 29.04 | 17.29 | 0.42 | 0.28 | 1.03 | 0.28 | 0.34 | 0.33 | 1.34 | 0.42 | 0.50 | 0.22 | 0.13 | 0.04 | 2.46 | 1.48 | 0.21 | 2.27 | 1800 | 41 | 18506521 | 2.74 | 58.04 | 39.22 | 50236309 | ||
Yunfu | 广东云浮 | 17.9 | 282.76 | 17.48 | 0.07 | 0.33 | 6.81 | 0.46 | 0.60 | 0.24 | 0.20 | 0.90 | 0.14 | 0.48 | 0.11 | 0.10 | 0.09 | 0.20 | 0.04 | 3.02 | 0.91 | 0.11 | 2.67 | 7779 | 19 | 4009741 | 25.12 | 41.18 | 33.70 | 4663419 |
Nanning | 广西南宁 | 23.4 | 707.37 | 70.55 | 1.54 | 0.15 | 14.43 | 1.11 | 6.78 | 3.18 | 0.90 | 4.50 | 1.43 | 2.62 | 1.64 | 3.23 | 2.54 | 1.51 | 0.16 | 9.77 | 4.43 | 1.14 | 9.49 | 22112 | 215 | 18002613 | 13.58 | 36.21 | 50.21 | 12854044 |
Liuzhou | 广西柳州 | 48.9 | 372.69 | 37.99 | 0.79 | 0.25 | 13.26 | 0.67 | 2.31 | 1.59 | 0.49 | 1.49 | 0.52 | 1.21 | 0.54 | 2.25 | 0.66 | 1.16 | 0.24 | 4.48 | 2.37 | 0.28 | 3.43 | 18617 | 135 | 13153121 | 8.32 | 63.86 | 27.82 | 23887937 |
Guigang | 广西贵港 | 36.8 | 523.81 | 15.31 | 0.21 | 0.04 | 2.49 | 0.40 | 0.66 | 0.75 | 0.07 | 0.49 | 0.01 | 0.45 | 0.04 | 0.15 | 0.25 | 0.42 | 0.02 | 5.01 | 1.42 | 0.05 | 2.38 | 10602 | 56 | 5446571 | 19.84 | 45.58 | 34.58 | 4702986 |
Laibin | 广西来宾 | 21.1 | 260.10 | 11.85 | 0.91 | 0.56 | 2.24 | 0.58 | 0.32 | 0.21 | 0.14 | 0.28 | 0.03 | 0.31 | 0.07 | 0.20 | 0.32 | 0.31 | 0.01 | 2.57 | 1.02 | 0.08 | 1.69 | 13411 | 29 | 4048883 | 24.16 | 47.42 | 28.41 | 3682803 |
Chongqing | 重庆 | 147.5 | 3303.45 | 248.75 | 1.71 | 9.53 | 57.42 | 6.42 | 43.32 | 13.45 | 2.55 | 11.17 | 4.25 | 9.75 | 4.88 | 4.30 | 5.39 | 3.38 | 0.77 | 33.78 | 10.89 | 2.41 | 23.38 | 82829 | 870 | 79255800 | 8.65 | 55.00 | 36.35 | 91435532 |
Chengdu | 四川成都 | 41.6 | 1149.07 | 172.05 | 0.23 | 0.07 | 45.19 | 2.16 | 43.49 | 5.42 | 1.31 | 8.73 | 3.12 | 5.23 | 2.30 | 2.97 | 6.85 | 2.39 | 0.50 | 16.32 | 8.70 | 1.83 | 15.24 | 12132 | 456 | 55513336 | 5.14 | 44.69 | 50.17 | 58097349 |
Zigong | 四川自贡 | 11.5 | 325.96 | 16.54 | 0.06 | 0.62 | 3.65 | 0.40 | 2.54 | 0.84 | 0.24 | 0.39 | 0.10 | 0.98 | 0.30 | 0.07 | 0.24 | 0.25 | 0.03 | 2.42 | 1.28 | 0.11 | 2.02 | 4373 | 80 | 6477251 | 13.07 | 57.25 | 29.67 | 11081674 |
Panzhihua | 四川攀枝花 | 81.2 | 111.38 | 17.18 | 0.12 | 1.90 | 7.99 | 0.47 | 1.12 | 0.36 | 0.12 | 0.21 | 0.08 | 0.58 | 0.04 | 0.07 | 0.11 | 0.26 | 0.01 | 1.31 | 0.62 | 0.12 | 1.69 | 7440 | 55 | 5239883 | 4.10 | 73.79 | 22.11 | 9532489 |
Deyang | 四川德阳 | 12.2 | 389.15 | 25.74 | 0.07 | 0.49 | 8.01 | 0.34 | 5.26 | 0.57 | 0.15 | 0.40 | 0.11 | 1.21 | 0.08 | 0.10 | 0.26 | 0.54 | 0.03 | 3.38 | 1.58 | 0.09 | 3.07 | 5911 | 54 | 9212679 | 16.54 | 57.82 | 25.63 | 16014090 |
Mianyang | 四川绵阳 | 16.6 | 541.87 | 34.56 | 0.16 | 0.01 | 12.11 | 0.87 | 3.32 | 0.94 | 0.29 | 0.65 | 0.22 | 1.54 | 0.27 | 0.15 | 1.99 | 0.77 | 0.04 | 5.03 | 1.94 | 0.16 | 4.10 | 20249 | 103 | 9602153 | 17.34 | 48.77 | 33.89 | 12675301 |
Guiyang | 贵州贵阳 | 40.4 | 337.16 | 70.34 | 0.30 | 1.01 | 14.90 | 1.42 | 16.79 | 2.44 | 1.14 | 5.13 | 1.74 | 1.77 | 2.60 | 1.86 | 1.68 | 1.04 | 0.73 | 5.92 | 2.80 | 0.93 | 6.14 | 8034 | 162 | 11218174 | 5.09 | 40.73 | 54.18 | 10583479 |
Zunyi | 贵州遵义 | 23.6 | 784.16 | 30.34 | 0.13 | 0.26 | 5.51 | 1.00 | 1.53 | 1.42 | 0.30 | 1.30 | 0.11 | 1.00 | 0.23 | 0.38 | 0.59 | 0.68 | 0.10 | 7.16 | 2.00 | 0.18 | 6.46 | 30762 | 47 | 9087570 | 15.43 | 41.78 | 42.79 | 7937190 |
Kunming | 云南昆明 | 80.9 | 583.99 | 98.96 | 0.80 | 1.49 | 21.21 | 1.54 | 14.62 | 8.24 | 1.76 | 7.05 | 2.85 | 2.96 | 2.10 | 3.59 | 3.56 | 1.21 | 0.59 | 9.29 | 4.20 | 1.56 | 9.54 | 21015 | 275 | 21203700 | 5.67 | 45.32 | 49.01 | 24361300 |
Xian | 陕西西安 | 55.7 | 782.73 | 140.37 | 0.39 | 0.37 | 42.00 | 3.23 | 11.62 | 9.99 | 4.94 | 6.19 | 4.16 | 5.89 | 3.40 | 1.65 | 9.07 | 2.27 | 1.47 | 16.15 | 5.58 | 2.53 | 9.47 | 10108 | 327 | 32414900 | 4.32 | 43.48 | 52.20 | 31253569 |
Baoji | 陕西宝鸡 | 26.6 | 381.09 | 31.65 | 0.50 | 0.65 | 10.85 | 0.92 | 2.08 | 1.98 | 0.34 | 1.71 | 0.32 | 0.89 | 0.14 | 0.11 | 0.50 | 0.51 | 0.05 | 4.42 | 1.99 | 0.22 | 3.47 | 18131 | 118 | 9760900 | 10.68 | 62.95 | 26.38 | 13168575 |
Xianyang | 陕西咸阳 | 26.8 | 520.09 | 37.10 | 0.38 | 1.49 | 9.15 | 1.14 | 3.26 | 0.77 | 0.33 | 0.92 | 0.33 | 1.31 | 0.24 | 0.42 | 0.70 | 1.21 | 0.03 | 7.19 | 2.24 | 0.36 | 5.63 | 10196 | 81 | 10986810 | 18.50 | 52.18 | 29.32 | 13353500 |
Yulin | 陕西榆林 | 54.1 | 364.50 | 25.91 | 0.62 | 3.78 | 1.40 | 1.94 | 0.56 | 1.16 | 0.19 | 0.65 | 0.09 | 0.65 | 0.13 | 0.24 | 0.38 | 1.23 | 0.01 | 4.69 | 1.52 | 0.30 | 6.37 | 43578 | 52 | 17566680 | 5.25 | 68.64 | 26.11 | 19599930 |
Lanzhou | 甘肃兰州 | 43.1 | 323.54 | 51.36 | 0.16 | 1.35 | 12.50 | 1.53 | 8.74 | 1.75 | 0.54 | 1.90 | 0.70 | 2.10 | 0.59 | 1.17 | 2.59 | 0.89 | 0.08 | 5.68 | 1.97 | 1.05 | 6.07 | 13086 | 196 | 11003898 | 3.07 | 48.09 | 48.84 | 15926000 |
Jiayuguan | 甘肃嘉峪关 | 22.9 | 21.80 | 5.01 | 0.01 | 0.01 | 3.55 | 0.10 | 0.02 | 0.04 | 0.04 | 0.05 | 0.04 | 0.13 | 0.01 | 0.04 | 0.02 | 0.12 | 0.01 | 0.23 | 0.12 | 0.05 | 0.42 | 2935 | 50 | 1843192 | 1.34 | 80.16 | 18.50 | 5155800 |
Baiyin | 甘肃白银 | 22.0 | 180.39 | 15.34 | 0.46 | 2.49 | 3.81 | 0.59 | 0.40 | 0.37 | 0.12 | 0.32 | 0.03 | 0.51 | 0.03 | 0.05 | 0.15 | 0.21 | 0.07 | 2.74 | 0.37 | 0.07 | 2.55 | 21158 | 55 | 3111826 | 12.10 | 54.99 | 32.91 | 4299063 |
Wuwei | 甘肃武威 | 4.9 | 191.26 | 9.89 | 1.12 | 0.34 | 1.24 | 0.38 | 0.38 | 0.46 | 0.06 | 0.22 | 0.04 | 0.48 | 0.26 | 0.27 | 0.22 | 0.35 | 0.01 | 1.68 | 0.74 | 0.28 | 1.36 | 33238 | 27 | 2287676 | 26.43 | 40.01 | 33.56 | 1755165 |
Xining | 青海西宁 | 28.0 | 220.87 | 28.65 | 0.14 | 0.39 | 7.25 | 0.51 | 3.37 | 2.34 | 0.58 | 1.39 | 0.38 | 1.25 | 0.56 | 0.48 | 1.41 | 0.65 | 0.06 | 2.92 | 1.76 | 0.43 | 2.78 | 7655 | 67 | 6282800 | 3.89 | 51.05 | 45.05 | 7512207 |
Yinchuan | 宁夏银川 | 95.5 | 158.80 | 30.08 | 1.22 | 5.15 | 4.68 | 2.48 | 1.64 | 0.90 | 0.41 | 0.85 | 0.24 | 1.68 | 0.49 | 1.02 | 0.94 | 0.70 | 0.02 | 2.65 | 1.37 | 0.50 | 3.14 | 9025 | 121 | 7694227 | 5.26 | 50.10 | 44.64 | 9355637 |
Urumchi | 新疆乌鲁木齐 | 59.0 | 243.03 | 47.17 | 1.23 | 1.77 | 7.97 | 1.09 | 5.63 | 5.81 | 0.53 | 2.04 | 1.03 | 1.73 | 0.75 | 1.29 | 1.97 | 0.53 | 0.07 | 4.74 | 2.91 | 1.01 | 5.07 | 13788 | 343 | 13385172 | 1.49 | 44.86 | 53.65 | 16727154 |
Karamay | 新疆克拉玛依 | 61.1 | 37.51 | 15.76 | 0.04 | 7.24 | 3.10 | 0.01 | 1.30 | 0.23 | 0.10 | 0.33 | 0.11 | 0.25 | 0.34 | 0.82 | 0.06 | 0.09 | 0.01 | 0.62 | 0.26 | 0.01 | 0.84 | 9548 | 57 | 7113531 | 0.49 | 89.75 | 9.76 | 13303969 |
Aletai | 新疆阿勒泰 | 4.5 | 19.70 | 33677 | 11481 | 10.6 | 83849.5 | |||||||||||||||||||||||
Bayinguoleng | 新疆巴音郭楞 | 18.0 | ||||||||||||||||||||||||||||
Tulufan | 新疆吐鲁番 | 11.1 | 28 | 27135 | 13589 | 13.4 | 372073 |
Data Records
A total of 365 data records (emissions-socioeconomic inventories) are contained in the datasets. Of these,
182 are emissions inventories for cities (2010) [“China city-level emissions inventory, 2010”, Data Citation 1];
182 are energy inventories for cities (2010) [“China city-level energy inventory, 2010”, Data Citation 1];
1 is a socioeconomic inventory for cities (2010) [“China city-level socioeconomic inventory, 2010”, Data Citation 1];
The cities’ CO2 emissions inventories are constructed at an IPCC territorial administrative scope, including both energy-related emissions (from fossil fuel combustion) and process-related emissions (from cement production). The socioeconomic inventory presents GDP, population, employed population (with structure), GDP (with structure), and area of the 182 cities.
Technical Validation
Uncertainties
CO2 emissions inventories gather the contributions of economic activity to total CO2 emissions for a given time period and area. Inventories are critical to many environmental decision-making processes and scientific goals. Policymaking and scientific research require reliable inventories to ensure the effectiveness of the policy process. In both types of applications, it is important to understand the uncertainty in emissions inventories. Additionally, uncertainty analysis can improve the accuracy of emissions accounts. Regarding the city-level CO2 emissions inventories in this article, the literature shows that uncertainty regarding the process-related emissions in cement production is low. The inventories’ uncertainty mainly depends on energy-related emissions part44,50. The contributing sources of uncertainty for energy-related emissions accounting are associated with emission factors, activity data and other estimation parameters (Volume 1, Chapter 3, Page 6)”41. The uncertainty induced by emissions factors and energy activity data are both quantified for the cities’ emission inventories.
Uncertainties in activity data and emission factors
China’s energy data are of relatively poor quality compared with those of developed countries, especially city-level data. The literature also shows that the uncertainties range widely from sector to sector. The coefficient of variation (CV; the standard deviation divided by the mean) is used to quantify the uncertainty. According to a field survey led by previous studies, the fossil fuel consumed in China’s power generation sector has the lowest CV (5%)51,52, compared with primary industry (30%)53, other manufacturing sectors (10%), construction (10%)41,54, transportation sector (16%)55, and residential energy use (20%)41. The sources of uncertainties could lie in the opaqueness in China’s statistical systems, especially on the “statistical approach on data collection, reporting and validation (Page 673)”56 and the dependence of China’s statistics departments on other government departments. Such uncertainties result in a large gap between China’s national fossil fuel consumption data and the aggregated provincial data. To cover the gap, China has adjusted its energy data three times since 2004, resulting in a gap between the latest national fossil fuel consumption data and provincial aggregated data of 5%57. The gap between city-level aggregated energy consumption and the national overall data could be even larger.
Previous studies have debated China’s emission factors58–61. The range of emission factors across different sources is as high as 40%. This study collects emission factors from Liu, et al.44, which measured them based on a broad investigation of China’s fuel quality. Based on the statistical analysis of surveyed fuel quality, the CVs of coal-, oil-, and gas-related fuels are estimated as 3, 1, and 2%, respectively.
Monte Carlo simulations
Monte Carlo methods are used to simulate the uncertainties resulting from both fossil fuel combustion and emissions factors to estimate the overall uncertainty of the emissions41. Monte Carlo simulations select random values for the emission factor and activity data (fossil fuel consumption) from within their individual normal probability (density) functions and calculate the corresponding emission values (chapter 6 IPCC41). To perform Monte Carlo simulations, we first set up probability density functions for each input variable (emission factor and activity data). Both variables are assumed to follow a normal distribution44. Then, we randomly sample both the activity data and the emission factors 20,000 times and obtain 20,000 CO2 emission estimations. The uncertainties are obtained at a 97.5% confidence level and are calculated as the 97.5% confidence intervals of the estimates.
This article finds that the average uncertainties in the cities’ total CO2 emissions range from −;3.65 to 3.67% at a 97.5% confidence level (±47.5% confidence interval around the estimate). Hegang in Heilongjiang has the highest uncertainties in emissions of (−5.83, 5.86%), while Huizhou in Guangxi has the lowest value of (−0.91, 0.91%).
Limitations and future work
The cities’ emission inventories have some limitations that could lead to more uncertainty. Although these uncertainties may not be large enough to quantify, they are an indispensable component of the emission inventories’ uncertainties. First, this study only takes the energy-related and process-related emissions from seven industrial production processes into account in the emission accounts, and emissions emitted by other sources is missing, such as “agriculture”, “land-use change and forestry”, “waste”, and other industrial processes. Thus, the analysis incomplete. In the future, we will expand the emission scope to achieve more complete inventories for cities. Second, the cities’ emission factors for fossil fuels and industrial processes are substituted by national average emission factors during the process of accounting for cities’ CO2 emissions, resulting in inaccuracy. We hope that specific city-level emissions factors could be updated in the future to increase the accuracy of our results. If not, in our future research, we could employ provincial emission factors to obtain a more accurate emission inventory for the provinces. Third, due to the poor data quality for the cities, the EBTs of most cities are a downscaled version of the provincial table, assuming that the cities have the same sectoral energy intensity and per capita residential energy consumption with their provinces. Such assumptions bring additional uncertainties to cities’ emission inventories. In the future, a consistent time-series emission inventory dataset for Chinese cities will be completed. We will integrate the bottom-up estimations (calculated based on survey data from enterprises)14 and satellite observations to achieve more emission accounts for these cities. More specifically, the high-resolution bottom-up emissions and satellite images can confirm some of the cities’ emission sources (i.e. some super-emitting points). The night-light data will also be used to verify our top-down emissions inventories16,62.
Additional information
How to cite this article: Rashid, H. et al. An emissions-socioeconomic inventory of Chinese cities. Sci. Data. 6:190027 https://doi.org/10.1038/sdata.2019.27 (2019).
Publisher’s note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Supplementary Material
Acknowledgments
The authors acknowledge the efforts and “crowd-sourcing” work of the Applied Energy Summer School 2017 and 2018 held in Nanjing Normal University and Tsinghua University. This work was supported by the National Key R&D Programme of China (2016YFA0602604), the Natural Science Foundation of China (71533005, 71874097, 71503156, 91846301, 41629501, 41501605, 71873059, 71503156, 71773075, 71503168, and 71373153), the National Social Science Foundation of China (15CJY058), Chinese Academy of Engineering (2017-ZD-15-07), the UK Natural Environment Research Council (NE/N00714X/1 and NE/P019900/1), the Economic and Social Research Council (ES/L016028/1), the Royal Academy of Engineering (UK-CIAPP/425).
Footnotes
The authors declare no competing interests.
Data Citations
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Data Citations
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