Regional-Level Carbon Allocation in China Based on Sectoral Emission Patterns under the Peak Commitment
Abstract
:1. Introduction
2. Model and Data
2.1. Data
2.1.1. Classification of Sectors
2.1.2. Data Sources and Processing
2.2. Model
2.2.1. National Carbon Allocations
2.2.2. Sectoral Carbon Allocations at the National Level
- (1)
- Conduct cluster analysis of the sectoral carbon emission structure of major countries (or regions), and extract typical target models.
- (2)
- Select a representative country in each target model.
- (3)
- Set the ratio of sectoral carbon emissions to national carbon emissions as the representative indicator. Assuming the representative indicator changes at a constant rate, the value of the representative indicator during the allocation period can be expressed using the following formula.
- (4)
- Allocate NCAs to all sectors using Equation (2).
2.2.3. Provincial Carbon Allocations
3. Simulation and Discussion
3.1. China's National Carbon Allocations during the Peak Commitment Period
3.2. China's SCANs during the Peak Commitment Period
3.2.1. Selection of Principles for SCAN allocation
3.2.2. Sectoral Emission Patterns in Major Countries or Regions
3.2.3. Comparison of SCANs under Different Target Patterns
3.3. China’s SCAPs and PCAs during the Peak Commitment Period
3.3.1. Provincial Preferences for Carbon Emission Patterns
3.3.2. Peaking Pressure at the Provincial Level
3.4. Sensitivity Analysis
4. Conclusions
Acknowledgments
Author Contributions
Conflicts of Interest
Appendix A
Definition | Abbreviation |
---|---|
United Nations Framework Convention on Climate Change | UNFCCC |
National carbon allocations | NCAs |
Provincial carbon allocations | PCAs |
Sectoral carbon allocations at the national level | SCANs |
Sectoral carbon allocations at the provincial level | SCAPs |
Appendix B
17 Sectors | 5 Sectors | 42 Sectors | ||
---|---|---|---|---|
Commercial catering Public utilities and residential services Finance and insurance Other services | Residential buildings and commercial and public services | Information transmission, computer services and software Wholesale and retail trade Accommodation and catering Financial industry Real estate industry Leasing and business services Research and experimental development Integrated technical service industry Water conservancy, environment 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 | ||
Food industry Textile, sewing and leather products manufacturing Other manufacturing industries Coking, gas and petroleum processing industries Chemical industry Building materials and other non-metallic mineral products industry Metal product manufacturing Machinery and equipment manufacturing Construction industry | Manufacturing and construction | Food manufacturing and tobacco processing Textile industry Textile, clothing, footwear, leather, down and related products Wood processing and furniture manufacturing Paper and printing and cultural, educational and sporting goods manufacturing industry Petroleum processing, coking and nuclear fuel processing Chemical industry Non-metallic mineral products industry Metal smelting and rolling processing industry Metal products industry General, special equipment manufacturing industry Transportation equipment manufacturing Electrical machinery and equipment Communications equipment, computers and other electronic equipment manufacturing Instruments, cultural and office machinery manufacturing Handicrafts and other manufacturing industries Waste materials Construction industry | ||
Transportation, post and telecommunications services | Transport | Transportation and Warehousing Postal service | ||
Electricity and steam, hot water production and supply | Electricity and heat production | Electricity, heat production and supply Gas production and supply Water production and supply | ||
Agriculture | “other” | Agriculture, forestry, animal husbandry and fishery | ||
Mining industry | Coal mining and washing industry Petroleum and natural gas industries Mining and dressing of metals Non-metallic mineral and other mining industry |
Appendix C. Provincial Preference for Target Patterns for Different Perturbations on γ
Region | Pattern T | Pattern R | Pattern M | Region | Pattern T | Pattern R | Pattern M | ||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Mean | SD | Mean | SD | Mean | SD | Mean | SD | Mean | SD | Mean | SD | ||
Beijing # | 1.45 | 0.01 | 1.48 * | 0.01 | 1.29 | 0.02 | Henan | 2.41 | 0.05 | 2.41 | 0.04 | 2.42 * | 0.05 |
Tianjin # | 0.75 | 0.02 | 0.76 * | 0.02 | 0.75 | 0.02 | Hubei # | 1.851 * | 0.07 | 1.83 | 0.06 | 1.848 | 0.07 |
Hebei # | 2.59 * | 0.04 | 2.54 | 0.04 | 2.43 | 0.05 | Hunan | 1.64 * | 0.05 | 1.63 | 0.04 | 1.62 | 0.05 |
Shanxi # | 1.31 * | 0.05 | 1.27 | 0.05 | 1.29 | 0.04 | Guangdong | 5.34 | 0.06 | 5.36 | 0.06 | 5.52 * | 0.08 |
Inner Mongolia # | 1.75 * | 0.05 | 1.67 | 0.05 | 1.64 | 0.05 | Guangxi | 1.10 * | 0.02 | 1.08 | 0.02 | 1.08 | 0.02 |
Liaoning # | 1.82 | 0.03 | 1.826 | 0.03 | 1.832 * | 0.02 | Hainan # | 0.210 * | 0.00 | 0.206 | 0.00 | 0.19 | 0.00 |
Jilin # | 0.64 | 0.01 | 0.66 * | 0.01 | 0.64 | 0.01 | Chongqing | 0.81 * | 0.03 | 0.80 | 0.03 | 0.80 | 0.04 |
Heilongjiang | 1.055 * | 0.01 | 1.050 | 0.01 | 1.01 | 0.01 | Sichuan | 1.775 * | 0.03 | 1.76 | 0.03 | 1.767 | 0.03 |
Shanghai # | 1.91 | 0.03 | 1.94 * | 0.03 | 1.90 | 0.05 | Guizhou # | 0.84 * | 0.02 | 0.80 | 0.02 | 0.82 | 0.02 |
Jiangsu # | 3.61 | 0.11 | 3.70 | 0.10 | 3.93 * | 0.11 | Yunnan # | 0.815 | 0.02 | 0.81 | 0.02 | 0.819 * | 0.02 |
Zhejiang | 2.68 | 0.22 | 2.76 | 0.20 | 2.77 * | 0.22 | Shaanxi # | 0.98 * | 0.02 | 0.96 | 0.02 | 0.95 | 0.02 |
Anhui | 1.217 | 0.02 | 1.219 * | 0.02 | 1.19 | 0.02 | Gansu # | 0.57 * | 0.02 | 0.56 | 0.01 | 0.55 | 0.02 |
Fujian | 1.69 * | 0.02 | 1.67 | 0.02 | 1.63 | 0.03 | Qinghai | 0.2322 * | 0.01 | 0.22 | 0.01 | 0.2320 | 0.01 |
Jiangxi | 0.88 | 0.02 | 0.88 | 0.01 | 0.89 * | 0.02 | Ningxia | 0.294 * | 0.01 | 0.28 | 0.01 | 0.289 | 0.01 |
Shandong # | 3.52 | 0.07 | 3.57 | 0.07 | 3.66 * | 0.06 | Xinjiang | 0.475 | 0.01 | 0.477 * | 0.01 | 0.44 | 0.01 |
Region | Pattern T | Pattern R | Pattern M | Region | Pattern T | Pattern R | Pattern M | ||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Mean | SD | Mean | SD | Mean | SD | Mean | SD | Mean | SD | Mean | SD | ||
Beijing # | 1.41 | 0.03 | 1.45 * | 0.03 | 1.31 | 0.04 | Henan | 2.301 | 0.09 | 2.302 * | 0.09 | 2.22 | 0.13 |
Tianjin # | 0.745 | 0.06 | 0.752 * | 0.05 | 0.71 | 0.06 | Hubei # | 1.714 * | 0.06 | 1.69 | 0.06 | 1.707 | 0.05 |
Hebei # | 2.41 * | 0.05 | 2.38 | 0.04 | 2.33 | 0.04 | Hunan | 1.70 * | 0.05 | 1.68 | 0.05 | 1.68 | 0.05 |
Shanxi # | 1.26 * | 0.11 | 1.23 | 0.10 | 1.21 | 0.10 | Guangdong | 6.16 * | 0.29 | 6.08 | 0.27 | 6.14 | 0.26 |
Inner Mongolia # | 1.59 * | 0.08 | 1.53 | 0.08 | 1.48 | 0.10 | Guangxi | 1.050 * | 0.03 | 1.045 | 0.03 | 1.045 | 0.03 |
Liaoning # | 1.78 | 0.05 | 1.78 | 0.05 | 1.83 * | 0.05 | Hainan # | 0.23 * | 0.02 | 0.22 | 0.02 | 0.21 | 0.01 |
Jilin # | 0.62 | 0.02 | 0.63 * | 0.03 | 0.60 | 0.03 | Chongqing | 0.76 * | 0.02 | 0.75 | 0.02 | 0.75 | 0.02 |
Heilongjiang | 1.07 * | 0.05 | 1.06 | 0.05 | 1.04 | 0.05 | Sichuan | 1.74 | 0.10 | 1.76 | 0.11 | 1.77 * | 0.12 |
Shanghai # | 1.80 | 0.06 | 1.83 * | 0.06 | 1.78 | 0.05 | Guizhou # | 0.87 * | 0.03 | 0.83 | 0.03 | 0.86 | 0.04 |
Jiangsu # | 3.36 | 0.13 | 3.46 | 0.12 | 3.58 * | 0.14 | Yunnan # | 0.76 | 0.02 | 0.76 | 0.02 | 0.77 * | 0.02 |
Zhejiang | 3.11 | 0.55 | 3.16 | 0.52 | 3.40 * | 0.60 | Shaanxi # | 0.98 * | 0.02 | 0.97 | 0.02 | 0.95 | 0.02 |
Anhui | 1.14 | 0.03 | 1.15 * | 0.02 | 1.11 | 0.03 | Gansu # | 0.55 * | 0.02 | 0.54 | 0.02 | 0.53 | 0.02 |
Fujian | 1.64 * | 0.04 | 1.62 | 0.04 | 1.57 | 0.04 | Qinghai | 0.20 | 0.02 | 0.20 | 0.01 | 0.21 * | 0.01 |
Jiangxi | 0.898 * | 0.03 | 0.895 | 0.03 | 0.88 | 0.03 | Ningxia | 0.26 * | 0.01 | 0.25 | 0.01 | 0.25 | 0.01 |
Shandong # | 3.62 | 0.10 | 3.70 | 0.10 | 3.79 * | 0.14 | Xinjiang | 0.50 * | 0.02 | 0.49 | 0.02 | 0.46 | 0.01 |
Region | Pattern T | Pattern R | Pattern M | Region | Pattern T | Pattern R | Pattern M | ||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Mean | SD | Mean | SD | Mean | SD | Mean | SD | Mean | SD | Mean | SD | ||
Beijing # | 1.68 | 0.16 | 1.69 * | 0.15 | 1.55 | 0.17 | Henan | 2.44 | 0.28 | 2.49 * | 0.29 | 2.39 | 0.30 |
Tianjin # | 0.68 | 0.07 | 0.70 * | 0.07 | 0.69 | 0.07 | Hubei # | 2.32 * | 0.29 | 2.26 | 0.27 | 2.21 | 0.25 |
Hebei # | 2.06 * | 0.26 | 2.04 | 0.25 | 1.96 | 0.29 | Hunan | 2.03 | 0.30 | 1.97 | 0.28 | 2.04 * | 0.34 |
Shanxi # | 1.03 * | 0.12 | 1.00 | 0.12 | 0.97 | 0.14 | Guangdong | 4.32 | 0.60 | 4.50 | 0.54 | 4.65 * | 0.54 |
Inner Mongolia # | 1.11 * | 0.26 | 1.07 | 0.24 | 1.04 | 0.21 | Guangxi | 1.10 | 0.10 | 1.10 | 0.09 | 1.16 * | 0.09 |
Liaoning # | 2.03 | 0.15 | 2.05 | 0.16 | 2.14 * | 0.23 | Hainan # | 0.244 * | 0.02 | 0.239 | 0.02 | 0.23 | 0.02 |
Jilin # | 0.60 | 0.03 | 0.61 * | 0.03 | 0.58 | 0.03 | Chongqing | 0.84 | 0.09 | 0.85 | 0.09 | 0.96 * | 0.15 |
Heilongjiang | 0.93 | 0.13 | 0.96 | 0.12 | 0.98 * | 0.10 | Sichuan | 1.75 * | 0.13 | 1.73 | 0.12 | 1.62 | 0.12 |
Shanghai # | 1.63 | 0.12 | 1.66 * | 0.12 | 1.63 | 0.12 | Guizhou # | 0.69 * | 0.15 | 0.65 | 0.14 | 0.65 | 0.16 |
Jiangsu # | 3.98 | 0.79 | 3.91 | 0.73 | 4.02 * | 0.86 | Yunnan # | 0.62 | 0.14 | 0.64 | 0.13 | 0.65 * | 0.14 |
Zhejiang | 5.53 * | 1.34 | 5.50 | 1.25 | 5.29 | 1.22 | Shaanxi # | 0.733 * | 0.08 | 0.731 | 0.08 | 0.71 | 0.08 |
Anhui | 1.29 * | 0.10 | 1.28 | 0.09 | 1.22 | 0.11 | Gansu # | 0.61 * | 0.05 | 0.60 | 0.04 | 0.60 | 0.05 |
Fujian | 1.77 | 0.20 | 1.78 | 0.20 | 1.89 * | 0.21 | Qinghai | 0.22 | 0.02 | 0.21 | 0.02 | 0.23 * | 0.02 |
Jiangxi | 0.70 | 0.07 | 0.72 | 0.06 | 0.73 * | 0.07 | Ningxia | 0.26 | 0.04 | 0.25 | 0.04 | 0.27 * | 0.05 |
Shandong # | 2.50 | 0.53 | 2.54 | 0.51 | 2.68 * | 0.47 | Xinjiang | 0.465 | 0.04 | 0.469 * | 0.03 | 0.44 | 0.03 |
Appendix D. Provincial Peaking Years for Different Perturbations on γ
Region | Pattern T | Pattern R | Pattern M | Region | Pattern T | Pattern R | Pattern M | ||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Mean | SD | Mean | SD | Mean | SD | Mean | SD | Mean | SD | Mean | SD | ||
Beijing | 2030 | 0.00 | 2030 | 0.00 | 2030 | 0.00 | Henan | 2030 | 0.59 | 2030 | 0.00 | 2030 | 0.00 |
Tianjin | 2030 | 0.10 | 2030 | 0.00 | 2030 | 0.00 | Hubei | 2030 | 0.46 | 2029 | 0.44 | 2030 | 0.45 |
Hebei | 2030 | 0.00 | 2030 | 0.00 | 2030 | 0.00 | Hunan | 2030 | 0.00 | 2030 | 0.38 | 2030 | 0.00 |
Shanxi | 2030 | 0.00 | 2029 | 1.14 | 2029 | 0.34 | Guangdong | 2025 | 0.62 | 2025 | 0.81 | 2030 | 0.49 |
Inner Mongolia | 2030 | 0.00 | 2030 | 0.17 | 2029 | 0.00 | Guangxi | 2030 | 0.34 | 2029 | 0.92 | 2029 | 0.17 |
Liaoning | 2030 | 0.24 | 2030 | 0.17 | 2030 | 0.00 | Hainan | 2030 | 0.00 | 2030 | 0.00 | 2030 | 0.00 |
Jilin | 2030 | 0.00 | 2030 | 0.00 | 2030 | 0.00 | Chongqing | 2030 | 0.00 | 2030 | 0.00 | 2030 | 0.00 |
Heilongjiang | 2030 | 0.00 | 2030 | 0.00 | 2030 | 0.00 | Sichuan | 2029 | 0.61 | 2029 | 0.85 | 2030 | 0.38 |
Shanghai | 2030 | 0.00 | 2030 | 0.00 | 2030 | 0.00 | Guizhou | 2030 | 0.41 | 2025 | 0.58 | 2028 | 0.00 |
Jiangsu | 2021 | 0.77 | 2024 | 1.24 | 2030 | 0.00 | Yunnan | 2027 | 0.50 | 2027 | 0.28 | 2030 | 0.44 |
Zhejiang | 2024 | 0.93 | 2025 | 1.39 | 2030 | 0.00 | Shaanxi | 2030 | 0.14 | 2030 | 0.59 | 2030 | 0.50 |
Anhui | 2030 | 0.00 | 2030 | 0.00 | 2030 | 0.00 | Gansu | 2030 | 0.00 | 2030 | 0.32 | 2030 | 0.00 |
Fujian | 2030 | 0.00 | 2030 | 0.00 | 2030 | 0.00 | Qinghai | 2027 | 0.73 | 2023 | 0.62 | 2028 | 0.00 |
Jiangxi | 2030 | 0.00 | 2030 | 0.00 | 2030 | 0.00 | Ningxia | 2029 | 0.58 | 2024 | 0.66 | 2028 | 0.00 |
Shandong | 2026 | 0.99 | 2028 | 0.74 | 2030 | 0.00 | Xinjiang | 2030 | 0.00 | 2030 | 0.00 | 2030 | 0.00 |
Region | Pattern T | Pattern R | Pattern M | Region | Pattern T | Pattern R | Pattern M | ||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Mean | SD | Mean | SD | Mean | SD | Mean | SD | Mean | SD | Mean | SD | ||
Beijing | 2030 | 0.00 | 2030 | 0.00 | 2030 | 0.00 | Henan | 2029 | 1.06 | 2029 | 0.82 | 2030 | 0.00 |
Tianjin | 2030 | 0.54 | 2029 | 1.51 | 2030 | 0.76 | Hubei | 2029 | 0.98 | 2028 | 1.02 | 2027 | 1.31 |
Hebei | 2030 | 0.44 | 2029 | 0.97 | 2029 | 0.43 | Hunan | 2030 | 0.27 | 2030 | 0.38 | 2030 | 0.29 |
Shanxi | 2029 | 0.94 | 2028 | 1.17 | 2029 | 0.51 | Guangdong | 2025 | 1.62 | 2025 | 1.93 | 2030 | 0.64 |
Inner Mongolia | 2030 | 0.00 | 2030 | 0.47 | 2029 | 0.84 | Guangxi | 2030 | 0.44 | 2028 | 1.47 | 2029 | 0.36 |
Liaoning | 2030 | 0.27 | 2029 | 0.82 | 2029 | 0.91 | Hainan | 2030 | 0.00 | 2030 | 0.00 | 2030 | 0.00 |
Jilin | 2030 | 0.86 | 2030 | 1.05 | 2030 | 0.00 | Chongqing | 2030 | 0.10 | 2030 | 0.30 | 2030 | 0.00 |
Heilongjiang | 2030 | 0.00 | 2030 | 0.00 | 2030 | 0.00 | Sichuan | 2028 | 0.82 | 2027 | 1.79 | 2027 | 1.94 |
Shanghai | 2030 | 0.00 | 2030 | 0.00 | 2030 | 0.00 | Guizhou | 2030 | 0.81 | 2025 | 0.61 | 2028 | 1.14 |
Jiangsu | 2020 | 1.61 | 2022 | 2.19 | 2028 | 1.14 | Yunnan | 2027 | 1.35 | 2027 | 0.55 | 2030 | 0.50 |
Zhejiang | 2024 | 1.84 | 2025 | 1.91 | 2030 | 0.00 | Shaanxi | 2029 | 0.96 | 2028 | 1.14 | 2029 | 0.57 |
Anhui | 2030 | 0.00 | 2030 | 0.00 | 2030 | 0.00 | Gansu | 2030 | 0.47 | 2029 | 0.47 | 2030 | 0.38 |
Fujian | 2030 | 0.00 | 2030 | 0.00 | 2030 | 0.00 | Qinghai | 2027 | 1.07 | 2023 | 0.98 | 2028 | 0.52 |
Jiangxi | 2030 | 0.00 | 2030 | 0.00 | 2030 | 0.00 | Ningxia | 2028 | 1.18 | 2024 | 1.39 | 2028 | 1.26 |
Shandong | 2025 | 1.29 | 2028 | 1.35 | 2029 | 1.25 | Xinjiang | 2030 | 0.00 | 2030 | 0.00 | 2030 | 0.00 |
Region | Pattern T | Pattern R | Pattern M | Region | Pattern T | Pattern R | Pattern M | ||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Mean | SD | Mean | SD | Mean | SD | Mean | SD | Mean | SD | Mean | SD | ||
Beijing | 2030 | 0.00 | 2030 | 0.00 | 2030 | 0.22 | Henan | 2027 | 1.47 | 2028 | 1.36 | 2030 | 0.00 |
Tianjin | 2030 | 1.00 | 2029 | 2.65 | 2029 | 0.80 | Hubei | 2029 | 1.57 | 2028 | 1.24 | 2027 | 2.14 |
Hebei | 2030 | 0.82 | 2028 | 2.53 | 2029 | 0.80 | Hunan | 2027 | 2.12 | 2026 | 2.60 | 2029 | 0.44 |
Shanxi | 2029 | 0.60 | 2026 | 2.53 | 2028 | 0.67 | Guangdong | 2023 | 2.94 | 2021 | 3.58 | 2030 | 0.70 |
Inner Mongolia | 2030 | 0.00 | 2030 | 0.50 | 2029 | 0.95 | Guangxi | 2027 | 2.30 | 2025 | 1.70 | 2028 | 0.49 |
Liaoning | 2028 | 1.27 | 2027 | 2.80 | 2029 | 1.80 | Hainan | 2030 | 0.00 | 2030 | 0.00 | 2030 | 0.00 |
Jilin | 2030 | 1.14 | 2030 | 1.20 | 2030 | 0.00 | Chongqing | 2027 | 1.68 | 2027 | 2.09 | 2029 | 0.52 |
Heilongjiang | 2027 | 2.17 | 2027 | 2.56 | 2029 | 0.59 | Sichuan | 2027 | 1.36 | 2026 | 2.32 | 2026 | 2.43 |
Shanghai | 2030 | 0.20 | 2030 | 0.00 | 2030 | 0.00 | Guizhou | 2030 | 0.40 | 2024 | 2.13 | 2028 | 1.49 |
Jiangsu | 2020 | 2.05 | 2022 | 3.30 | 2026 | 3.14 | Yunnan | 2026 | 2.98 | 2027 | 2.11 | 2030 | 0.80 |
Zhejiang | 2024 | 2.25 | 2025 | 2.26 | 2027 | 1.19 | Shaanxi | 2029 | 1.00 | 2028 | 1.13 | 2028 | 0.71 |
Anhui | 2027 | 2.22 | 2027 | 2.47 | 2030 | 0.00 | Gansu | 2027 | 3.38 | 2026 | 2.60 | 2028 | 0.46 |
Fujian | 2027 | 2.52 | 2027 | 2.17 | 2030 | 0.49 | Qinghai | 2026 | 1.41 | 2023 | 2.28 | 2028 | 1.65 |
Jiangxi | 2030 | 0.10 | 2030 | 0.10 | 2030 | 0.31 | Ningxia | 2028 | 2.34 | 2024 | 1.70 | 2027 | 2.31 |
Shandong | 2023 | 3.45 | 2024 | 2.40 | 2029 | 1.41 | Xinjiang | 2026 | 2.83 | 2025 | 4.11 | 2029 | 0.86 |
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Categories | Countries (or Regions) | Characteristics | |
---|---|---|---|
1st Clustering | 2nd Clustering: | ||
Category 1 | / | Cameroon, Ghana, Cambodia, Tanzania, Ethiopia, Nepal, Benin, Congo, Angola, Albania, Costa Rica, Sudan, Kenya, El Salvador, Kyrgyzstan, Guatemala, Georgia, Brazil, Switzerland, Colombia, Ecuador, Botswana, Mozambique, Namibia, Togo, the Democratic Republic of the Congo | Emissions from the transport sector contribute most to total emissions |
Category 2 | / | the United Arab Emirates, Oman, Trinidad and Tobago, Zambia, the Democratic People’s Republic of Korea | Emissions from the manufacturing and construction sectors contribute most to total emissions |
Category 3 | Category 3-1 (Pattern T) | U.S., Canada, Argentina, Bolivia, Chile, Côte d’Ivoire, the Dominican Republic, Algeria, the Arab Republic of Egypt, Eritrea, Gabon, Honduras, Croatia, Haiti, Indonesia, Iraq, Jamaica, Jordan, Lebanon, Libya, Sri Lanka, Morocco, Mexico, Nigeria, Nicaragua, Pakistan, Panama, Peru, Philippines, Saudi Arabia, Senegal, Syrian Arab Republic, Thailand, Tunisia, Uruguay, Bolivarian Republic of Venezuela, Viet Nam, Republic of Yemen | Emissions from electricity and heat production sectors contribute most to total emissions and emissions from the transport sector significantly contribute to total emissions compared with Category 3-2 and Category 3-3 |
Category 3 | Category 3-2 (Pattern R) | EU, Turkey, Armenia, Azerbaijan, Iran (Islamic Republic of), Moldova, Turkmenistan, Uzbekistan, Zimbabwe | Emissions from electricity and heat production sectors contribute most to total emissions and emissions from residential buildings and commercial and public services significantly contribute to total emissions compared with Category 3-1 and Category 3-3 |
Category 3 | Category 3-3 (Pattern M) | China, India, Russian Federation, South Africa, Australia, Bangladesh, Bahrain, Bosnia and Herzegovina, Belarus, Brunei Darussalam, Cuba, Israel, Japan, Kazakhstan, Republic of Korea, Kosovo, Kuwait, Macedonia, Montenegro, Mongolia, Mauritius, Malaysia, Qatar, Singapore, Serbia, Ukraine | Emissions from electricity and heat production sectors contribute most to total emissions and emissions from the manufacturing and construction significantly contribute to total emissions compared with Category 3-1 and Category 3-2 |
Sector | Target Pattern | ||
---|---|---|---|
Pattern T | Pattern R | Pattern M | |
Residential Buildings and Commercial and Public Services | 3.11 | 4.83 * | 2.65 |
Manufacturing and Construction | 8.03 | 9.13 | 13.80 * |
Transport | 10.30 * | 9.21 | 4.03 |
Electricity and Heat Production | 24.05 | 22.09 | 24.75 * |
Other | 0.70 | 0.94 | 0.96 * |
Region | Pattern T | Pattern R | Pattern M | Region | Pattern T | Pattern R | Pattern M |
---|---|---|---|---|---|---|---|
Beijing | 1.45 | 1.49 * | 1.30 | Henan | 2.330 | 2.328 | 2.335 * |
Tianjin | 0.77 | 0.78 * | 0.76 | Hubei | 1.738 * | 1.725 | 1.737 |
Hebei | 2.59 * | 2.55 | 2.43 | Hunan | 1.66 * | 1.65 | 1.65 |
Shanxi | 1.38 * | 1.34 | 1.34 | Guangdong | 5.48 | 5.49 | 5.71 * |
Inner Mongolia | 1.66 * | 1.59 | 1.56 | Guangxi | 1.10 * | 1.09 | 1.09 |
Liaoning | 1.82 | 1.82 | 1.83 * | Hainan | 0.214 * | 0.211 | 0.20 |
Jilin | 0.65 | 0.67 * | 0.65 | Chongqing | 0.76 * | 0.75 | 0.74 |
Heilongjiang | 1.10 * | 1.09 | 1.04 | Sichuan | 1.781 | 1.77 | 1.783 * |
Shanghai | 1.82 | 1.86 * | 1.79 | Guizhou | 0.82 * | 0.79 | 0.81 |
Jiangsu | 3.56 | 3.66 | 3.85 * | Yunnan | 0.80 | 0.80 | 0.81 * |
Zhejiang | 2.90 | 2.95 | 3.06 * | Shanxi | 0.95 * | 0.94 | 0.92 |
Anhui | 1.170 * | 1.168 | 1.11 | Gansu | 0.58 * | 0.57 | 0.56 |
Fujian | 1.66 * | 1.64 | 1.60 | Qinghai | 0.221 | 0.21 | 0.223 * |
Jiangxi | 0.863 * | 0.862 | 0.85 | Ningxia | 0.280 * | 0.27 | 0.277 |
Shandong | 3.60 | 3.65 | 3.73 * | Xinjiang | 0.470 | 0.472 * | 0.43 |
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Zhu, Q.; Tang, W. Regional-Level Carbon Allocation in China Based on Sectoral Emission Patterns under the Peak Commitment. Sustainability 2017, 9, 552. https://doi.org/10.3390/su9040552
Zhu Q, Tang W. Regional-Level Carbon Allocation in China Based on Sectoral Emission Patterns under the Peak Commitment. Sustainability. 2017; 9(4):552. https://doi.org/10.3390/su9040552
Chicago/Turabian StyleZhu, Qianting, and Wenwu Tang. 2017. "Regional-Level Carbon Allocation in China Based on Sectoral Emission Patterns under the Peak Commitment" Sustainability 9, no. 4: 552. https://doi.org/10.3390/su9040552