A Calculation Model of Carbon Emissions Based on Multi-Scenario Simulation Analysis of Electricity Consumption
Abstract
:1. Introduction
2. Literature Review
2.1. Carbon Emission Calculation Methods Based on Indirect Variables
2.2. Carbon Emission Calculation Methods Based on Direct Variables
3. Calculation Model and Scenario Design
3.1. Calculation Model of Carbon Emissions
3.1.1. Total Carbon Emission Model
3.1.2. Carbon Emissions from Electricity Generation to Meet Overall Electricity Demand
- Overall Electricity Demand :The overall electricity demand (OED) is a key parameter in predicting carbon emissions. The electricity consumption per unit output value (ECPUOV) method [32] can be used to predict industrial electricity consumption, and PCEC can be used to predict residential electricity consumption. ECPUOV statistically analyzes the ECPUOV of the three major industries (agriculture, industry, and service sector) and determines the ECPUOV of the three industries according to economic development and industrial structure adjustment. Plus annual residential electricity consumption, it predicts the overall electricity demand (OED) in a designated area [33]. The formula is as follows:
- Carbon Emission Factor :According to the results of the Pearson correlation coefficient analysis in Appendix A, and combined with the experience of industry experts and the studies of [34,35,36,37,38,39], this paper defines the carbon emission factor as a parameter that describes the association of the total carbon emissions in the region with the URICFPG, the PFPG and the CRFPG. The calculation formula of the carbon emission factor is as follows:In order to avoid pseudo regression of data, the sequence of each variable must be stable in the regression analysis of explanatory variables and explained variables. This paper uses Eviews to establish an econometric model for the ADF unit root test and cointegration analysis. See Appendix A for specific analysis and results. Firstly, the stationarity of each variable sequence is determined according to the results of the ADF test, and the nonstationary sequence is transformed into a stationary sequence by difference. The test results show that the carbon emission coefficient and its strongly related factors, such as , and , are stable after the first-order difference (FOD). Therefore, the Johansen cointegration test can be performed between these four variables. The results of the cointegration test show that there is at least one cointegration relationship among the four variables at the 5% confidence level, so regression analysis can still be carried out even in the case of non-stationary series.
3.1.3. Carbon Emissions from Electricity Network Losses
3.2. Design of Multi-Scenario
- REF: No climate change measures are adopted. The technology, installed structure, and structure of power generation remain at the same level as that in the year 2021, and the final electricity demand consistent with this emission scenario is met. Coal power has basically realized clean development. The coal consumption rate of generation has a limited reduction space for the generation has basically realized clean development. coal power and new energy power are developing at a low rate. The installed capacity and power generation of thermal power always dominate. According to the current power supply structure, by 2030, the installed capacity of fossil power will account for 61.8%, and electricity generated from fossil energy will account for 64%. Grid losses remain at the current level.
- LC: The latest industrial planning, clean energy goals, and low carbon policies stipulated by the 14th Five-year Plan of G Province have been realized. Coal power gradually decreases after slow growth. New energy power generation develops at a moderate speed. Technical and management measures for reducing grid losses develop at a moderate speed. By 2030, the percentages of the installed capacity of fossil power and power generated from fossil power are expected to fall to 48.7% and 50.4% respectively.
- SC: This scenario considers constraint factors such as rapid growth of electricity consumption and shortages of gas and water based on the low carbon scenario. China’s electricity consumption will grow rapidly and reach 920 billion kWh in 2025, 1050 billion kWh in 2030, and 1130 billion kWh in 2035. There will be a natural gas shortage, and the rise in natural gas prices will lead to an increase in electricity costs. Thus, annual power generation utilization hours are expected to experience a significant decrease. Moreover, the gap in the power supply from western China will widen. It is estimated that 190 billion kWh of electricity will be sent from western China to eastern China during the 14th Five-year Plan period. Under the above security constraints, coal power will compensate for the electricity gap to achieve electricity balance. On the power supply side, the installed capacity of non-fossil energy and power generated by non-fossil energy will gradually play a dominant role after 2025. By 2030, the percentages of the installed capacity of fossil and power generated from fossil energy are expected to drop to 48.7% and 54.6% respectively. On the electric grid side, the electric grid loss reduction level will be constantly optimized. The electric grid loss rate will be reduced to 3.75% beyond expectations and remain relatively stable.
3.3. Data Source
- Source of data for calculating the carbon emission factor. The data of used in the REF are from the Brief Introduction to the Operation of Electric Power Industry of China Electricity Council. The data of and used in the REF are calculated based on electricity generated from different types of energy and unit standard coal consumption specified in the China Energy Statistical Yearbook. The data of , , , and electricity transmitted according to the West-to-East Power Transmission Project used in the LC and the SC are estimated according to the 14th Five-year Plan for Energy Development of G Province.
- Historical output values and ECPUOV of the three major industries of the national economy used to predict electricity consumption are from the G Province Statistical Yearbook. The output values of the three major industries, unit electricity consumption, and per capita household electricity consumption (PCHEC), which are used in the REF, the LC, and the SC, are all reasonably estimated based on historical data.
- The data of grid loss rate of G province power system used in the REF are from the Energy Statistics of G Province Electricity Trade Association, and the data used in the LC and the SC are reasonably estimated according to the 14th Five-year Plan for Energy Development of G Province.
4. Estimated Carbon Emissions of G Province Power System Based on Designed Scenarios
4.1. Calculation of Key Parameters of the Model
4.1.1. Calculation of Carbon Emission Coefficient
4.1.2. Power Consumption Prediction of the Whole Society
4.1.3. Grid Loss Rate
4.2. Quantitative Evaluation Analysis in Different Scenarios
4.2.1. Reference Scenario
- Estimated Carbon EmissionsCarbon emissions in the REF are shown in Table 5 below.
- Carbon Emission CurveIn the REF, the carbon emission curve of G province’s power system is shown in Figure 2 below. The carbon emissions will gradually increase before 2030, but they will not peak. In 2030, the carbon emissions will stand at 310 million tons, of which 294 million tons are from total electricity consumption and 16 million tons are from grid losses.
4.2.2. Low Carbon Scenario
- Estimated Carbon EmissionsAccording to calculation results of the carbon emission factor model and the carbon emission model for the overall electricity demand (OED), estimated carbon emissions under the LC are shown in Table 6 below.
- Carbon Emission CurveIn the LC, the carbon emission curve of G province’s power system is shown in Figure 3 below. In around 2026, G province will peak carbon emissions. In 2030, G province’s power system carbon emissions will stand at 242 million tons, of which 230 million tons are from total electricity consumption and 12 million tons are from grid losses.
4.2.3. Security Constraint Scenario
- Estimated Carbon EmissionsBased on the hypothesized electric quantity and power supply structure in the security constraint scenario, estimated carbon emissions in the SC are shown in Table 7.
- Carbon Emission CurveIn the SC, the carbon emission curve of G province’s power system is shown in Figure 4 below. carbon emissions from electricity consumption in G province will reach peak carbon emissions in around 2028. In 2025, carbon emissions from electricity consumption in G province will amount to 330 million tons, and in 2030, 294 million tons.
4.2.4. Summary and Discussion of Three Scenarios
5. Discussion
6. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
Abbreviations
EKC | Environment Kuznets Curve |
OED | Overall Electricity Demand |
PCEC | Per Capita Electricity Consumption |
PCHEC | Per Capita Household Electricity Consumption |
URICFPG | Utilization Rate of the Installed Capacity of Fossil Power Generation |
PFPG | Proportion of Fossil Power Generation |
CRFPG | Conversion Rate of Fossil Power Generation |
FOD | First-Order Differential |
SOD | Second-Order Differential |
TOD | Third-Order Differential |
REF | Reference Scenario |
LC | Low Carbon Scenario |
SC | Security Constraint Scenario |
CPUOV | Consumption per Unit Output Value |
ECPUOV | Electricity Consumption per Unit Output Value |
Appendix A. Introduction to Fitting of Carbon Emission Factor
Appendix A.1. Correlation Analysis of Driving Factors
Type of Variable | (X,Y) |
---|---|
URICFPG | 0.9029 |
PFPG | 0.9839 |
CRFPG | −0.8542 |
Electricity Consumption Elasticity Coefficient | −0.4308 |
Energy Consumption Elasticity Coefficient | −0.1581 |
Natural Population Growth Rate (%) | −0.7436 |
Contribution Rate of Tertiary Industry to Regional GDP | −0.8509 |
Growth Rate of Electricity Consumption Per Unit GDP (%) | −0.3342 |
Type of Variable | Variables | Correlation | Type |
---|---|---|---|
Explained Variable | Carbon Emission Factor | ||
URICFPG | Strong Positive Correlation | Direct | |
PFPG | Strong Positive Correlation | Direct | |
CRFPG | Strong Negative Correlation | Direct | |
Explanatory Variable | Electricity Consumption Elasticity Coefficient | Negative Correlation | Indirect |
Energy Consumption Elasticity Coefficient | Weak Negative Correlation | Indirect | |
Natural Population Growth Rate (%) | Negative Correlation | Indirect | |
Contribution Rate of Tertiary Industry to Regional GDP | Negative Correlation | Indirect | |
Growth Rate of Electricity Consumption Per Unit GDP (%) | Negative Correlation | Indirect |
Variable | t-Statistic | 5%-Level | P | Conclusion |
---|---|---|---|---|
Carbon Emission Factor | −0.2098 | −3.25981 | 0.9043 | Non-stationary |
Carbon Emission Factor (FOD) | −6.0512 | −3.2598 | 0.0014 | Stationary |
URICFPG | −0.6642 | −3.2127 | 0.8123 | Non-stationary |
URICFPG (FOD) | −3.8076 | −3.2598 | 0.0231 | Stationary |
PFPG | −0.6509 | −3.2127 | 0.8157 | Non-stationary |
PFPG (FOD) | −5.3281 | −3.3209 | 0.0043 | Stationary |
CRFPG | −0.7831 | −3.2598 | 0.7738 | Non-stationary |
CRFPG (FOD) | −5.3894 | −3.2598 | 0.003 | Stationary |
Hypothesized No. of CE(s) | t-Statistic | 5%-Level | P |
---|---|---|---|
None | 69.333 | 47.856 | 0.0002 |
At most 1 | 31.191 | 29.797 | 0.0343 |
At most 2 | 14.751 | 15.4995 | 0.0645 |
At most 3 | 2.423 | 3.841 | 0.1196 |
Appendix A.2. Hypothesized Method to Estimate Carbon Emission Factor
Appendix A.3. Calculation of Carbon Emission Factor
Year | -Model | 1 | ||||
---|---|---|---|---|---|---|
2010 | 68.50% | 80.58% | 40.32% | 0.5672 | 0.5714 | 0.967 |
2011 | 77.22% | 82.41% | 40.86% | 0.5793 | 0.5903 | |
2012 | 70.73% | 78.16% | 41.13% | 0.5552 | 0.5515 | |
2013 | 65.07% | 78.42% | 41.27% | 0.5538 | 0.5418 | |
2014 | 61.05% | 77.08% | 41.69% | 0.514 | 0.5222 | |
2015 | 55.68% | 75.32% | 41.00% | 0.5058 | 0.5113 | |
2016 | 52.73% | 70.61% | 41.69% | 0.4569 | 0.4708 | |
2017 | 58.21% | 72.79% | 42.41% | 0.4646 | 0.484 | |
2018 | 57.66% | 71.79% | 43.61% | 0.4552 | 0.4629 | |
2019 | 53.77% | 66.58% | 43.00% | 0.411 | 0.4337 | |
2020 | 51.25% | 67.87% | 42.70% | 0.4338 | 0.4403 |
Coefficients | Regression Statistics | ||
---|---|---|---|
0.4765 | Multiple R | 0.977 | |
0.1703 | R Square | 0.967 | |
0.5747 | Standard Error | 0.0097 | |
−1.2024 |
Item | 2021 | 2022 | 2023 | 2024 | 2025 | 2030 | 2035 |
---|---|---|---|---|---|---|---|
REF | 0.4497 | 0.4302 | 0.4204 | 0.4227 | 0.4138 | 0.3958 | 0.3993 |
LC | 0.4497 | 0.4307 | 0.4168 | 0.4081 | 0.394 | 0.309 | 0.2727 |
SC | 0.4497 | 0.4515 | 0.4382 | 0.4325 | 0.4207 | 0.3446 | 0.315 |
Appendix B. Introduction to Estimation of Total Electricity Consumption
Appendix B.1. ECPUOV Method
Item | 2025 | 2030 | 2035 |
---|---|---|---|
ECPUOV of Primary Industry | 0.033 | 0.034 | 0.035 |
ECPUOV of Secondary Industry | 0.103 | 0.09 | 0.078 |
ECPUOV of Tertiary Industry | 0.025 | 0.022 | 0.018 |
Appendix B.2. Estimated PCHEC
Year | 2005 | 2006 | 2007 | 2008 | 2009 | 2010 | 2011 | 2012 |
---|---|---|---|---|---|---|---|---|
PCEC (kWh) | 359 | 393 | 432 | 471 | 517 | 529 | 593 | 651 |
Year | 2013 | 2014 | 2015 | 2016 | 2017 | 2018 | 2019 | |
PCEC (kWh) | 672 | 756 | 780 | 822 | 845 | 873 | 937 |
Coefficients | Regression Statistics | ||
---|---|---|---|
Intercept | 307.06 | Multiple R | 0.997 |
41.855 | R Square | 0.994 | |
Standard Error | 15.089 |
Appendix B.3. Estimated Total Electricity Consumption
Item | 2025 (Estimated) | 2030 (Estimated) | 2035 (Estimated) |
---|---|---|---|
G province’s GDP (CNY 100 Million) 1 | 126,182 | 161,043 | 195,934 |
Population (10,000 Persons) | 12,240 | 12,700 | 13,000 |
ECPUOV of Primary Industry (kWh/CNY) | 0.033 | 0.034 | 0.035 |
ECPUOV of Secondary Industry (kWh/CNY) | 0.103 | 0.09 | 0.078 |
ECPUOV of Tertiary Industry (kWh/CNY) | 0.025 | 0.022 | 0.018 |
PCHEC (kWh/Person/Year) | 1186 | 1396 | 1605 |
Total Electricity Consumption | 9200 | 10,500 | 11,300 |
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Year | -Model | 1 | ||||
---|---|---|---|---|---|---|
2010 | 68.50% | 80.58% | 40.32% | 0.5672 | 0.5714 | 0.967 |
2011 | 77.22% | 82.41% | 40.86% | 0.5793 | 0.5903 | |
2012 | 70.73% | 78.16% | 41.13% | 0.5552 | 0.5515 | |
2013 | 65.07% | 78.42% | 41.27% | 0.5538 | 0.5418 | |
2014 | 61.05% | 77.08% | 41.69% | 0.514 | 0.5222 | |
2015 | 55.68% | 75.32% | 41.00% | 0.5058 | 0.5113 | |
2016 | 52.73% | 70.61% | 41.69% | 0.4569 | 0.4708 | |
2017 | 58.21% | 72.79% | 42.41% | 0.4646 | 0.484 | |
2018 | 57.66% | 71.79% | 43.61% | 0.4552 | 0.4629 | |
2019 | 53.77% | 66.58% | 43.00% | 0.411 | 0.4337 | |
2020 | 51.25% | 67.87% | 42.70% | 0.4338 | 0.4403 |
Item | 2021 | 2022 | 2023 | 2024 | 2025 | 2030 | 2035 |
---|---|---|---|---|---|---|---|
REF | 0.4497 | 0.4302 | 0.4204 | 0.4227 | 0.4138 | 0.3958 | 0.3993 |
LC | 0.4497 | 0.4307 | 0.4168 | 0.4081 | 0.394 | 0.309 | 0.2727 |
SC | 0.4497 | 0.4515 | 0.4382 | 0.4325 | 0.4207 | 0.3446 | 0.315 |
Item | 2019 | 2020 | 2025 | 2030 | 2035 | |
---|---|---|---|---|---|---|
Total Population (10,000 Persons) | 11,521 | 11,640 | 12,240 | 12,700 | 13,000 | |
PCHEC (kWh/Person) | 937 | 972 | 1200 | 1400 | 1600 | |
Primary Industry | 3664 | 3772 | 4416 | 4847 | 5310 | |
Economy (CNY 100 Million) | Secondary Industry | 36,668 | 37,716 | 48,958 | 59,683 | 69,106 |
Tertiary Industry | 50,332 | 52,803 | 73,059 | 96,465 | 12,1518 | |
Primary Industry | 0.032 | 0.033 | 0.033 | 0.034 | 0.035 | |
CPUOV (kWh/CNY) | Secondary Industry | 0.112 | 0.112 | 0.103 | 0.090 | 0.078 |
Tertiary Industry | 0.027 | 0.027 | 0.025 | 0.022 | 0.018 | |
Primary Industry | 117 | 124 | 146 | 165 | 186 | |
Electricity Consumption (100 Million kWh) | Secondary Industry | 4120 | 4209 | 5043 | 5371 | 5390 |
Tertiary Industry | 1380 | 1435 | 1826 | 2122 | 2187 | |
Residents | 1079 | 1132 | 1469 | 1778 | 2080 | |
Total Electricity Consumption (100 Million kWh) | 6696 | 6926 | 9200 | 10,500 | 11,300 |
Item | 2021 | 2022 | 2023 | 2024 | 2025 | 2030 | 2035 |
---|---|---|---|---|---|---|---|
REF | 3.98% | 3.98% | 3.98% | 3.98% | 3.98% | 3.98% | 3.98% |
LC | 3.96% | 3.95% | 3.94% | 3.90% | 3.80% | 3.80% | 3.80% |
SC | 3.96% | 3.95% | 3.94% | 3.90% | 3.80% | 3.75% | 3.75% |
Variable | 2020 | 2021 | 2022 | 2023 | 2024 | 2025 | 2030 | 2035 |
---|---|---|---|---|---|---|---|---|
(100 Million Tons) | 2.16 | 2.92 | 2.77 | 2.83 | 2.97 | 3.01 | 3.10 | 3.36 |
(100 Million Tons) | 2.04 | 2.77 | 2.63 | 2.69 | 2.82 | 2.86 | 2.94 | 3.18 |
(100 Million Tons) | 0.12 | 0.15 | 0.14 | 0.14 | 0.15 | 0.15 | 0.16 | 0.18 |
Variable | 2020 | 2021 | 2022 | 2023 | 2024 | 2025 | 2030 | 2035 |
---|---|---|---|---|---|---|---|---|
(100 Million Tons) | 2.16 | 2.92 | 2.77 | 2.81 | 2.86 | 2.86 | 2.42 | 2.29 |
(100 Million Tons) | 2.04 | 2.77 | 2.63 | 2.67 | 2.72 | 2.72 | 2.30 | 2.17 |
(100 Million Tons) | 0.12 | 0.15 | 0.14 | 0.14 | 0.14 | 0.14 | 0.12 | 0.12 |
Variable | 2020 | 2021 | 2022 | 2023 | 2024 | 2025 | 2030 | 2035 |
---|---|---|---|---|---|---|---|---|
(100 Million Tons) | 2.16 | 2.92 | 3.09 | 3.16 | 3.28 | 3.31 | 2.94 | 2.90 |
(100 Million Tons) | 2.04 | 2.77 | 2.94 | 3.01 | 3.12 | 3.16 | 2.80 | 2.76 |
(100 Million Tons) | 0.12 | 0.15 | 0.15 | 0.15 | 0.16 | 0.15 | 0.14 | 0.14 |
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Chen, X.; Liao, Z.; Gao, Z.; Li, Q.; Lv, P.; Zheng, G.; Yang, K. A Calculation Model of Carbon Emissions Based on Multi-Scenario Simulation Analysis of Electricity Consumption. Sustainability 2022, 14, 8765. https://doi.org/10.3390/su14148765
Chen X, Liao Z, Gao Z, Li Q, Lv P, Zheng G, Yang K. A Calculation Model of Carbon Emissions Based on Multi-Scenario Simulation Analysis of Electricity Consumption. Sustainability. 2022; 14(14):8765. https://doi.org/10.3390/su14148765
Chicago/Turabian StyleChen, Xiaoli, Zhiwei Liao, Zhihua Gao, Qian Li, Peng Lv, Guangyu Zheng, and Kun Yang. 2022. "A Calculation Model of Carbon Emissions Based on Multi-Scenario Simulation Analysis of Electricity Consumption" Sustainability 14, no. 14: 8765. https://doi.org/10.3390/su14148765