Multilevel Index Decomposition of Energy-Related Carbon Emissions and Their Decoupling from Economic Growth in Northwest China
Abstract
:1. Introduction
2. Methodology and Date
2.1. Methodology
2.1.1. Energy-Related CO2 Emissions Estimation Approach
2.1.2. Multilevel Index Decomposition Analysis
2.1.3. Decoupling Measurement of CO2 Emissions and Economic Growth
2.2. Data
3. Results and Discussion
3.1. Trajectory of CO2 Emissions
3.1.1. Features of CO2 Emissions in Northwest China
3.1.2. Features of CO2 Emissions in Each Province
3.2. Decomposition Results of CO2 Emissions
3.2.1. Additive Decomposition Results of CO2 Emission Changes at Regional Level
3.2.2. Multiplicative Decomposition Results of CO2 Emission Changes at Provincial Level
3.3. Decoupling State in Northwest China
4. Conclusions and Policy Implications
4.1. Conclusions
- In Northwest China, total CO2 emissions and per capita CO2 emissions increased rapidly during 1996–2014. In 2014, these two indicators were 4.9 times and 4.3 times their 1995 levels. At the same time, the trend of the CO2 emission intensity is more complicated. From 1996 to 2002, it decreased slowly. Then, it increased with the average annual rate of 1.0% during 2002–2009. After 2009, it almost unchanged. Specifically at the provincial level, although the trends of the total CO2 emissions, per capita CO2 emissions, and CO2 emission intensity were similar in the provinces, variations of these indicators were different among provinces.
- The results derived from the additive decomposition of CO2 emissions at the regional level show the following. The economic activity proves to be an overwhelming contributor to CO2 emissions increase, which accounts for 67.5% of the total emissions during the study period. At the same time, the population also contributes to CO2 emissions with the contribution rate of 5.1%. Conversely, the energy intensity partially offsets emission growth, with the contribution rate of −13.3%. Moreover, the energy structure has a marginal effect with the rate of only about −0.02%.
- Comparative analysis of the multiplicative decomposition results for the five provinces indicates that the population effect in Ningxia and Xinjiang is more significant than that in the other provinces. At the same time, the contributions of the economic growth to carbon emissions are more remarkable in Shannxi and Gansu. Moreover, the intensity effect was weak in Xinjiang, whereas it was very strong in Gansu. In addition, the structural effect varies significantly among provinces, ranging from −4.2% in Shannxi to 2.0% in Xinjiang.
- According to the decoupling index, the “relative decoupling effort” and “no decoupling effort” are the main characteristics during the examined period. Specifically, in 1996, 1998, 2000–2002 and 2007–2009, the decoupling state is characterized as “relative decoupling”, while during 2003–2006, 2010–2013, the decoupling state is characterized as “no decoupling”. Also, there was “strong decoupling” in 1997 and 1999.
4.2. Policy Implications
- More attention should be paid to the environmental impact of the Western Development Strategy. To achieve low-carbon development in Northwest China, the government should continually change economic growth patterns. In particular, the government should increase its investments in energy-related technologies, while restricting transfers of backward production capacities to Northwest China. For other underdeveloped regions, it is equally important to change the mode of economic growth. Underdeveloped areas should obtain more technology spillovers from advanced areas, but not as pollution havens, aimed to boost the economy without considering the environment and only relying on a large number of resources consumption.
- Readjusting the energy use structure is urgently required. From the national point of view, the energy use structure significantly inhibited CO2 emissions in recent years [10,18]. On the contrary, it increased CO2 emissions since 2005 in Northwest China. This indicates that optimization of the energy use structure in Northwest China, where the proportion of coal consumption continued to be at around 60.0%, clearly lags behind the rest of the country. The government must strictly control coal consumption caps, and continuously reduce the proportion of coal in the total primary energy consumption mix. Furthermore, policy makers should seize the opportunities of the “One Road, One Belt” initiative, strengthen energy cooperation with Central Asia, and increase the proportion of clean and renewable energy. In addition, there are many efficacious energy innovation policy tools and energy innovation organizations, such as energy development plan, preferential taxes, subsidies, and public procurement which can be used to invite investment in clean and renewable energy technology.
- Energy efficiency should be persistently improved. Northwest China is a major energy and chemical industry base. Since 2000, the central government began to implement the Western Development Strategy. Financial support and preferential policies were provided to promote growth of economy, especially industries. However, the progress in developing energy utilization technologies in Northwest China lags far behind the national average. Accordingly, the government should focus more on research and development of advanced energy technologies, eliminating backward production capacities to improve energy efficiency. Moreover, given that technology inequity exists between developed and undeveloped regions, the central government has to make significant efforts to balance the development of carbon emission reduction technology through removing technology barrier between regions.
Acknowledgments
Author Contributions
Conflicts of Interest
Abbreviations
LMDI | Logarithmic Mean Divisia Index |
BTH | Beijing-Tianjin-Hebei |
YRD | Yangtze River Delta |
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Fuel Type | LCV a (KJ/kg or KJ/m3) | Oxidation Rate b | Potential Carbon Content c (kgC/GJ) | CO2 EF d (tCO2/ton or 103 m3) |
---|---|---|---|---|
Raw coal | 20,908 | 0.918 | 26.37 | 1.981 |
Coke | 28,435 | 0.928 | 29.5 | 2.860 |
Crude oil | 41,816 | 0.979 | 20.1 | 3.020 |
Gasoline | 43,070 | 0.986 | 18.9 | 2.925 |
Kerosene | 43,070 | 0.980 | 19.6 | 3.033 |
Diesel oil | 42,652 | 0.982 | 20.2 | 3.096 |
Fuel oil | 41,816 | 0.985 | 21.1 | 3.170 |
Natural gas | 38,931 | 0.990 | 15.3 | 2.162 |
IDA Identity | ||
Total Effect | Additive decomposition | Multiplicative decomposition |
Effect by Factor | ||
Effect | Province | 1996–2000 | 2001–2005 | 2006–2010 | 2011–2014 | 1996–2014 |
---|---|---|---|---|---|---|
Population | Shannxi | 1.0372 | 1.0126 | 1.0122 | 1.0107 | 1.0745 |
Gansu | 1.0509 | 0.9934 | 1.0058 | 1.0121 | 1.0627 | |
Qinghai | 1.0732 | 1.0515 | 1.0373 | 1.0346 | 1.2111 | |
Ningxia | 1.0818 | 1.0753 | 1.0615 | 1.0458 | 1.2913 | |
Xinjiang | 1.1132 | 1.0870 | 1.0851 | 1.0536 | 1.3833 | |
Economic Activity | Shannxi | 1.5329 | 1.7316 | 1.9207 | 1.5484 | 7.8943 |
Gansu | 1.4796 | 1.6697 | 1.6898 | 1.5096 | 6.3020 | |
Qinghai | 1.4169 | 1.6795 | 1.7395 | 1.4896 | 6.1661 | |
Ningxia | 1.5207 | 1.5657 | 1.7029 | 1.4159 | 5.7405 | |
Xinjiang | 1.3190 | 1.4774 | 1.5228 | 1.4529 | 4.3114 | |
Energy Intensity | Shannxi | 0.6042 | 1.2412 | 0.9780 | 0.8901 | 0.6528 |
Gansu | 0.7152 | 0.8917 | 0.7754 | 0.8109 | 0.4010 | |
Qinghai | 0.7102 | 1.0667 | 0.8596 | 0.9399 | 0.6120 | |
Ningxia | 0.6295 | 0.9308 | 0.9700 | 1.0426 | 0.5926 | |
Xinjiang | 0.8366 | 0.9570 | 1.0516 | 1.1180 | 0.9413 | |
Energy Structure | Shannxi | 0.9368 | 0.9973 | 1.0045 | 1.0217 | 0.9588 |
Gansu | 0.9831 | 1.0090 | 1.0113 | 0.9990 | 1.0021 | |
Qinghai | 0.9881 | 0.9933 | 0.9845 | 0.9945 | 0.9609 | |
Ningxia | 0.9894 | 1.0140 | 1.0070 | 1.0031 | 1.0134 | |
Xinjiang | 0.9786 | 0.9866 | 1.0390 | 1.0168 | 1.0200 | |
Total Effect | Shannxi | 0.8999 | 2.1706 | 1.9098 | 1.4231 | 5.3092 |
Gansu | 1.0932 | 1.4923 | 1.3328 | 1.2378 | 2.6062 | |
Qinghai | 1.0670 | 1.8713 | 1.5269 | 1.4406 | 4.3920 | |
Ningxia | 1.0247 | 1.5891 | 1.7657 | 1.5485 | 4.4522 | |
Xinjiang | 1.2020 | 1.5162 | 1.8054 | 1.7402 | 5.7257 |
Time Period | δ | δpop | Δint | Δstr | Decoupling State |
---|---|---|---|---|---|
1995–1996 | 0.1591 | −0.1464 | 0.2851 | 0.0204 | Relative decoupling |
1996–1997 | 1.2483 | −0.1369 | 1.2318 | 0.1534 | Strong decoupling |
1997–1998 | 0.9162 | −0.1509 | 0.9810 | 0.0861 | Relative decoupling |
1998–1999 | 1.3446 | −0.1455 | 1.4160 | 0.0741 | Strong decoupling |
1999–2000 | 0.6929 | −0.1981 | 0.7690 | 0.1220 | Relative decoupling |
2000–2001 | 0.4011 | −0.0214 | 0.3792 | 0.0433 | Relative decoupling |
2001–2002 | 0.1370 | −0.0659 | 0.1986 | 0.0043 | Relative decoupling |
2002–2003 | −0.9952 | −0.0619 | −0.8919 | −0.0414 | No decoupling |
2003–2004 | −0.2138 | −0.0553 | −0.1818 | 0.0232 | No decoupling |
2004–2005 | −0.2451 | −0.0747 | −0.1104 | −0.0600 | No decoupling |
2005–2006 | −0.2789 | −0.0620 | −0.2051 | −0.0119 | No decoupling |
2006–2007 | 0.1689 | −0.0593 | 0.2420 | −0.0138 | Relative decoupling |
2007–2008 | 0.2585 | −0.0532 | 0.3336 | −0.0219 | Relative decoupling |
2008–2009 | 0.1731 | −0.0530 | 0.2887 | −0.0625 | Relative decoupling |
2009–2010 | −0.1467 | −0.0441 | −0.0807 | −0.0218 | No decoupling |
2010–2011 | −0.3501 | −0.0445 | −0.2511 | −0.0546 | No decoupling |
2011–2012 | −0.0228 | −0.0555 | 0.0651 | −0.0325 | No decoupling |
2012–2013 | 0.1822 | −0.0628 | 0.2640 | −0.0191 | No decoupling |
2013–2014 | 0.3659 | −0.0796 | 0.4408 | 0.0048 | Relative decoupling |
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Dong, J.-F.; Deng, C.; Wang, X.-M.; Zhang, X.-L. Multilevel Index Decomposition of Energy-Related Carbon Emissions and Their Decoupling from Economic Growth in Northwest China. Energies 2016, 9, 680. https://doi.org/10.3390/en9090680
Dong J-F, Deng C, Wang X-M, Zhang X-L. Multilevel Index Decomposition of Energy-Related Carbon Emissions and Their Decoupling from Economic Growth in Northwest China. Energies. 2016; 9(9):680. https://doi.org/10.3390/en9090680
Chicago/Turabian StyleDong, Jie-Fang, Chun Deng, Xing-Min Wang, and Xiao-Lei Zhang. 2016. "Multilevel Index Decomposition of Energy-Related Carbon Emissions and Their Decoupling from Economic Growth in Northwest China" Energies 9, no. 9: 680. https://doi.org/10.3390/en9090680