Identifying Driving Factors of Jiangsu’s Regional Sulfur Dioxide Emissions: A Generalized Divisia Index Method
Abstract
:1. Introduction
1.1. Background
1.2. Literature Review
1.2.1. Sulfur Dioxide Emissions from Trade
1.2.2. Relationship between Sulfur Dioxide Emissions and Socio-Economic Factors
1.3. Motivations and Contributions
1.4. Paper Organization
2. Materials and Methods
2.1. Comparison of Decomposition Methods
2.2. Variable Selection and Data Collection
- (1)
- Economic scale. Due to the long-term adherence of the development strategies centered on economic construction, the protection of the environment was diluted to a large extent in China. Recently, the government promulgated many environmental regulations, such as the “Five-Year Plan”, Regulations on Promoting the Circular Economy, and Extended Producer Responsibility, and put forward the concept of the “new normal” for the first time in 2014 to slow down the speed of economic development [53]. However, the transformation from an extensive pattern to intensive pattern will take a long time, and economic development still has a significant impact on the environment. Thus, we incorporate the economic scale effect into the model and measure it with per capita GDP.
- (2)
- Industrialization and industrial structure. The combustion of fossil fuels from the secondary industry is an important source of generated sulfur dioxide emissions [22]. Meanwhile, the unreasonable industrial structure leads to the fact that most of Jiangsu’s GDP comes from the secondary industry. In addition, the prosperity of the real estate industry promotes the continuous development of the construction industry, which aggravates the pollution of sulfur dioxide, both directly and indirectly [54]. It is necessary to consider industrialization and the industrial structure as model variables, and we choose gross industrial output value and its proportion to GDP to reflect the impact of industrialization and industrial structure, respectively.
- (3)
- Energy consumption and energy intensity. Energy consumption will increase with the development of industrialization. At present, the energy consumption structure in China is still dominated by petroleum and coal, and large-scale use of these unclean energy sources is the essential factor causing air pollution [55]. On the other hand, energy intensity can be regarded as a reflection of utilization efficiency or technology damage to the environment [56]. The enhancement of process flow or introduction of green technology could reduce energy intensity, which is conducive to decreasing air pollution. In addition, energy intensity has been used as a factor in many studies [21,29,57]. Therefore, energy consumption and energy intensity are also integrated into the model. Referring to Shao et al. [50], we adopt the proportion of energy consumption to gross industrial output value to represent the energy intensity.
2.3. Decomposition Model Based on GDIM
3. Results and Discussion
3.1. The Evolution of Absolute Indicators
3.2. The Evolution of Relative Indicators
3.3. Decomposition Results
3.3.1. Chain-Linked Decomposition
3.3.2. Stage Decomposition
4. Policy Inspirations
- (1)
- Implementing the targeted strategies for the management of industrial sulfur dioxide emissions. By observing the spatial distribution and decomposition results of industrial sulfur dioxide emissions in different regions during 2004–2016, the sulfur emissions present obvious spatial heterogeneity. Thus, it is necessary to implement tailored emission reduction policies in different regions, depending on the relevant factors affecting variations and their location advantages, so as to promote the coupling of development of the environment and economy with high quality.
- (2)
- The GDIM used in this paper indicates that two factors (industrial structure effect and energy intensity effect) have a weak impact on the reduction of industrial sulfur dioxide emissions, which means they can still be further optimized. In fact, traditional high energy consumption and high emissions are signals of economic waste and inefficiency of resources. Relevant industries should adopt a reasonable method of resource transformation. For instance, relying on material resources to attract knowledge and human resources, thereby improving resource potential, or, referring to industrial symbiosis theory or typical eco-industrial park patterns to accelerate the transformation and upgrade traditional industries.
- (3)
- Despite the various measures employed by the Jiangsu provincial government in the field of ecological management, including amending the “Environmental Protection Law” and promulgating the 13th “Five-Year Plan” (2016–2020), the high energy consumption and coal-based energy structure will continue for a long time. Therefore, the government should not only concentrate on end-of-pipe treatment, but also implement the ideology of “environmental protection” into the whole life cycle of products. Meanwhile, the production-oriented enterprises should be regarded as the main factor controlling industrial sulfur dioxide emissions, so as to extend their responsibility for emission reduction and eliminate backward production capacity. In addition, the technologies for long-distance transport and energy storage need to be developed to raise the proportion of renewable energy supply.
5. Conclusions
- (1)
- The share of each region in the total industrial sulfur dioxide emissions evolved with the trend, except in 2010. Although the share of the South region has declined by about 8% (from 58.40% in 2004 to 50.46% in 2016), it still accounts for more than half of the total share. The share of the Middle region also decreased (from 16.37% in 2004 to 12.88% in 2016), while that of the North region increased from 25.23% in 2004 up to 36.65% in 2016.
- (2)
- In general, the industrialization effect, economic scale effect, and energy consumption effect of Jiangsu province and its three regions promote industrial sulfur dioxide emissions, whereas the remaining factors, namely the technology effect, sulfur efficiency effect, energy mix effect, energy intensity effect, and industrial structure effect, play mitigating roles in the emissions.
- (3)
- The results of chain-linked decomposition in the South, Middle, and North regions demonstrate that several factors may show anomalous contribution direction. For the South and North regions, this phenomenon mainly occurred in 2010 and 2011, respectively. Specifically, the technology effect, energy mix effect, and sulfur efficiency effect unexpectedly promote industrial sulfur dioxide emissions rather than mitigate them. Regarding the Middle region, it is not easy to comprehend that the energy mix effect and industrial structure effect act as facilitators in 2009, while the energy consumption effect acts as an inhibitor. In addition, by comparing these three regions, the technology effect in the Middle region is not as anomalous as the other two regions. Similarly, the energy consumption effect in the North region promotes industrial sulfur dioxide emissions all the time.
Author Contributions
Funding
Conflicts of Interest
Appendix A
Y | SY | E | SE | G | SG | EY | YG | Change | |
---|---|---|---|---|---|---|---|---|---|
2004–2005 | 0.0926 | −0.0390 | 0.0562 | −0.0079 | 0.0565 | −0.0085 | −0.0011 | −0.0012 | 0.1476 |
2005–2006 | 0.0780 | −0.1017 | 0.0420 | −0.0747 | 0.0527 | −0.0845 | −0.0014 | −0.0006 | −0.0901 |
2006–2007 | 0.0836 | −0.1087 | 0.0366 | −0.0736 | 0.0523 | −0.0881 | −0.0024 | −0.0008 | −0.1013 |
2007–2008 | 0.0642 | −0.0789 | 0.0069 | −0.0287 | 0.0509 | −0.0708 | −0.0042 | 0.0000 | −0.0606 |
2008–2009 | 0.0419 | −0.0700 | 0.0075 | −0.0390 | 0.0478 | −0.0761 | −0.0019 | −0.0001 | −0.0898 |
2009–2010 | 0.0897 | 0.0179 | 0.0672 | 0.0389 | 0.0709 | 0.0352 | −0.0004 | −0.0003 | 0.3191 |
2010–2011 | 0.0486 | −0.1000 | 0.0266 | −0.0822 | 0.0515 | −0.1032 | −0.0008 | 0.0000 | −0.1595 |
2011–2012 | 0.0348 | −0.0542 | 0.0512 | −0.0680 | 0.0385 | −0.0570 | −0.0004 | 0.0000 | −0.0551 |
2012–2013 | 0.0355 | −0.0523 | 0.0316 | −0.0491 | 0.0304 | −0.0479 | 0.0000 | 0.0000 | −0.0518 |
2013–2014 | 0.0213 | −0.0356 | 0.0104 | −0.0252 | 0.0286 | −0.0424 | −0.0002 | −0.0001 | −0.0432 |
2014–2015 | 0.0155 | −0.0372 | 0.0193 | −0.0405 | 0.0239 | −0.0449 | 0.0000 | −0.0001 | −0.0641 |
2015–2016 | 0.0137 | −0.1337 | 0.0108 | −0.1312 | 0.0229 | −0.1399 | 0.0000 | −0.0002 | −0.3576 |
2005–2010 | 0.3269 | −0.2861 | 0.1695 | −0.2226 | 0.2416 | −0.2788 | −0.0250 | −0.0032 | −0.0777 |
2010–2015 | 0.1345 | −0.2428 | 0.1206 | −0.2370 | 0.1478 | −0.2479 | −0.0004 | −0.0004 | −0.3257 |
2004–2016 | 0.3710 | −0.4550 | 0.3233 | −0.5944 | 0.3244 | −0.4929 | −0.0168 | −0.0011 | −0.5415 |
Y | SY | E | SE | G | SG | EY | YG | Change | |
---|---|---|---|---|---|---|---|---|---|
2004–2005 | 0.0902 | −0.0593 | 0.0588 | −0.0338 | 0.0595 | −0.0346 | −0.0009 | −0.0009 | 0.0790 |
2005–2006 | 0.0727 | −0.0834 | 0.0427 | −0.0594 | 0.0537 | −0.0697 | −0.0010 | −0.0003 | −0.0446 |
2006–2007 | 0.0775 | −0.0974 | 0.0368 | −0.0659 | 0.0526 | −0.0805 | −0.0019 | −0.0005 | −0.0791 |
2007–2008 | 0.0496 | −0.0941 | −0.0003 | −0.0514 | 0.0461 | −0.0937 | −0.0037 | 0.0000 | −0.1474 |
2008–2009 | 0.0271 | −0.0535 | 0.0077 | −0.0353 | 0.0432 | −0.0678 | −0.0007 | −0.0005 | −0.0799 |
2009–2010 | 0.0902 | 0.1245 | 0.0489 | 0.1671 | 0.0752 | 0.1402 | −0.0014 | −0.0001 | 0.6445 |
2010–2011 | 0.0371 | −0.1479 | 0.0188 | −0.1353 | 0.0445 | −0.1534 | −0.0007 | −0.0002 | −0.3370 |
2011–2012 | 0.0159 | −0.0468 | 0.0675 | −0.0888 | 0.0383 | −0.0656 | −0.0039 | −0.0003 | −0.0837 |
2012–2013 | 0.0231 | −0.0488 | 0.0234 | −0.0490 | 0.0277 | −0.0529 | 0.0000 | 0.0000 | −0.0766 |
2013–2014 | 0.0092 | −0.0215 | 0.0012 | −0.0136 | 0.0224 | −0.0340 | −0.0001 | −0.0003 | −0.0366 |
2014–2015 | 0.0006 | −0.0264 | 0.0216 | −0.0457 | 0.0208 | −0.0451 | −0.0006 | −0.0005 | −0.0753 |
2015–2016 | 0.0056 | −0.1253 | 0.0108 | −0.1284 | 0.0207 | −0.1356 | 0.0000 | −0.0004 | −0.3527 |
2005–2010 | 0.3170 | −0.2305 | 0.1503 | −0.1146 | 0.2587 | −0.2195 | −0.0259 | −0.0004 | 0.1351 |
2010–2015 | 0.0676 | −0.2573 | 0.1029 | −0.2622 | 0.1203 | −0.2665 | −0.0014 | −0.0036 | −0.5002 |
2004–2016 | 0.3030 | −0.4604 | 0.2607 | −0.5379 | 0.2911 | −0.4507 | −0.0092 | −0.0005 | −0.6038 |
Y | SY | E | SE | G | SG | EY | YG | Change | |
---|---|---|---|---|---|---|---|---|---|
2004–2005 | 0.1001 | −0.0022 | 0.0382 | 0.0562 | 0.0625 | 0.0307 | −0.0033 | −0.0010 | 0.2812 |
2005–2006 | 0.1027 | −0.1055 | 0.0470 | −0.0642 | 0.0582 | −0.0753 | −0.0029 | −0.0018 | −0.0419 |
2006–2007 | 0.1055 | −0.1112 | 0.0485 | −0.0701 | 0.0571 | −0.0789 | −0.0030 | −0.0023 | −0.0544 |
2007–2008 | 0.0928 | −0.0881 | 0.0314 | −0.0384 | 0.0549 | −0.0616 | −0.0038 | −0.0011 | −0.0141 |
2008–2009 | 0.0725 | −0.0736 | −0.0415 | 0.0360 | 0.0503 | −0.0604 | −0.0153 | 0.0001 | −0.0318 |
2009–2010 | 0.0708 | −0.1186 | 0.1155 | −0.1475 | 0.0597 | −0.1077 | −0.0033 | −0.0005 | −0.1317 |
2010–2011 | 0.0546 | −0.0624 | 0.0208 | −0.0321 | 0.0542 | −0.0633 | −0.0016 | 0.0000 | −0.0298 |
2011–2012 | 0.0437 | −0.0531 | −0.0064 | −0.0060 | 0.0359 | −0.0480 | −0.0034 | 0.0001 | −0.0372 |
2012–2013 | 0.0458 | −0.0661 | 0.1050 | −0.1104 | 0.0331 | −0.0526 | −0.0057 | −0.0007 | −0.0516 |
2013–2014 | 0.0323 | −0.0295 | 0.0167 | −0.0146 | 0.0400 | −0.0368 | −0.0004 | −0.0001 | 0.0076 |
2014–2015 | 0.0283 | −0.0860 | 0.0042 | −0.0657 | 0.0258 | −0.0849 | −0.0009 | 0.0000 | −0.1792 |
2015–2016 | 0.0179 | −0.1815 | 0.0168 | −0.1807 | 0.0245 | −0.1848 | 0.0000 | −0.0001 | −0.4879 |
2005–2010 | 0.3695 | −0.3328 | 0.2071 | −0.3330 | 0.2286 | −0.3476 | −0.0238 | −0.0170 | −0.2491 |
2010–2015 | 0.1816 | −0.2507 | 0.1228 | −0.2296 | 0.1621 | −0.2489 | −0.0045 | 0.0000 | −0.2673 |
2004–2016 | 0.3877 | −0.4427 | 0.3385 | −0.6652 | 0.3112 | −0.5388 | −0.0210 | −0.0088 | −0.6390 |
Y | SY | E | SE | G | SG | EY | YG | Change | |
---|---|---|---|---|---|---|---|---|---|
2004–2005 | 0.0880 | −0.0110 | 0.0506 | 0.0222 | 0.0412 | 0.0319 | −0.0010 | −0.0022 | 0.2196 |
2005–2006 | 0.0871 | −0.1457 | 0.0380 | −0.1151 | 0.0461 | −0.1225 | −0.0025 | −0.0018 | −0.2163 |
2006–2007 | 0.0947 | −0.1430 | 0.0279 | −0.0989 | 0.0469 | −0.1163 | −0.0047 | −0.0022 | −0.1957 |
2007–2008 | 0.1059 | −0.0501 | 0.0268 | 0.0207 | 0.0614 | −0.0155 | −0.0057 | −0.0014 | 0.1421 |
2008–2009 | 0.0760 | −0.1228 | 0.0396 | −0.0968 | 0.0586 | −0.1128 | −0.0017 | −0.0002 | −0.1601 |
2009–2010 | 0.1145 | −0.1078 | 0.0978 | −0.0988 | 0.0702 | −0.0753 | 0.0000 | −0.0025 | −0.0020 |
2010–2011 | 0.0952 | 0.0438 | 0.0649 | 0.0733 | 0.0735 | 0.0645 | −0.0007 | −0.0003 | 0.4141 |
2011–2012 | 0.0932 | −0.0866 | 0.0289 | −0.0356 | 0.0404 | −0.0476 | −0.0038 | −0.0027 | −0.0138 |
2012–2013 | 0.0595 | −0.0588 | 0.0192 | −0.0238 | 0.0351 | −0.0394 | −0.0018 | −0.0005 | −0.0105 |
2013–2014 | 0.0409 | −0.0674 | 0.0381 | −0.0652 | 0.0354 | −0.0629 | 0.0000 | 0.0000 | −0.0812 |
2014–2015 | 0.0379 | −0.0293 | 0.0219 | −0.0145 | 0.0300 | −0.0224 | −0.0003 | 0.0000 | 0.0232 |
2015–2016 | 0.0262 | −0.1247 | 0.0063 | −0.1100 | 0.0257 | −0.1253 | −0.0007 | 0.0000 | −0.3025 |
2005–2010 | 0.3671 | −0.3553 | 0.2153 | −0.3982 | 0.2137 | −0.3956 | −0.0197 | −0.0240 | −0.3966 |
2010–2015 | 0.3580 | −0.2033 | 0.1952 | −0.1024 | 0.2221 | −0.1439 | −0.0160 | −0.0125 | 0.2973 |
2004–2016 | 0.5651 | −0.4335 | 0.5184 | −0.7065 | 0.4121 | −0.6158 | −0.0263 | −0.0476 | −0.3340 |
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Variables | Definition | Effect |
---|---|---|
Industrial sulfur dioxide emissions | Not Applicable | |
Per capita GDP | Economic scale effect | |
Gross industrial output value | Industrialization effect | |
Energy consumption | Energy consumption effect | |
Sulfur intensity of per capita GDP | Sulfur efficiency effect | |
Sulfur intensity of gross industrial output value | Technology effect | |
Sulfur intensity of energy consumption | Energy mix effect | |
Energy intensity | Energy intensity effect | |
Industrial structure | Industrial structure effect |
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Yang, J.; Shan, H. Identifying Driving Factors of Jiangsu’s Regional Sulfur Dioxide Emissions: A Generalized Divisia Index Method. Int. J. Environ. Res. Public Health 2019, 16, 4004. https://doi.org/10.3390/ijerph16204004
Yang J, Shan H. Identifying Driving Factors of Jiangsu’s Regional Sulfur Dioxide Emissions: A Generalized Divisia Index Method. International Journal of Environmental Research and Public Health. 2019; 16(20):4004. https://doi.org/10.3390/ijerph16204004
Chicago/Turabian StyleYang, Junliang, and Haiyan Shan. 2019. "Identifying Driving Factors of Jiangsu’s Regional Sulfur Dioxide Emissions: A Generalized Divisia Index Method" International Journal of Environmental Research and Public Health 16, no. 20: 4004. https://doi.org/10.3390/ijerph16204004