Influential Factors and Spatiotemporal Characteristics of Carbon Intensity on Industrial Sectors in China
Abstract
:1. Introduction
2. Literature Review
2.1. Influential Factors and Study Perspectives of Carbon Intensity
2.2. Spatial Panel Data Model for the Study of Carbon Emissions
3. Method and Date
3.1. Estimating Carbon Emissions in Industrial Sectors
3.2. The Extended STIRPAT Model
3.3. Spatial Econometric Analysis Model
3.3.1. Spatial Autocorrelation of Carbon Intensity
3.3.2. Spatial Weight Matrix
3.3.3. Spatial Panel Model
3.4. Data Source
- (1)
- The categories and codes of the industrial sectors. Since there is a certain distinction between the national economic industries divided by the National Bureau of Statistics in China and the Organisation for Economic Co-operation and Development (OECD) input-output table, the two standards are considered and combined [58]. Twenty-two-digit industry names and codes in this study are as shown in Table S2.
- (2)
- In the actual statistical process, the wide range of product exchanges between various industries and departments requires human resources, material resources, and time [59,60]. Therefore, in China, the corresponding input-output tables are only available in the years with mantissa 2 and 7, which directly results in the discontinuity of the input-output table. Due to the limitation of actual data, many studies using the input-output table to analyze practical problems can only be limited to some years. Considering that the input-output data before 2000 are too short of timeliness, this study only used the input-output table data after 2000. Meanwhile, the most recent year of the input-output table published by the China Input–Output Society is 2015, so the latest data used in this paper are from 2015 [61].
- (3)
- The data of WP, IAV, FAI, and CR were taken from the China Statistical Yearbook [62], China Industrial Statistics Yearbook [63], and the National Bureau of Statistics of China from 2005 to 2015 [8]. Furthermore, the data of RTE were obtained from the China Taxation Yearbook from 2005 to 2015 [64]. The statistical descriptions of variables are represented in Table 2.
4. Results and Discussions
4.1. Results of Carbon Intensity
4.2. Results of Spatial Autocorrelation Test
4.3. Spatial Econometric Regression Results
4.3.1. Test Results of the SDM Model
4.3.2. Estimation Analysis of Spatial Durbin Model
4.3.3. Results of the Direct and Spillover Effects
5. Conclusions
Supplementary Materials
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Reference | Period | Perspectives | Models | Influential Factors |
---|---|---|---|---|
[30] | 1990–2006 | Agriculture, Manufacturing, Service | Environmental Data Envelopment Analysis (DEA), Multiplicative LMDI | Energy intensity, Energy efficiency, Economic effect, Structural effect, Technical change, etc. |
[31] | 2000–2009 | Agriculture, Industry, Construction, Transportation, Commercial, The Other Sectors | LMDI of sectoral | Energy intensity, Energy Structure, Structure effect, GDP, etc. |
[32] | 1985–2014 | Transportation | LMDI | Energy intensity, Structure effect, Economic output, Population Effects |
[33] | 1985–2011 | Power Industry | Scenario analysis, Monte Carlo analysis, LMDI | Population, Economic activity Energy efficiency, Electricity generation structure, Electricity intensity |
[34] | 2000–2014 | Equipment Manufacturing Industry | Tapio decoupling, Evaluation model, LMDI | The average number of labor, Energy Intensity, Energy consumption, Industry value added |
[35] | 2002–2012 | 42 industrial sectors | SDA and IDA | Intermediate input, Added value, Total input, Energy structure |
[36] | 2016–2050 | Building Sector | Emission reduction potential model, scenario analysis | Population, Urbanization rate, Total area, Rural building area, Commercial building area, Energy intensity, Energy consumption, Urban building area, |
[37] | 2011–2050 | Iron and Steel, Electric, Power, Cement, Transport, Construction, Other industries | The ZSG-DEA model Scenarios analysis | GDP growth rate, Total GDP, Energy consumption growth rate, Total energy consumption, |
[38] | 2005–2015 | 26 industrial sectors | Input-output model | Propensity to consume, Population, Per capita income, Production intensity, Capital investment, Export |
Variables | Unit | Mean | Max | Min | Std. Dev | Median | Skewness | Kurtosis |
---|---|---|---|---|---|---|---|---|
Ln(DCI) | tonnes/104 yuan | −0.0132 | 4.4495 | −3.7907 | 1.8452 | −0.6179 | 0.5880 | −0.2711 |
Ln(WP) | 104 people | 6.0868 | 7.9800 | 4.3095 | 0.7448 | 6.2405 | −0.4296 | 0.0351 |
Ln(IAV) | 109 yuan | 9.0985 | 10.9315 | 6.3130 | 0.9252 | 9.2664 | −0.5674 | −0.2150 |
Ln(FAI) | 109 yuan | 8.4921 | 10.7991 | 5.6380 | 1.0331 | 8.5361 | −0.1890 | −0.6207 |
Ln(CR) | 104 tonnes of standard coal | 7.6576 | 11.8227 | 4.6635 | 1.9001 | 7.2513 | 0.4224 | −0.9689 |
Ln(RTE) | 104 yuan | 10.2803 | 14.4714 | 6.7124 | 1.8813 | 9.9873 | 0.2508 | −0.9226 |
Year | I | E(I) | Sd(I) | Z | p-Value |
---|---|---|---|---|---|
2005 | 0.460 | −0.053 | 0.096 | 5.337 | 0.000 |
2006 | 0.400 | −0.053 | 0.085 | 5.321 | 0.000 |
2007 | 0.372 | −0.053 | 0.079 | 5.380 | 0.000 |
2008 | 0.425 | −0.053 | 0.086 | 5.567 | 0.000 |
2009 | 0.430 | −0.053 | 0.086 | 5.640 | 0.000 |
2010 | 0.371 | −0.053 | 0.076 | 5.566 | 0.000 |
2011 | 0.372 | −0.053 | 0.076 | 5.576 | 0.000 |
2012 | 0.347 | −0.053 | 0.072 | 5.553 | 0.000 |
2013 | 0.262 | −0.053 | 0.060 | 5.225 | 0.000 |
2014 | 0.242 | −0.053 | 0.057 | 5.156 | 0.000 |
2015 | 0.230 | −0.053 | 0.055 | 5.130 | 0.000 |
Variables | Pooled OLS | Spatial Fixed Effects | Time-Period Fixed Effects | Spatial and Time-Period Fixed Effects |
---|---|---|---|---|
lnWP | 0.007117 | 0.110081 | −0.062697 | 0.077985 |
(0.070536) | (1.230155) | (−0.696842) | (0.907808) | |
lnIAV | −0.673708 *** | −0.930919 *** | −0.738112 *** | −1.075756 *** |
(−7.232953) | (−8.98873) | (−8.918925) | (−9.731367) | |
lnFAI | −0.526065 *** | −0.004036 | −0.193378 * | −0.090964 * |
(−5.067212) | (−0.09171) | (−1.797468) | (−1.78732) | |
lnCR | 0.298374 *** | 0.071815 *** | 0.24465 *** | 0.060327 *** |
(5.423242) | (3.142763) | (4.960244) | (2.739103) | |
lnRTE | 0.731265 *** | 0.087624 *** | 0.776671 *** | 0.082699 ** |
(10.047195) | (3.219515) | (11.845452) | (2.319146) | |
intercept | 0.738175 | |||
(1.21517) | ||||
R2 | 0.8119 | 0.7328 | 0.8509 | 0.4288 |
adj.R-sq | 0.8075 | 0.7278 | 0.8482 | 0.4182 |
σ2 | 0.6584 | 0.0204 | 0.5127 | 0.0183 |
Durbin–Watson | 1.8483 | 1.5975 | 2.2642 | 1.8401 |
Log-likelihood | −263.1514 | 118.5949 | −236.1576 | 130.6136 |
LM spatial lag | 63.0448 *** | 1.2834 | 37.8617 *** | 0.2227 |
LM spatial error | 0.6931 | 7.8131 *** | 113.4589 *** | 0.0156 |
Robust LM spatial lag | 112.6474 *** | 6.1047 ** | 8.1296 *** | 0.3682 |
Robust LM spatial error | 50.2956 *** | 12.6344 *** | 83.7268 *** | 0.1610 |
Time Period Fixed Effects | t-Stat | Spatial Fixed Effects | t-Stat | Spatial and Time Period Fixed Effects | t-Stat | Spatial Random Effects and Time Period Fixed Effects | t-Stat | |
---|---|---|---|---|---|---|---|---|
lnWP | 0.16781 *** | (2.792187) | 0.189684 ** | (2.146972) | 0.180104 ** | (2.177192) | 0.153244 * | (1.774003) |
lnIAV | −0.297274 *** | (−5.233159) | −1.018526 *** | (−8.908154) | −1.083282 *** | (−10.191193) | −1.035031 *** | (−9.434184) |
lnFAI | 0.107959 | (1.33415) | 0.01054 | (0.183685) | −0.007189 | (−0.133335) | 0.006709 | (0.119745) |
lnCR | 0.100926 *** | (2.914759) | 0.081244 *** | (3.570684) | 0.058751 *** | (2.658103) | 0.074476 *** | (3.228029) |
lnRTE | 0.22668 *** | (4.255919) | 0.117734 *** | (3.479601) | 0.082713 ** | (2.383868) | 0.120634 *** | (3.387609) |
W*lnWP | −0.545619 *** | (−3.262531) | −0.002428 | (−0.011857) | −0.036972 | (−0.176001) | −0.177213 | (−0.815479) |
W*lnIAV | −0.352355 *** | (−2.788412) | 0.803933 *** | (4.56538) | 0.232655 | (1.052079) | 0.398847 * | (1.796506) |
W*lnFAI | 0.475544 *** | (3.075947) | −0.260073 *** | (−3.336868) | −0.317381 *** | (−4.166166) | −0.297505 *** | (−3.706654) |
W*lnCR | 0.49059 *** | (7.783382) | 0.065306 * | (1.820832) | 0.010556 | (0.288807) | 0.032331 | (0.843988) |
W*lnRTE | 0.111858 | (1.336613) | −0.111432 ** | (−2.302324) | −0.235546 *** | (−2.907783) | −0.127943 | (−1.605383) |
ρ | 0.082039 | (1.218762) | 0.172016** | (2.340959) | 0.016039 | (0.206813) | 0.137008 * | |
R2 | 0.9478 | 0.995 | 0.9954 | 0.9948 | ||||
Corr-squared | 0.9461 | 0.7659 | 0.5012 | 0.1577 | ||||
σ2 | 0.1871 | 0.0189 | 0.0156 | 0.0175 | ||||
Log-likelihood | −122.72521 | 133.31188 | 145.37759 | 59.152713 |
Direct | Indirect | Total | ||||
---|---|---|---|---|---|---|
Coefficient | t-Stat | Coefficient | t-Stat | Coefficient | t-Stat | |
lnWP | 0.191349 ** | (2.090209) | 0.043921 | (0.18183) | 0.23527 | (0.853011) |
lnIAV | −0.983596 *** | (−8.958051) | 0.717703 *** | (3.760386) | −0.265893 | (−1.247669) |
lnFAI | −0.005691 | (−0.09889) | −0.29469 *** | (−3.220505) | −0.300381 ** | (−2.744674) |
lnCR | 0.085909 *** | (3.576337) | 0.090861 ** | (2.146368) | 0.17677 *** | (3.121794) |
lnRTE | 0.113068 *** | (3.498257) | −0.10465 * | (−2.021752) | 0.008418 | (0.169586) |
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Han, Y.; Jin, B.; Qi, X.; Zhou, H. Influential Factors and Spatiotemporal Characteristics of Carbon Intensity on Industrial Sectors in China. Int. J. Environ. Res. Public Health 2021, 18, 2914. https://doi.org/10.3390/ijerph18062914
Han Y, Jin B, Qi X, Zhou H. Influential Factors and Spatiotemporal Characteristics of Carbon Intensity on Industrial Sectors in China. International Journal of Environmental Research and Public Health. 2021; 18(6):2914. https://doi.org/10.3390/ijerph18062914
Chicago/Turabian StyleHan, Ying, Baoling Jin, Xiaoyuan Qi, and Huasen Zhou. 2021. "Influential Factors and Spatiotemporal Characteristics of Carbon Intensity on Industrial Sectors in China" International Journal of Environmental Research and Public Health 18, no. 6: 2914. https://doi.org/10.3390/ijerph18062914