What Drives Policy Attention to Climate Change in China? An Empirical Analysis through the Lens of People’s Daily
Abstract
:1. Introduction
2. Theoretical Framework and Hypotheses
2.1. Problem Indicators
2.2. Focusing Events
2.3. Policy Knowledge
2.4. Spillovers
2.4.1. Spillover from Attention to Environmental Subsystems Other than Climate Change
2.4.2. Spillover from Abroad
3. Data and Methodology
3.1. Measuring Attention to Climate Change
3.1.1. Why?
3.1.2. How?
3.2. Measuring the Independent Variables
3.2.1. Problem Indicators
3.2.2. Focusing Events
3.2.3. Policy Knowledge
3.2.4. Attention to Environmental Issues Other than Climate Change
3.2.5. Pressure from Abroad
3.3. Auto Regressive Integrated Moving Average Model
4. Analysis
4.1. Monthly-Based Regression
4.2. Quarterly-Based Regression
5. Discussion
5.1. Attention and the Time Effect
5.2. Attention and Problem Indicators
5.3. Attention and Focusing Events
5.4. Attention and Policy Knowledge
5.5. Attention and the Trans-Subsystem Effect
5.6. Attention and the Boundary-Spanning Effect
6. Conclusions
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
Appendix A
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Focusing Events | Content |
---|---|
COP | Conferences of Parties of UNFCCC, 20 in total (1995–2013) |
Rio+ 1 | United Nations Conferences on Sustainable Development, 3 in total |
APP | Asia Pacific Partnership on Clean Development and Climate Summit, 3 in total |
G20 2 | G20 Summits, 19 in total (1999–2013) |
BRICS 3 | BRICS Summits, 10 in total (2008–2013) |
IPCC 4 | Release of IPCC Assessment Reports |
Base ARMA 1 | Model 1 | Model 2 | Model 3 | Model 4a | Model 4b | Model 5 | Model 6 | ||
---|---|---|---|---|---|---|---|---|---|
Attention | _cons | 7.94 *** | 7.88 *** | 7.73 *** | 6.06 *** | 6.25 *** | 6.23 *** | 5.14 *** | 5.28 *** |
(1.26) 3 | (1.36) | (1.24) | (1.23) | (1.34) | (0.56) | (0.92) | (0.79) | ||
ARMA | L.ar | 0.97 *** | 0.97 *** | 0.97 *** | 0.95 *** | 0.96 *** | 0.93 *** | 0.94 *** | 0.93 *** |
(0.02) | (0.02) | (0.02) | (0.03) | (0.02) | (0.04) | (0.03) | (0.03) | ||
L.ma | −0.77 *** | −0.77 *** | −0.75 *** | −0.75 *** | −0.77 *** | −0.81 *** | −0.81 *** | −0.80 *** | |
(0.04) | (0.04) | (0.04) | (0.04) | (0.04) | (0.04) | (0.04) | (0.04) | ||
H1 2 | disaster | −0.00 | 0.00 | ||||||
(0.01) | (0.01) | ||||||||
L.disaster | 0.00 | 0.00 | |||||||
(0.01) | (0.00) | ||||||||
H2 | cop | 0.32 *** | 0.17 *** | 0.18 *** | |||||
(0.07) | (0.06) | (0.06) | |||||||
rio | 0.35 * | 0.28 ** | 0.32 ** | ||||||
(0.18) | (0.14) | (0.13) | |||||||
app | −0.64 | −0.68 | |||||||
(0.41) | (0.42) | ||||||||
g20 | −0.38 | −0.19 | |||||||
(0.36) | (0.34) | ||||||||
brics | 0.55 | 1.22 | |||||||
(0.84) | (0.93) | ||||||||
ipcc | 0.51 | 0.44 | |||||||
(0.42) | (0.37) | ||||||||
H3 | knowledge | 0.05 | −0.18 | ||||||
(0.14) | (0.12) | ||||||||
L.knowledge | 0.32 ** | 0.16 | 0.11 | ||||||
(0.16) | (0.12) | (0.11) | |||||||
H4a | envi | 0.02 | 0.01 | ||||||
(0.01) | (0.01) | ||||||||
L.envi | 0.02 * | 0.02 | 0.02 | ||||||
(0.01) | (0.01) | (0.01) | |||||||
H4b | pressure_us | 0.09 ** | 0.08 ** | 0.08 ** | |||||
(0.04) | (0.04) | (0.04) | |||||||
L.pressure_us | 0.01 | 0.01 | |||||||
(0.03) | (0.02) | ||||||||
pressure_uk | 0.25 *** | 0.23 *** | 0.22 *** | ||||||
(0.05) | (0.05) | (0.05) | |||||||
L.pressure_uk | 0.07 * | 0.07 * | 0.07 * | ||||||
(0.04) | (0.04) | (0.04) | |||||||
sigma | _cons | 3.25 *** | 3.25 *** | 3.10 *** | 3.24 *** | 3.23 *** | 3.01 *** | 2.91 *** | 2.95 *** |
(0.13) | (0.13) | (0.12) | (0.13) | (0.13) | (0.11) | (0.11) | (0.11) | ||
Log Likelihood | −904.86 | −902.39 | −888.13 | −900.45 | −899.89 | −874.33 | −863.47 | −867.54 | |
AIC 4 | 1817.72 | 1816.78 | 1796.25 | 1812.91 | 1811.78 | 1764.66 | 1766.94 | 1757.08 | |
BIC 5 | 1833.13 | 1839.88 | 1834.77 | 1836.00 | 1834.88 | 1795.45 | 1843.93 | 1799.42 |
Explanatory Variables | ADF Test | Transforming Method | ADF Test |
---|---|---|---|
Policy knowledge | Z(t) = −0.486 p-value = 0.8948 | Square root/difference | Z(t) = −14.948 p-value = 0.0000 |
Trans-subsystem effect | Z(t) = −2.856 p-value = 0.0507 | Square root/difference | Z(t) = −13.846 p-value = 0.0000 |
Boundary-spanning effect | Z(t) = −2.253 p-value = 0.1875 | Square root/difference | Z(t) = −17.447 p-value = 0.0000 |
Base ARMA 1 | Model 1 | Model 2 | Model 3 | Model 4a | Model 4b | Model 5 | Model 6 | ||
---|---|---|---|---|---|---|---|---|---|
attention | _cons | 0.27 | 0.22 | 0.23 | 0.34 | 0.13 | −0.38 | −0.63 | −0.45 |
(0.88) 3 | (0.91) | (0.87) | (0.80) | (0.85) | (0.65) | (0.63) | (0.59) | ||
ARMA | L3.ar | −0.19 ** | −0.19 ** | −0.20 * | −0.17 | −0.19 ** | −0.23 ** | −0.24 *** | −0.21 ** |
(0.09) | (0.09) | (0.10) | (0.10) | (0.09) | (0.09) | (0.09) | (0.09) | ||
L7.ar | −0.48 *** | −0.50 *** | −0.48 *** | −0.50 *** | −0.55 *** | −0.38 ** | −0.48 *** | −0.43 *** | |
(0.13) | (0.13) | (0.13) | (0.12) | (0.11) | (0.16) | (0.12) | (0.12) | ||
L.ma | −0.42 *** | −0.41 *** | −0.40 *** | −0.43 *** | −0.45 *** | −0.38 *** | −0.43 *** | −0.46 *** | |
(0.11) | (0.11) | (0.14) | (0.10) | (0.11) | (0.13) | (0.16) | (0.12) | ||
L7.ma | 0.30 ** | 0.32 ** | 0.32 ** | 0.28 ** | 0.36 *** | 0.15 | 0.23 ** | 0.21 * | |
(0.13) | (0.13) | (0.13) | (0.13) | (0.12) | (0.14) | (0.12) | (0.12) | ||
H1 2 | D.disaster | 0.01 | 0.01 | ||||||
(0.01) | (0.01) | ||||||||
LD.disaster | 0.02 | −0.00 | |||||||
(0.02) | (0.02) | ||||||||
H2 | D.cop | 1.01 *** | 0.00 | −0.06 | |||||
(0.37) | (0.43) | (0.41) | |||||||
D.rio | 0.95 | −1.52 * | |||||||
(0.77) | (0.79) | ||||||||
D.app | −0.90 | −1.57 | |||||||
(3.74) | (2.94) | ||||||||
D.g20 | −4.12 * | 0.32 | 0.11 | ||||||
(2.40) | (2.38) | (2.35) | |||||||
D.brics | −0.51 | 1.52 | |||||||
(5.23) | (4.73) | ||||||||
D.ipcc | 1.71 | 1.12 | |||||||
(2.11) | (1.81) | ||||||||
H3 | D.knowledge | 2.02 | 0.47 | ||||||
(1.96) | (1.52) | ||||||||
LD.knowledge | −2.29 | 0.28 | |||||||
(1.79) | (1.15) | ||||||||
H4a | D.envi | 3.43 *** | 2.61 ** | 2.36 ** | |||||
(1.17) | (1.24) | (0.98) | |||||||
LD.envi | −0.79 | 0.26 | |||||||
(0.94) | (1.18) | ||||||||
H4b | D.pressure_us | 8.37 *** | 7.93 *** | 7.20 *** | |||||
(1.63) | (1.90) | (1.75) | |||||||
LD.pressure_us | 0.84 | 0.76 | |||||||
(1.41) | (1.47) | ||||||||
D.pressure_uk | 9.12 *** | 9.95 *** | 9.99 *** | ||||||
(2.31) | (3.14) | (2.58) | |||||||
LD.pressure_uk | −3.58 | −3.31 | |||||||
(2.49) | (2.60) | ||||||||
sigma | _cons | 17.56 *** | 17.45 *** | 16.74 *** | 17.33 *** | 17.00 *** | 14.18 *** | 13.70 *** | 13.97 *** |
(1.35) | (1.34) | (1.24) | (1.34) | (1.36) | (0.94) | (0.91) | (0.93) | ||
Log Likelihood | −493.25 | −488.28 | −487.72 | −487.54 | −485.51 | −464.56 | −460.80 | −466.94 | |
AIC 4 | 998.49 | 992.56 | 999.45 | 991.09 | 987.01 | 949.11 | 963.60 | 955.88 | |
BIC 5 | 1014.96 | 1014.45 | 1032.39 | 1012.98 | 1008.90 | 976.47 | 1021.06 | 986.08 |
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Fan, S.; Xue, L.; Xu, J. What Drives Policy Attention to Climate Change in China? An Empirical Analysis through the Lens of People’s Daily. Sustainability 2018, 10, 2977. https://doi.org/10.3390/su10092977
Fan S, Xue L, Xu J. What Drives Policy Attention to Climate Change in China? An Empirical Analysis through the Lens of People’s Daily. Sustainability. 2018; 10(9):2977. https://doi.org/10.3390/su10092977
Chicago/Turabian StyleFan, Shiwei, Lan Xue, and Jianhua Xu. 2018. "What Drives Policy Attention to Climate Change in China? An Empirical Analysis through the Lens of People’s Daily" Sustainability 10, no. 9: 2977. https://doi.org/10.3390/su10092977