| Title |
The power of attention: Government climate-risk attention and agricultural-land carbon emissions |
| ID_Doc |
33625 |
| Authors |
Chen, MH; Xiao, HY; Zhao, H; Liu, LA |
| Title |
The power of attention: Government climate-risk attention and agricultural-land carbon emissions |
| Year |
2024 |
| Published |
|
| DOI |
10.1016/j.envres.2024.118661 |
| Abstract |
Climate change is a common challenge faced by all humanity. Promoting emission and carbon reduction in agricultural land is the most important priority for addressing climate change and realizing sustainable development. Based on data from 296 prefecture-level cities in China from 2011 to 2021, this study utilizes machinelearning and text-analysis methods to construct an indicator of government climate-risk attention (GCRA). It combines a two-way fixed-effects model to investigate how GCRA affects agricultural-land carbon emissions (ALCE) and carbon intensity (ALCI) and the mechanism of the impact. The results indicate that (1) GCRA substantially reduces ALCE and ALCI, and the conclusions are robust to a battery of tests. Furthermore, (2) mechanism analysis reveals that GCRA primarily uses three mechanisms-strengthening environmental regulation, promoting agricultural green-technology innovation, and upgrading agricultural-land mechanization-to reduce ALCE and lower ALCI. Additionally, (3) heterogeneity analysis suggests that the carbon-emission reduction effect of GCRA is more significant in the east, in arid and humid climate zones, and in non-grain-producing regions. Finally, (4) spatial-spillover effect analysis and quantile regression results demonstrate that GCRA also significantly inhibits carbon emissions and the carbon intensity of nearby agricultural land, with the inhibition effect becoming more pronounced at higher levels of government attention. This study's discoveries are helpful in promoting the emission reduction and carbon sequestration of agricultural land and provide references for developing countries to cope with climate change. |
| Author Keywords |
Climate change; Agricultural land carbon emissions; Spatial spillover effects; Machine learning; Text analysis |
| Index Keywords |
Index Keywords |
| Document Type |
Other |
| Open Access |
Open Access |
| Source |
Science Citation Index Expanded (SCI-EXPANDED) |
| EID |
WOS:001219130200001 |
| WoS Category |
Environmental Sciences; Public, Environmental & Occupational Health |
| Research Area |
Environmental Sciences & Ecology; Public, Environmental & Occupational Health |
| PDF |
|