|
on China |
| By: | Yuan, Zhiming; Fan, Shenggen; Zhang, Yumei; Wang, Jingjing; Meng, Ting |
| Abstract: | In the current Chinese diets, merely 14% of residents adhere to the recent dietary guidelines. The excessive consumption of red meat presents significant health and environmental challenges, leading to increased pressure on protein feed imports. This study proposes a pragmatic solution wherein the entire population partially replaces red meat with soybeans in China, and evaluates the impacts. Employing meta-analysis and counterfactual analysis, we investigate the correlations between food intake and disease risk, calculating avoidable mortality and the associated disease burden. Consuming 50g/day of soybeans may prevent 1.2 million deaths annually, saving $250.74 million indirect costs and $3.52 billion in direct medical expenses. Through substituting, completely eliminating the population exceeding 100g daily red meat intake in China could preventing 0.28 million deaths, and saving $247.66 million indirect and $2.06 billion direct medical costs. Furthermore, utilizing a partial equilibrium model, we projected the regional impacts and costs of following the recommended soybean consumption on water use, land use, carbon, nitrogen, and phosphorus emissions. Through dynamic data validation, estimating a 19.6% reduction in carbon emissions, 5.4% less water use, 26.2% lower nitrogen footprint, and 24.6% less phosphorus footprint. These findings offer valuable evidence for improving agricultural economic policies and strategies in China. |
| Keywords: | Agricultural and Food Policy |
| Date: | 2024–08–07 |
| URL: | https://d.repec.org/n?u=RePEc:ags:iaae24:344309 |
| By: | International Monetary Fund |
| Abstract: | China is exposed to both transition and physical risks from climate change. As a large greenhouse gas emitter, China faces noticeable transition risks. Coal and oil dominate energy supply amidst growing global momentum towards a low carbon greener economy where China also has made notable progress in green energy transition. China is also highly exposed to economic damage from hydrometeorological hazards such as floods, typhoons, and extreme temperatures, as the probability and intensity of (extreme) climate events is expected to increase. The FSAP mission analyzed transition risk using a data set obtained from the 20 D-SIBS through an ad-hoc data request. Physical risks were analyzed using publicly available data at provincial level.2 |
| Date: | 2025–11–14 |
| URL: | https://d.repec.org/n?u=RePEc:imf:imfscr:2025/303 |
| By: | Yumou Wang; Siu Kei Wong |
| Abstract: | This paper examines the relationship between housing policy interventions and market returns in China with a novel policy index. Using natural language processing techniques, we construct a sentence-level policy index based on government policy documents from 2008 to 2024. The policy index identifies three loosening periods and two tightening periods in Chinese housing market. We find that the policy index is associated with higher long-term housing returns. The mechanism analysis shows that policy effects on housing returns are transmitted primarily through supply channels (44%) and demand channels (16%). Notably, our findings reveal that only loosening measures are effective in stabilizing housing returns. Granger causality tests uncover an asymmetric temporal relationship: while housing returns Granger-cause policy changes across all time horizons, policy interventions demonstrate causality only for long-term housing returns, suggesting a delayed impact of policy implementation on market outcomes. |
| Keywords: | Housing Policy; Housing return; Machine Learning; Natural Language Processing |
| JEL: | R3 |
| Date: | 2025–01–01 |
| URL: | https://d.repec.org/n?u=RePEc:arz:wpaper:eres2025_261 |
| By: | Siming Chen |
| Abstract: | As China’s real estate market faces a significant downturn, understanding and forecasting the dynamics of housing prices and real estate companies’ stock performance has become increasingly critical. This study explores the relationship between housing price trends and public sentiment, using quantitative analyses to uncover the intricate interplay of these factors. By leveraging housing price data from China’s top 50 cities and sentiment analysis of comments on social media, and further scoring public sentiment using SnowNLP and Maximum Information Coefficient (MIC) to identify the correlation between sentiment score and housing prices. The results indicate that some of the cities have significant correlation between sentiment score and housing price. This research offers novel insights into how public sentiment influences the real estate market, providing evidence and reference for further study in not only Chinese social media but also other languages’ social media regarding real estate market. |
| Keywords: | Correlation; real estate; Sentiment; Social Media |
| JEL: | R3 |
| Date: | 2025–01–01 |
| URL: | https://d.repec.org/n?u=RePEc:arz:wpaper:eres2025_238 |