Yiyi Zhao
Biographic Data
| ID | 4976781 |
|---|---|
| NAME | Yiyi Zhao |
| GIVEN NAMES | Yiyi |
| FAMILY NAME | Zhao |
| SIGNATURE | ZHAO Y |
| AFFILIATIONS | Australian National University |
| ORCID | 0000-0002-8766-1383 |
| VERIFIED | Yes |
| TOTAL WORKS | 3 |
| TOTAL CITATIONS | 0 |
| AUTHOR COUNT | 3 |
| EDITOR COUNT | 0 |
| FIRST PUBLICATION YEAR | 2022 |
| LATEST PUBLICATION YEAR | 2026 |
| H-INDEX | 0 |
Energy poverty and unpaid work
This study is the first to investigate the impact of energy poverty on unpaid labour in rural China, utilising a five-year panel dataset spanning from 2014 to 2022. The analysis demonstrates that energy poverty contributes an additional 4.3 to 6.1 min of daily household work for rural adults in China before the COVID-19 pandemic. Households affected by energy poverty bear a greater burden of unpaid household work compared to energy-affluent house…
Interpretable machine learning method to predict the risk of pre-diabetes using a national-wide cross-sectional data
The constructed model comprises nine easily accessible predictive factors, which prove highly effective in forecasting the risk of pre-diabetes. Concurrently, we have quantified the specific impact of each predictive factor on the risk and ranked them based on their influence. This result may serve as a convenient tool for early identification of individuals at high risk of pre-diabetes, providing effective guidance for preventing the progression…
An evaluation of management effectiveness of China’s marine protected areas and implications of the 2018 Reform
No prominent works on this page.
An evaluation of management effectiveness of China’s marine protected areas and implications of the 2018 Reform
Interpretable machine learning method to predict the risk of pre-diabetes using a national-wide cross-sectional data
The constructed model comprises nine easily accessible predictive factors, which prove highly effective in forecasting the risk of pre-diabetes. Concurrently, we have quantified the specific impact of each predictive factor on the risk and ranked them based on their influence. This result may serve as a convenient tool for early identification of individuals at high risk of pre-diabetes, providing effective guidance for preventing the progression…
Energy poverty and unpaid work
This study is the first to investigate the impact of energy poverty on unpaid labour in rural China, utilising a five-year panel dataset spanning from 2014 to 2022. The analysis demonstrates that energy poverty contributes an additional 4.3 to 6.1 min of daily household work for rural adults in China before the COVID-19 pandemic. Households affected by energy poverty bear a greater burden of unpaid household work compared to energy-affluent house…
China (2 works) · Artificial Intelligence (1 works) · Artificial Intelligence in Healthcare (1 works) · Artificial neural network (1 works) · Biodiversity (1 works) · Business (1 works) · Coastal and Marine Management (1 works) · Computer Science (1 works) · Coral and Marine Ecosystems Studies (1 works) · Diabetes mellitus (1 works)