The relationship between urbanization and the ecological environment is not conclusive in the literature. We used the provincial data from China from 1998 to 2019 to empirically study the relationship between the urbanization ratio and ecological environment which is proxied by the NDVI (normalized difference vegetation index) from the remote sensing data. The results indicate that the coefficient of the interaction between urbanization and per capita GDP is statistically significant and negative (−0.0946), while the coefficient of urbanization itself is very trivial and not statistically significant. It implies that urbanization could reduce ecological quality, particularly for the high-income regions. The higher the urbanization ratio is, the larger the negative impact is. It implies that urban expansion does reduce the ecological environment. The effect of economic development level on the NDVI can be divided into three stages: the one where the NDVI improves with the increase of GDP per capita (urbanization rate of less than 77.59%), the one where the value of the NDVI is not affected by the level of GDP per capita (urbanization rate equal to 77.59%), and the one where the NDVI decreases with the increase of GDP per capita (urbanization rate of more than 77.59%).

Purpose: The study uses machine learning techniques to cluster regional retail egg prices after 2000 in China. Furthermore, it combines machine learning results with econometric models to study determinants of cluster affiliation. Eggs are an inexpensiv, nutritious and sustainable animal food. Contextually, China is the largest country in the world in terms of both egg production and consumption. Regional clustering can help governments to imporve the precision of price policies and help producers make better investment decisions. The results are purely driven by data.Design/methodology/approach: The study introduces dynamic time warping (DTW) algorithm which takes into account time series properties to analyze provincial egg prices in China. The results are compared with several other algorithms, such as TADPole. DTW is superior, though it is computationally expensive. After the clustering, a multinomial logit model is run to study the determinants of cluster affiliation.Findings: The study identified three clusters. The first cluster including 12 provinces and the second cluster including 2 provinces are the main egg production provinces and their neighboring provinces in China. The third cluster is mainly egg importing regions. Clusters 1 and 2 have higher price volatility. The authors confirm that due to transaction costs, the importing areas may have less price volatility.Practical implications: The machine learning techniques could help governments make more precise policies and help producers make better investment decisions.Originality/value: This is the first paper to use machine learning techniques to cluster food prices. It also combines machine learning and econometric models to better study price dynamics.

This project is based on the climate characteristics of Guangxi, combined with regional traditional construction methods and vernacular construction materials, and mainly in the light of the passive green building technology system guided by the principle of less manpower and more natures features, which saves early investment and the cost of operation and maintenance, further verifies the effectiveness of the strategy after completion, providing a reference for the development and application of related passive building technology.