This paper analyses the driving for the emission reduction performance of industrial wastewater from 2008 to 2017 in Beijing, Tianjin and Hebei by utilizing the modified Kaya identity and the Logarithmic Mean Divisia Index (LMDI) method. As a result, we find that industrial water-saving technology and water consumption per unit of industrial GDP are the negative inhibitors of industrial wastewater discharge, while worker productivity and the scale of workers are the positive factors for industrial wastewater discharge. This study also shows that although the performance of industrial wastewater reduction in this area has improved in the last ten years, there is still room for improvement.

This paper presents human action recognition by using shape and texture based DMM-Haar features where collaborative representation classifier is adopted for action classification. In this study, we have introduced effective feature extraction technique based on Depth Motion Maps (DMMs) and Haar wavelet transformation, where different actions can be represented with a range of features. Firstly, we have calculated three DMMs such as DMM front view, top view and side view from 3D action video sequences as the shape features. After that, we have utilized Haar wavelet on the DMMs generated images to extract texture information and concatenated all features as a feature matrix. We have utilized principal component analysis for reducing the feature dimensions of the feature matrix. Finally, l2 normed based collaborative representative classification technique is adopted to classify different actions. For this research, we have analyzed the effects of the DMM-Haar features on experimental basis with DMM features based results. The performance study of the proposed method is comparable with the state-of-the-art methods to recognize human action on the publicly available Microsoft Research Action 3D dataset.