Environmental performance rating and disclosure - China's green-watch program

This paper describes a new incentive-based pollution control program in China in which the environmental performance of firms is rated and reported to the public. Firms are rated from best to worst using five colors-green, blue, yellow, red, and black-and the ratings are disseminated to the public through the media. The impact has been substantial, suggesting that public disclosure provides a significant incentive for firms to improve their environmental performance. The paper focuses on the experience of the first two disclosure programs, in Zhenjiang, Jiangsu Province and Hohhot, Inner Mongolia. Successful implementation of these programs at two very different levels of economic and institutional development suggests that public disclosure should be feasible in most of China. The Zhenjiang and Hohhot experiences have also shown that performance disclosure can significantly reduce pollution, even in settings where environmental nongovernmental organizations are not very active and there is no formal channel for public participation in environmental regulation.Environmental Economics&Policies,Public Health Promotion,Decentralization,Water and Industry,Health Monitoring&Evaluation,Environmental Economics&Policies,Water and Industry,Health Monitoring&Evaluation,National Governance,Health Economics&Finance

Object Detection in Foggy Scenes by Embedding Depth and Reconstruction into Domain Adaptation

Most existing domain adaptation (DA) methods align the features based on the domain feature distributions and ignore aspects related to fog, background and target objects, rendering suboptimal performance. In our DA framework, we retain the depth and background information during the domain feature alignment. A consistency loss between the generated depth and fog transmission map is introduced to strengthen the retention of the depth information in the aligned features. To address false object features potentially generated during the DA process, we propose an encoder-decoder framework to reconstruct the fog-free background image. This reconstruction loss also reinforces the encoder, i.e., our DA backbone, to minimize false object features.Moreover, we involve our target data in training both our DA module and our detection module in a semi-supervised manner, so that our detection module is also exposed to the unlabeled target data, the type of data used in the testing stage. Using these ideas, our method significantly outperforms the state-of-the-art method (47.6 mAP against the 44.3 mAP on the Foggy Cityscapes dataset), and obtains the best performance on multiple real-image public datasets. Code is available at: https://github.com/VIML-CVDL/Object-Detection-in-Foggy-ScenesComment: Accepted by ACC