In 2014, China introduced an ultra-low emissions (ULE) standards policy for renovating coal-fired power-generating units to limit SO2, NOX and PM emissions to 35, 50 and 10 mg m-3 , respectively. The ULE standard policy had ambitious levels (surpassing those of all other countries) and implementation timeline. We estimate emission reductions associated with the ULE policy by constructing a nationwide, unit-level, hourly-frequency emissions dataset using data from a continuous emission monitoring systems network covering 96-98% of Chinese thermal power capacity during 2014-2017. We find that between 2014 and 2017 China's annual power emissions of SO2, NOX and PM dropped by 65%, 60% and 72%, respectively. Our estimated emissions using actual monitoring data are 18-92% below other recent estimates. We detail the technologies used to meet the ULE standards and the determinants of compliance, underscoring the importance of ex-post evaluation and providing insights for other countries wishing to reduce their power emissions.

Abstract:To improve the accuracy of change detection in urban areas using bi-temporal high-resolution remote sensing images, a novel object-based change detection scheme combining multiple features and ensemble learning is proposed in this paper. Image segmentation is conducted to determine the objects in bi-temporal images separately. Subsequently, three kinds of object features, i.e., spectral, shape and texture, are extracted. Using the image differencing process, a difference image is generated and used as the input for nonlinear supervised classifiers, including k-nearest neighbor, support vector machine, extreme learning machine and random forest. Finally, the results of multiple classifiers are integrated using an ensemble rule called weighted voting to generate the final change detection result. Experimental results of two pairs of real high-resolution remote sensing datasets demonstrate that the proposed approach outperforms the traditional methods in terms of overall accuracy and generates change detection maps with a higher number of homogeneous regions in urban areas. Moreover, the influences of segmentation scale and the feature selection strategy on the change detection performance are also analyzed and discussed.