The Low-Carbon City Pilot Policy and Urban Land Use Efficiency:A Policy Assessment from China

Against the backdrop of severe global warming, the low-carbon city pilot policy, with carbon emission reduction as its main objective, is an important initiative for China to fulfil its international commitment to carbon emission reduction and promote a green and low-carbon development strategy. However, none of the literature has yet evaluated whether the pilot low-carbon city policy promotes urban land use efficiency as a policy effect. In view of this, this paper measures urban land use efficiency from a low-carbon perspective using a global reference super-efficiency SBM model based on data from 186 prefecture-level cities in China from 2005–2017, and subsequently constructs a difference-in-differences method to test the true impact of low-carbon city pilot policies on urban land use efficiency and carbon emissions, and uses a propensity score matching method to test its robustness. It is found that: (1) the average level of urban land use efficiency in China is low and on a downward trend; (2) overall, cities are predominantly low-efficiency cities, with only the high-efficiency cities in Guangdong Province showing spatial agglomeration; and (3) the low-carbon city pilot policy reduces carbon emissions while also negatively affecting urban land use efficiency. Accordingly, this paper puts forward corresponding policy recommendations

Urban2Vec: Incorporating Street View Imagery and POIs for Multi-Modal Urban Neighborhood Embedding

Understanding intrinsic patterns and predicting spatiotemporal characteristics of cities require a comprehensive representation of urban neighborhoods. Existing works relied on either inter- or intra-region connectivities to generate neighborhood representations but failed to fully utilize the informative yet heterogeneous data within neighborhoods. In this work, we propose Urban2Vec, an unsupervised multi-modal framework which incorporates both street view imagery and point-of-interest (POI) data to learn neighborhood embeddings. Specifically, we use a convolutional neural network to extract visual features from street view images while preserving geospatial similarity. Furthermore, we model each POI as a bag-of-words containing its category, rating, and review information. Analog to document embedding in natural language processing, we establish the semantic similarity between neighborhood ("document") and the words from its surrounding POIs in the vector space. By jointly encoding visual, textual, and geospatial information into the neighborhood representation, Urban2Vec can achieve performances better than baseline models and comparable to fully-supervised methods in downstream prediction tasks. Extensive experiments on three U.S. metropolitan areas also demonstrate the model interpretability, generalization capability, and its value in neighborhood similarity analysis.Comment: To appear in Proceedings of the Thirty-Fourth AAAI Conference on Artificial Intelligence (AAAI-20

ABC-CNN: An Attention Based Convolutional Neural Network for Visual Question Answering

We propose a novel attention based deep learning architecture for visual question answering task (VQA). Given an image and an image related natural language question, VQA generates the natural language answer for the question. Generating the correct answers requires the model's attention to focus on the regions corresponding to the question, because different questions inquire about the attributes of different image regions. We introduce an attention based configurable convolutional neural network (ABC-CNN) to learn such question-guided attention. ABC-CNN determines an attention map for an image-question pair by convolving the image feature map with configurable convolutional kernels derived from the question's semantics. We evaluate the ABC-CNN architecture on three benchmark VQA datasets: Toronto COCO-QA, DAQUAR, and VQA dataset. ABC-CNN model achieves significant improvements over state-of-the-art methods on these datasets. The question-guided attention generated by ABC-CNN is also shown to reflect the regions that are highly relevant to the questions