China\u27s domestic and international policies on global warming : explanations and assessment

Global warming and resulting climate change present the world with major and potentially devastating challenges. China is among the countries that will suffer the effects of climate change. Although its per captia emissions of pollutants causing global warming remain relatively low compared to the world’s richest countries, China is now the second largest global polluter, and in coming decades it will overtake the United States as the world’s largest polluter of the global atmosphere. How China responds to this problem has profound implications for its people, its neighbors and the world. China has joined with other countries in debating this issue, and it has started to implement programs and policies to reduce its emissions of so-called greenhouse gases. However, its domestic actions have rarely been motivated by concerns about the global impacts of climate change, and its response has been to avoid international regulation while waiting for the developed countries to act. Given this response, short of radical change in politics and environment, it is unlikely that China will adequately restrain its greenhouse gas emissions, thus mirroring—to the detriment if all—the industrialization and growth of the world’s wealthy countries

Digesting omni-video along routes for navigation

poster abstractOmni-directional video records complete visual information along a route. Though replaying an omni-video presents reality, it requires significant amount of memory and communication bandwidth. This work extracts distinct views from an omni-video to form a visual digest named route sheet for navigation. We sort scenes at the motion and visibility level and investigate the similarity/redundancy of scenes in the context of a route. We use source data from 3D elevation map or omni-videos for the view selection. By condensing the flow in the video, our algorithm can generate distinct omni-view sequences with visual information as rich as the omni-video for further scene indexing and navigation with GIS data

Two logics of climate change games : environmental governance and know-how competition

Global warming and the resulting climate change present the world with major and potentially devastating challenges. They lead to environmental degradation/scarcity and a radical reform of the energy mix among industrial countries, in addition to other nontraditional security concerns.2 From the 1992 Rio Summit through the Kyoto Conference and the Bali Roadmap, a generation has passed since the world\u27s governments began to seriously consider the problems of global warming and the resulting climate change. It is now patently clear that the world should get together to combat the climate disaster. However, we are always confused with two questions: why has global climate governance been so difficult, and what factors hamper the effectiveness of international cooperation. This article will give an explanation to two logics of climate change games by linking environmental governance and know-how competition. For those concerned about climate change, collective actions and regimes designed to limit carbon emissions are at the core of global warming concerns. However, preventing catastrophic climate change is actually an energy challenge that leads to dramatic know-how competition in both new and alternative energy. In the international collective action against global warming, on the one hand, the pursuit of rational common goods leads to cooperation; on the other hand, the pursuit of rational self-interest or preference (in carbon emissions and energy know-how) among different states often frustrates international cooperation. Thus there are two logics of the climate change games: the logic of collective action in international environmental cooperation; and the logic of power competition in energy innovation, which is the foundation of power transition in this century. The energy revolution induced by global warming includes the discovery and exclusive possession of new energy sources, along with revolutionary progress in the promotion and application of new energy technology, improved social and economic efficiency, and government control over energy use. Not surprisingly, the transition of power and hegemony in the future will most likely be connected to energy. In the fight against global warming, Western countries not only need to deal with the failure of collective action among Annex I countries (developed countries),3 but also resolve interest conflicts between themselves. They also have to encourage developing countries to share concrete responsibilities in global governance on global climate change. Particularly, the rich countries—the European Union, Japan and the United States—have and will continue to achieve domination in the process of climate change politics, and they will compete for leadership in new and alternative energy, which is at the core of a low-carbon economy. China, which is the world\u27s largest economic powerhouse and polluter, is central to both regional and global efforts against global warming, particularly in the post-Kyoto climate negotiations. The two-layer games in global climate change politics will pose double challenges on the country’s domestic political economy and diplomacy. China’s current development route is still a growth-oriented, unsustainable and resource-constrained economic model, and the country faces the crucial need to promote development while joining the global struggle against global warming and contributing to global economic growth. The government in Beijing seeks to act as a “responsible stakeholder in the international system,” while pursuing a “scientific outlook on development” in its national economic development. On the one hand, given the growing absolute carbon emissions, China has turned into an “environmental superpower;”4on the other hand, its energy-intensive economy is not only pushing up growth rates in the United States, Japan, the EU and other economies, but also strengthening the capacity building for low-carbon and new energy. With these considerations in mind, in this paper, I look at some of the consequences and characteristics of the two-layer games in global climate change politics. Then I describe international and domestic implications for China

Explicit Planning Helps Language Models in Logical Reasoning

Language models have been shown to perform remarkably well on a wide range of natural language processing tasks. In this paper, we propose a novel system that uses language models to perform multi-step logical reasoning. Our system incorporates explicit planning into its inference procedure, thus able to make more informed reasoning decisions at each step by looking ahead into their future effects. In our experiments, our full system significantly outperforms other competing systems. On a multiple-choice question answering task, our system performs competitively compared to GPT-3-davinci despite having only around 1.5B parameters. We conduct several ablation studies to demonstrate that explicit planning plays a crucial role in the system's performance

Road Planning for Slums via Deep Reinforcement Learning

Millions of slum dwellers suffer from poor accessibility to urban services due to inadequate road infrastructure within slums, and road planning for slums is critical to the sustainable development of cities. Existing re-blocking or heuristic methods are either time-consuming which cannot generalize to different slums, or yield sub-optimal road plans in terms of accessibility and construction costs. In this paper, we present a deep reinforcement learning based approach to automatically layout roads for slums. We propose a generic graph model to capture the topological structure of a slum, and devise a novel graph neural network to select locations for the planned roads. Through masked policy optimization, our model can generate road plans that connect places in a slum at minimal construction costs. Extensive experiments on real-world slums in different countries verify the effectiveness of our model, which can significantly improve accessibility by 14.3% against existing baseline methods. Further investigations on transferring across different tasks demonstrate that our model can master road planning skills in simple scenarios and adapt them to much more complicated ones, indicating the potential of applying our model in real-world slum upgrading. The code and data are available at https://github.com/tsinghua-fib-lab/road-planning-for-slums.Comment: KDD'2