Resilient Monotone Submodular Function Maximization

In this paper, we focus on applications in machine learning, optimization, and control that call for the resilient selection of a few elements, e.g. features, sensors, or leaders, against a number of adversarial denial-of-service attacks or failures. In general, such resilient optimization problems are hard, and cannot be solved exactly in polynomial time, even though they often involve objective functions that are monotone and submodular. Notwithstanding, in this paper we provide the first scalable, curvature-dependent algorithm for their approximate solution, that is valid for any number of attacks or failures, and which, for functions with low curvature, guarantees superior approximation performance. Notably, the curvature has been known to tighten approximations for several non-resilient maximization problems, yet its effect on resilient maximization had hitherto been unknown. We complement our theoretical analyses with supporting empirical evaluations.Comment: Improved suboptimality guarantees on proposed algorithm and corrected typo on Algorithm 1's statemen

Energy and Industrial Growth in India: The Next Emissions Superpower?

India is often referred to as the next development superpower and is widely seen as a potential destination for large scale manufacturing hubs. In this work we draw comparisons between India, Indonesia and China and find that all countries have a carbon intensive energy sector. However, there is a staggering difference between industrial energy intensity between them where India and Indonesia require double the amount of energy to produce the same output as China. We look into the decomposed industrial sectors and find that iron and steel and non-metallic minerals present the highest energy intensity in India. We argue that a production transition from China to India and Indonesia would result in a dangerous global emissions growth which has to be countered with rapid adoption of innovative energy technologies and policies

China’s electricity emission intensity in 2020 – an analysis at provincial level

In order to maintain the 2°C climate change target, global carbon intensity of electricity generation needs to achieve a short-term target of 600 g/kWh by 2020. This target is important for China, which has been the largest consumer and producer of electricity since 2011. China has set ambitious targets to reduce its electricity carbon intensity in the 13th five-year plan. For a country as large as China, the outcomes of these policies rely on the implementation strategies and effectiveness of each province. In this study, we estimate the carbon intensities of power generation in China’s provinces by 2020. Results show that despite progress in renewable energy growth most provinces are expected to have carbon intensities well above 600 g/kWh by 2020. Renewable energy sources can help reduce carbon intensities in most provinces, but the magnitude of such impacts depends on the coordination among provinces. The over-dependence on coal power generation has made carbon capture and storage a necessity for China’s provinces to reduce their carbon intensity for power generation. Therefore, government support should be addressed sooner rather than later