Climate Change: National and Local Policy Opportunities in China

Climate Change poses a wide range of potentially very severe threats in China. This aggravates the existing vulnerability of China and is one of the big challenges faced by the Chinese government. Adaptation programmes and projects are being developed and implemented at national and local level. As China is engaged in heavy investment in infrastructure development as a consequence of the rapid process of development and urbanization, mainstreaming adaptation into such development process is a priority for China. China has also made positive contributions to reducing greenhouse gas emissions through participations in the CDM under the Kyoto Protocol framework. Although mitigation is not a priority at national or local level, it has been integrated into national and local development plans explicitly. This paper addresses the following questions: What is the policy space for climate change mitigation and adaptation policy at national and local level and what is already being done? The three case studies at local level - Beijing, Guangdong and Shanghai – presented here, highlight the local benefits in terms of local pollution of integrating mitigation policies into local development. However, financial constraints usually prevent such a positive policy integration. National policies and international cooperation aiming at bridging the financial gap and promoting technology transfer would help in integrating local pollution control and mitigation efforts in China today.Climate Change, Local Policy, National Policy, Mitigation, Local Pollution

Using Parametric and Residual-based Bootstrap to Assess the Absolute Goodness-of-fit for State Space Model

In this work, two types of bootstrap methods are used to evaluate the absolute goodness-of-fit for the linear state space model. The first bootstrap is called parametric bootstrap, and the second one is called the residual-based bootstrap. The results from the two bootstrap methods are similar, but both bootstrap methods failed to detect the model misspecification introduced for the state space model considered

Penalized Estimation of Directed Acyclic Graphs From Discrete Data

Bayesian networks, with structure given by a directed acyclic graph (DAG), are a popular class of graphical models. However, learning Bayesian networks from discrete or categorical data is particularly challenging, due to the large parameter space and the difficulty in searching for a sparse structure. In this article, we develop a maximum penalized likelihood method to tackle this problem. Instead of the commonly used multinomial distribution, we model the conditional distribution of a node given its parents by multi-logit regression, in which an edge is parameterized by a set of coefficient vectors with dummy variables encoding the levels of a node. To obtain a sparse DAG, a group norm penalty is employed, and a blockwise coordinate descent algorithm is developed to maximize the penalized likelihood subject to the acyclicity constraint of a DAG. When interventional data are available, our method constructs a causal network, in which a directed edge represents a causal relation. We apply our method to various simulated and real data sets. The results show that our method is very competitive, compared to many existing methods, in DAG estimation from both interventional and high-dimensional observational data.Comment: To appear in Statistics and Computin

M\"{o}bius disjointness for a class of exponential functions

A vast class of exponential functions is showed to be deterministic. This class includes functions whose exponents are polynomial-like or "piece-wise" close to polynomials after differentiation. Many of these functions are indeed disjoint from the M\"obius function. As a consequence, we show that Sarnak's Disjointness Conjecture for the M\"obius function (from deterministic sequences) is equivalent to the disjointness in average over short intervalsComment: 37 pages. To better understand the main results of this paper, we split it into two independent papers. The second paper is arXiv:2101.1013