The economics of Kyoto flexible mechanisms: a survey

Scientific study has revealed that man-made greenhouse gas emissions are causing global warming. A milestone of international effort for combating global warming was the establishment of the Kyoto Protocol to the United Nations Framework Convention on Climate Change in 1997. The Protocol sets out a 5% emission reduction target from 1990 level for Annex I region in 2010. Economic research has foreseen that it will be very costly for some industrial countries to comply the target individually and therefore suggested international co-operation through flexible mechanisms - Emissions Trading, Joint Implementation and Clean Development Mechanism. Because main greenhouse gases have the uniform-mixing feature, the use of the flexible mechanisms could be both environmentally and economically effective. Recently emerged a number of theoretical and empirical studies to explore the economics of the mechanisms. This paper is intended to survey the existing research results with the mechanisms, including conceptual clarifications, cost and benefit analysis, potential problems, empirical findings, and perspectives for future research.global warming; Kyoto Protocol; flexible mechanisms

Incremental Learning Method for Data with Delayed Labels

Most research on machine learning tasks relies on the availability of true labels immediately after making a prediction. However, in many cases, the ground truth labels become available with a non-negligible delay. In general, delayed labels create two problems. First, labelled data is insufficient because the label for each data chunk will be obtained multiple times. Second, there remains a problem of concept drift due to the long period of data. In this work, we propose a novel incremental ensemble learning when delayed labels occur. First, we build a sliding time window to preserve the historical data. Then we train an adaptive classifier by labelled data in the sliding time window. It is worth noting that we improve the TrAdaBoost to expand the data of the latest moment when building an adaptive classifier. It can correctly distinguish the wrong types of source domain sample classification. Finally, we integrate the various classifiers to make predictions. We apply our algorithms to synthetic and real credit scoring datasets. The experiment results indicate our algorithms have superiority in delayed labelling setting

FFHQ-UV: Normalized Facial UV-Texture Dataset for 3D Face Reconstruction

We present a large-scale facial UV-texture dataset that contains over 50,000 high-quality texture UV-maps with even illuminations, neutral expressions, and cleaned facial regions, which are desired characteristics for rendering realistic 3D face models under different lighting conditions. The dataset is derived from a large-scale face image dataset namely FFHQ, with the help of our fully automatic and robust UV-texture production pipeline. Our pipeline utilizes the recent advances in StyleGAN-based facial image editing approaches to generate multi-view normalized face images from single-image inputs. An elaborated UV-texture extraction, correction, and completion procedure is then applied to produce high-quality UV-maps from the normalized face images. Compared with existing UV-texture datasets, our dataset has more diverse and higher-quality texture maps. We further train a GAN-based texture decoder as the nonlinear texture basis for parametric fitting based 3D face reconstruction. Experiments show that our method improves the reconstruction accuracy over state-of-the-art approaches, and more importantly, produces high-quality texture maps that are ready for realistic renderings. The dataset, code, and pre-trained texture decoder are publicly available at https://github.com/csbhr/FFHQ-UV.Comment: The dataset, code, and pre-trained texture decoder are publicly available at https://github.com/csbhr/FFHQ-U

Inference of nonlinear causal effects with GWAS summary data

Large-scale genome-wide association studies (GWAS) have offered an exciting opportunity to discover putative causal genes or risk factors associated with diseases by using SNPs as instrumental variables (IVs). However, conventional approaches assume linear causal relations partly for simplicity and partly for the only availability of GWAS summary data. In this work, we propose a novel model {for transcriptome-wide association studies (TWAS)} to incorporate nonlinear relationships across IVs, an exposure, and an outcome, which is robust against violations of the valid IV assumptions and permits the use of GWAS summary data. We decouple the estimation of a marginal causal effect and a nonlinear transformation, where the former is estimated via sliced inverse regression and a sparse instrumental variable regression, and the latter is estimated by a ratio-adjusted inverse regression. On this ground, we propose an inferential procedure. An application of the proposed method to the ADNI gene expression data and the IGAP GWAS summary data identifies 18 causal genes associated with Alzheimer's disease, including APOE and TOMM40, in addition to 7 other genes missed by two-stage least squares considering only linear relationships. Our findings suggest that nonlinear modeling is required to unleash the power of IV regression for identifying potentially nonlinear gene-trait associations. Accompanying this paper is our Python library nl-causal(https://github.com/nl-causal/nonlinear-causal) that implements the proposed method.Comment: 36 pages, 8 figure

China's trade-off between economic benefits and sulfur dioxide emissions in changing global trade

China has been suffering from air quality degradation since its ascension into the World Trade Organization in 2001. The unequal exchange that occurs with international trade—that is, developed countries obtaining larger shares of trade‐related value added relative to the shares of trade‐related air pollution incurred locally—may obstruct the greening of global supply chains. In this study, we conduct a multi‐regional input‐output analysis to examine the change in the distribution of economic benefits and sulfur dioxide emissions underlying China's international trade from 2002 to 2015. The results show that both net trade‐related economic benefits and SO2 emissions in China rapidly increased from 2002 to 2007 and then decelerated after 2007 due to changes in China's green development strategy. In the past 13 years, China has suffered from economic‐environmental inequality due to trade with most developed countries, for example, the United States, the European Union, East Asia, and Canada. East Asia, particularly Japan and South Korea, became both an economic and environmental winner while trading with China in 2015. China has also outsourced emissions to less developed regions, such as Sub‐Saharan Africa. We propose policy implications to further reduce the economic‐environmental inequality underlying China's international trade

An Integrated Model for Marine Fishery Management: Linking Ecosystem and Socio-Economic Systems

The paper devises an integrated ecological–economics–social model to assess the implementation of ecosystem-based fisheries management in the Pearl River Estuary (PRE) in the South China Sea (SCS). In particular, this paper presents the development of an integrated model, which links a regional economics social accounting matrix (SAM) model to an ecological model constructed using Ecopath with Ecosim (EwE) software. The impacts on the ecological–economics–social system are examined by varying fishing efforts for four scenarios, including status quo management, fishing effort reduction policy, fishing gear switch policy, and summer closure extension policy. Key results from the predictions (2010–2020) and policy simulations illustrate that the collapse effect is apparent in the status quo scenario. Further, re-ducing or switching of fishing effort (e.g. elimination of overfishing and reduced habitat disturbance) positively affects the ecosystem and can lead to economic and social welfare gains in the PRE's economy. The gear switch scenario presents a compromise among the economics, social, and conservation metrics, and also outperforms other scenarios in terms of biomass at the end of the simulation period. The fishing effort reduction policy performs better than the summer closure extension policy in terms of the con- servation metrics but does relatively poorly in economic terms