MANAGING THE CLIMATE RENT: HOW CAN REGULATORS IMPLEMENT INTERTEMPORALLY EFFICIENT MITIGATION POLICIES?

This paper provides a formal framework to analyze informational and commitment requirements of several intertemporal price and quantity instruments for mitigating global warming. We ask under what conditions and to what extend the regulator can shift the complex and daunting intertemporal optimization of fossil resource use to markets. Mitigation always generates an intertemporal climate rent which reflects the stock‐dependent damages and emerging scarcities of the atmospheric carbon deposit. In order to calculate and to manage this climate rent appropriately, common policy instruments like Pigouvian taxes or emissions trading presume perfect information about resource demand, extraction costs, reserve sizes, and damages for the entire planning horizon. To reduce these informational requirements we develop an alternative policy approach—a state dependent tax rule—that relies only on current observations of cumulative extraction (or atmospheric carbon concentration). Within a cost–benefit analysis, this instrument is capable to shift the complex intertemporal optimization problem completely to the resource sector when resource owners are homogeneous. Under a cost‐effective carbon budget approach, emissions trading with banking and borrowing can also unburden the regulator from solving the intertemporal social planner optimization problem. Additionally, we discuss which instruments can obtain an optimal allocation even if resource owners employ discount rate mark‐ups (i.e., due to imperfect commitment or insecure property rights). While an emissions trading scheme without banking and borrowing is robust against discount rate mark‐ups, resource taxes have to be modified in order to achieve an optimal allocation.     

面板半参数空间自回归模型的变量选择——基于STIRPAT模型的碳排放影响因素分析

变量选择有助于简化模型,提高估计和预测的精度,但目前鲜有涉及面板半参数空间自回归模型变量选择的研究。本文在ALASSO的基础上提出了SSAR-ALASSO法,该法的核心在于惩罚函数的选择和目标函数的构建。SSAR-ALASSO在变量和参数的对应关系、惩罚函数的选择、特殊参数的取值区间以及适用模型等方面与ALASSO存在差异。模拟结果显示,SSAR-ALASSO法在变量选择的准确性和参数估计的精度两方面均表现良好,随着样本容量的增加表现效果更佳。本文在碳排放量影响因素实证中采用SSAR-ALASSO法对STIRPAT模型进行变量选择。研究结果表明人均财富、技术水平、产业结构、所有制结构和产业集聚显著影响碳排放量,城市化、对外开放、能源价格和环境政策对碳排放量无显著影响。     

碳交易机制下考虑制造商竞争的供应链协调研究

在碳交易机制背景下考虑普通制造商和低碳制造商存在竞争的情形,分别构建集中和分散模式下的供应链决策模型并对减排率以及产品销售价格进行求解。针对分散决策造成的供应链利润损失情况,采用Shapley值法对供应链进行协调,并给出契约协调机制。最后通过算例分析了Shapley值法的有效性,并对碳交易价格和消费者价格敏感性进行灵敏度分析。研究表明:Shapley值法的引入提高了供应链成员以及供应链的总利润,实现了供应链协调;碳交易机制的实施减少了碳排放总量,但较高的碳交易价格会降低供应链利润;消费者价格敏感性的增加使得普通制造商、低碳制造商、零售商以及供应链整体利润均降低。     

Point and interval forecasting for carbon price based on an improved analysis-forecast system

Effective analysis and forecasting of carbon prices, which is an essential endeavor for the carbon trading market, is still considered a difficult task because of the nonlinearity and nonstationarity inherent in carbon prices. Previous studies have failed at the analysis and interval prediction of carbon prices and are limited to point forecasts. Therefore, an improved carbon price analysis and forecasting system that consists of an analysis module and a forecasting module is established in this study; more importantly, the forecasting module includes point forecasting and interval forecasting as well. Aimed at investigating the characteristics of the carbon price series, a chaotic analysis based on the maximum Lyapunov exponent is performed, the determination of appropriate distribution functions based on our newly proposed hybrid optimization algorithm is conducted, and different distribution functions are effectively designed in the analysis module. Furthermore, in the point forecasting model, the phase space reconstruction technique is applied to reconstruct the sequences decomposed by variational mode decomposition due to the chaotic characteristics of the carbon price series, and the reconstructed sequences are considered as the optimal input–output variables of the forecasting model. Then, an adaptive neuro-fuzzy inference system model is trained by the newly proposed hybrid optimization algorithm, which is developed for the first time in the domain of carbon price point forecasting. Moreover, based on the results of point forecasting and the distribution function of the carbon price series determined by the analysis module, the interval forecasting results can be obtained and implemented to provide more reliable information for decision making. Empirical results based on the carbon price data of the European Union Emissions Trading System and Shenzhen of China demonstrate that the proposed system achieves better results than other benchmark models in point forecasting as well as interval forecasting.     

Decay of the energy for the Benjamin–Bona–Mahony equation posed on bounded intervals and on a half‐line

This paper concerns nonlinear dissipative initial boundary value problems for the Benjamin–Bona–Mahony equation posed on a half‐line and on bounded intervals. We prove the existence and uniqueness of global solutions and decay of the energy as time tends to infinity as well as convergence of solutions on bounded intervals to a solution on a half‐line. Copyright © 2012 John Wiley & Sons, Ltd.     

Environmentally friendly facility location with market competition

Many traditional facility location models assume spatial monopoly where market competition is ignored. Since facility locations affect the firm’s market exposure and subsequently its profit, accounting for the impact of the location decisions on customers while anticipating the reaction of competitor firms is essential. In this paper, we introduce a competitive facility location problem where market prices and production costs are determined through the economic equilibrium while explicitly considering competition from other firms. In order to accommodate for the growing efforts on limiting carbon emissions, the presented model includes constraints on the amount of carbon emissions that are due to transportation, while allowing carbon trading. The problem is formulated as a mixed integer non-linear model. Through numerical examples, we illustrate the effect of market competition on the location decisions and discuss the impact of emission limits and carbon trading on customers.     

Zero‐coupon bond prices in the Vasicek and CIR models: Their computation as group‐invariant solutions

We compute prices of zero‐coupon bonds in the Vasicek and Cox–Ingersoll–Ross interest rate models as group‐invariant solutions. Firstly, we determine the symmetries of the valuation partial differential equation that are compatible with the terminal condition and then seek the desired solution among the invariant solutions arising from these symmetries. We also point to other possible studies on these models using the symmetries admitted by the valuation partial differential equations. Copyright © 2007 John Wiley & Sons, Ltd.     

ENERGY‐USE CHOICES AND ALLOWANCE TRADING UNDER THE PERSONAL CARBON TRADING SCHEME

Carbon emissions caused by the household sector have become a major contributor to total emissions. Personal carbon trading (PCT), although untested in practice, could potentially be a powerful tool to induce change in consumer behavior. In this paper, we present an optimization model to determine the energy use choices and allowance trading, and a market equilibrium model to obtain the total supply and demand functions of allowances and then to derive the equilibrium allowance price. It is shown that the level of allocated allowance, energy price, emission rate, and transaction costs could influence the equilibrium allowance price and traded volume. Furthermore, the allowance price is affected negatively and slightly by changes in energy prices, so the total energy price variations will be lessened relatively in the PCT scheme. To further demonstrate these relationships, numerical simulations are conducted. On the basis of the simulation results, the implications of this study are discussed and suggestions for future study are provided.     

Diagnosing and modeling extra‐binomial variation for time‐dependent counts

This article considers the modeling of count data time series with a finite range having extra‐binomial variation. We propose a beta‐binomial autoregressive model using the concept of random coefficient thinning. We discuss the stationarity conditions, derive the moments and autocovariance function and consider approaches for parameter estimation. Furthermore, we develop two new tests for detecting extra‐binomial variation, and we derive the asymptotic distributions of the test statistics under the null hypothesis of a binomial autoregressive model. The size and power performance of the two tests are analyzed under various alternatives taken from a beta‐binomial autoregressive model with Monte Carlo experiments. The article ends with a real‐data example about the Harmonised Index of Consumer Prices of the European Union. Copyright © 2013 John Wiley & Sons, Ltd.     

Moment estimates for weak solutions to the Navier–Stokes equations in half‐space

We establish the moment estimates for a class of global weak solutions to the Navier–Stokes equations in the half‐space. Copyright © 2009 John Wiley & Sons, Ltd.     

碳交易机制下考虑竞争和信息非对称的供应链协调研究

本文在碳交易机制下,考虑制造商之间存在竞争且减排成本系数为低碳制造商私有信息的情况,分别构建信息完全对称和信息非对称情形下的供应链模型,对低碳制造商减排率和产品销售价格进行决策。通过引入由批发价和成本共担组成的联合契约,促使低碳制造商传递真实的减排成本信息,并通过算例对碳交易价格和减排成本系数等进行灵敏度分析。研究发现:引入联合契约能够使得信息非对称下的供应链利润基本达到集中决策水平;碳交易价格的增加会激励制造商积极减排、降低碳排放总量、提高供应链利润;低碳制造商减排成本系数的降低有助于减少碳排放量、增加产品市场需求、实现供应链利润增长。     

Competitive carbon emission yields the possibility of global self-control

Despite many international climate meetings such as Copenhagen 2009, it is still unclear how annual global emissions can be reduced without requiring governments to micro-manage the emitting companies within their individual jurisdictions. Here we examine a simple, yet highly non-trivial, computer model of carbon emission which is consistent with recent activity in the European carbon markets. Our simulation results show that the ongoing daily competition to emit CO₂ within a population of emitters, can lead to a form of collective self-control over the aggregated emissions. We identify regimes in which such a population spontaneously hits its emissions target with minimal fluctuations. We then focus on the emission dynamics induced by a governing body which chooses to actively manage the capping level. Finally we lay some formal stepping stones toward a complete analytic theory for carbon emissions fluctuations within this model framework – in so doing, we also connect this problem to more familiar theoretical terrain within computer science.     

Modeling stock index returns by means of partial least‐squares methods: An out‐of‐sample analysis for three stock markets

We analyze the underlying economic forces of the stock markets in Germany, the U.K. and the U.S. Identifying a number of variables evincing return predictability, we follow a partial least‐squares (PLS) approach to combine these observables into a few latent factors. Conditional on European markets, our findings indicate (i) superior prediction performance of PLS‐based schemes in comparison with both, a random walk and a first‐order autoregressive benchmark model, (ii) consistent profitable trading on the German and British market, (iii) profitable linear forecast combinations, (iv) the U.S. stock market is diagnosed as informationally efficient. Copyright © 2010 John Wiley & Sons, Ltd.     

碳排放交易机制下的供应链运作策略及协调研究

碳排放权交易是控制和减少碳排放的有效工具,同时也使得企业的运作成本增加,供应链的决策管理更复杂。考虑由零售商和制造商组成的二阶段供应链,研究不同碳排放权交易政策(供应链成员内部碳排放权交易、外部市场碳排放权交易)下基于数量折扣契约的供应链协调问题,并与无碳排放约束的情形进行比较。研究结果表明:在不同碳排放权交易政策下,数量折扣契约能够实现供应链的协调;无碳排放约束的供应链最优订货量大于外部碳排放权交易的供应链最优订货量,成员内部碳排放交易下的供应链最优订货量和供应链期望利润随着碳排放配额的增加最终等于无碳排放约束下的供应链最优订货量和供应链期望利润;外部市场碳排放交易政策下供应链的期望利润随着碳排放配额的增加而增加。     

Modeling carbon spot and futures price returns with GARCH and Markov switching GARCH models

This paper makes use of spot and futures market data to carry out a thorough analysis of the dynamics of carbon price returns in the European Union Emission Trading Scheme for the whole first commitment period from 2008 to 2012. Understanding the properties of carbon price returns is especially crucial for industries which have to comply with an emission trading system and other market participants such as risk managers and speculators. We therefore seek to develop accurate models which capture the behavior of carbon price returns comprehensively. We apply a broad spectrum of GARCH model specifications, using different distributions for model innovations. As both time series, spot and futures price returns, exhibit asymmetric behavior in their variance, we additionally take Markov regime switching models for the variance equation into consideration. Empirical results demonstrate that AGARCH, NARCH and GJR fit the data best. We further show that, in the error term of any model, fat-tailed distributions—in particular the generalized error distribution—significantly improve the fit. Additionally, as futures returns seem to carry informational content concerning subsequent spot returns, we propose a sound, yet parsimonious, spot returns model, well-suited to capturing the dynamics. Finally, the most appropriate models for spot and futures price returns are tested in an out-of-sample environment, and further checked for robustness in data subsets. Subsequently a model for each market is proposed.     

GLOBAL CLIMATE CHANGE AND OPTIMAL FOREST MANAGEMENT

A policy question of current interest is how to cope with climate change. One suggestion is to use forests to offset carbon emissions, and therefore, reduce the threat of global warming. This study develops a rigorous model of the relationship between optimal forest harvesting regimes and carbon sequestration. The theoretical analysis integrates the carbon sequestration life cycle into the Faustmann framework and develops optimal cutting rules when carbon sequestration benefits are considered. The carbon life cycle includes both the sequestration of carbon and its ultimate re-release into the atmosphere. A case study of Douglas fir applies the theoretical framework.     

交通运输结构调整对碳排放的影响效应研究

近年来愈发严重的雾霾天气,使得交通运输行业的节能减排问题成为中国能源与环境战略研究领域的重中之重。因此,有必要研究交通运输结构调整对节能减排的影响效应。在考虑别国交通运输行业结构变化对我国交通节能减排示范效应以及数据可得性的基础上,本文利用1990~2011年公路、铁路、水运、民航客货周转量和交通碳排放量的数据,构建以中国、美国、欧盟以及日本为截面个体的面板数据模型。实证结果表明对于结构减排效应最明显的日本和欧盟来说,日本铁路周转量占比每提高1%,本国的单位周转量碳排放减少3.63%,欧盟水运周转量占比每提高1%,单位周转量碳排放将减少22.11%。而我国对应的这一数字分别为0.38%和0.06%,表明我国交通运输行业在结构性碳减排方面潜力很大。最后基于交通运输行业供给侧改革的背景,结合本文研究结果从客运和货运两方面提出了相关政策建议。     

Asymptotic dynamics of the one‐dimensional attraction–repulsion Keller–Segel model

The asymptotic behavior of the attraction–repulsion Keller–Segel model in one dimension is studied in this paper. The global existence of classical solutions and nonconstant stationary solutions of the attraction–repulsion Keller–Segel model in one dimension were previously established by Liu and Wang (2012), which, however, only provided a time‐dependent bound for solutions. In this paper, we improve the results of Liu and Wang (2012) by deriving a uniform‐in‐time bound for solutions and furthermore prove that the model possesses a global attractor. For a special case where the attractive and repulsive chemical signals have the same degradation rate, we show that the solution converges to a stationary solution algebraically as time tends to infinity if the attraction dominates. Copyright © 2014 John Wiley & Sons, Ltd.     

Option pricing when asset returns jump interruptedly

This paper proposes an extension of Merton's jump‐diffusion model to reflect the time inhomogeneity caused by changes of market states. The benefit is that it simultaneously captures two salient features in asset returns: heavy tailness and volatility clustering. On the basis of an empirical analysis where jumps are found to happen much more frequently in risky periods than in normal periods, we assume that the Poisson process for driving jumps is governed by a two‐state on‐off Markov chain. This makes jumps happen interruptedly and helps to generate different dynamics under these two states. We provide a full analysis for the proposed model and derive the recursive formulas for the conditional state probabilities of the underlying Markov chain. These analytical results lead to an algorithm that can be implemented to determine the prices of European options under normal and risky states. Numerical examples are given to demonstrate how time inhomogeneity influences return distributions, option prices, and volatility smiles. The contrasting patterns seen in different states indicate the insufficiency of using time‐homogeneous models and justify the use of the proposed model. Copyright © 2012 John Wiley & Sons, Ltd.     

Hierarchical Bayesian autoregressive models for large space–time data with applications to ozone concentration modelling

Increasingly large volumes of space–time data are collected everywhere by mobile computing applications, and in many of these cases, temporal data are obtained by registering events, for example, telecommunication or Web traffic data. Having both the spatial and temporal dimensions adds substantial complexity to data analysis and inference tasks. The computational complexity increases rapidly for fitting Bayesian hierarchical models, as such a task involves repeated inversion of large matrices. The primary focus of this paper is on developing space–time autoregressive models under the hierarchical Bayesian setup. To handle large data sets, a recently developed Gaussian predictive process approximation method is extended to include autoregressive terms of latent space–time processes. Specifically, a space–time autoregressive process, supported on a set of a smaller number of knot locations, is spatially interpolated to approximate the original space–time process. The resulting model is specified within a hierarchical Bayesian framework, and Markov chain Monte Carlo techniques are used to make inference. The proposed model is applied for analysing the daily maximum 8‐h average ground level ozone concentration data from 1997 to 2006 from a large study region in the Eastern United States. The developed methods allow accurate spatial prediction of a temporally aggregated ozone summary, known as the primary ozone standard, along with its uncertainty, at any unmonitored location during the study period. Trends in spatial patterns of many features of the posterior predictive distribution of the primary standard, such as the probability of noncompliance with respect to the standard, are obtained and illustrated. Copyright © 2012 John Wiley & Sons, Ltd.