Fractional Integration Versus Structural Change: Testing the Convergence of $$\hbox {CO}_{2}$$ Emissions

This paper assesses the stochastic convergence of relative \(\hbox {CO}_{2}\) emissions within 28 OECD countries over the period 1950–2013. Using the local Whittle estimator and some of its variants we assess whether relative per capita \(\hbox {CO}_{2}\) emissions are long memory processes which, although highly persistent, may revert to their mean/trend in the long run thereby indicating evidence of stochastic convergence. Furthermore, we test whether (possibly) slow convergence or the complete lack of it may be the result of structural changes to the deterministics of each of the relative per-capita emissions series by means of the tests of Berkes et al. (Ann Stat 1140–1165, 2006) and Mayoral (Oxford Bull Econ Stat 74(2):278–305, 2012). Our results show relatively weak support for stochastic convergence of \(\hbox {CO}_2\) emissions, indicating that only between 30 and 40% of the countries converge to the OECD average in a stochastic sense. This weak evidence disappears if we enlarge the sample to include 4 out of the 5 BRICS, indicating that our results are not robust to the inclusion of countries which are experiencing rates of growth which are far larger than those of the OECD members. Our results also decisively indicate that a slow or lack of convergence is not the results of a structural break in the relative \(\hbox {CO}_{2}\) emissions series.     

Fiscal Policy and $$\hbox {CO}_{2}$$ Emissions of New Passenger Cars in the EU

To what extent have national fiscal policies contributed to the decarbonisation of newly sold passenger cars? We construct a simple model that generates predictions regarding the effect of fiscal policies on average \(\hbox {CO}_{2}\) emissions of new cars, and then test the model empirically. Our empirical strategy combines a diverse series of data. First, we use a large database of vehicle-specific taxes in 15 EU countries over 2001–2010 to construct a measure for the vehicle registration and annual road tax levels, and separately, for the \(\hbox {CO}_{2}\) sensitivity of these taxes. We find that for many countries the fiscal policies have become more sensitive to \(\hbox {CO}_{2}\) emissions of new cars. We then use these constructed measures to estimate the effect of fiscal policies on the \(\hbox {CO}_{2}\) emissions of the new car fleet. The increased \(\hbox {CO}_{2}\)-sensitivity of registration taxes have reduced the \(\hbox {CO}_{2}\) emission intensity of the average new car by 1.3 %, partly through an induced increase of the share of diesel-fuelled cars by 6.5 percentage points. Higher fuel taxes lead to the purchase of more fuel efficient cars, but higher diesel fuel taxes also decrease the share of (more fuel efficient) diesel cars; higher annual road taxes have no or an adverse effect.     

Aspects of Governance and $$\hbox {CO}_2$$Emissions: A Non-linear Panel Data Analysis

The reduction of \(\hbox {CO}_2\)emissions has been at the centre of worldwide debates on environmental issues, though its inclusion as one of the millennium development goals (MDGs) by the United Nations has changed the focus of relative literature. Among many, one of the World Bank’s “recipes” to achieve a higher position toward MDGs has been to undertake reforms for a better governance. While, the majority of researches’ focus has been on one single aspect and its relationship with the environment, some studies, have simultaneously considered two governance dimensions. In this paper, we focus on the role of several aspects of governance on \(\hbox {CO}_2\)emissions. This provides us with a chance to explore the possible impacts of all aspects of governance on a more direct measure of emissions, that is different to previous researches which have focused on the indirect transmission and considered the maximum of two. Using the IV method within panel data analysis, we show that only one single aspect of governance, Control of Corruption, has a negative significant effect on \(\hbox {CO}_2\)emissions and its effect has a non-linear relationship. The non-linearity exists in both parametric and nonparametric analysis after controlling for endogeneity.     

Shadow prices of $$\hbox {CO}_{2}$$ emissions at US electric utilities: a random-coefficient,random-directional-vector directional output distance function approach

We estimate the shadow prices of \(\hbox {CO}_{2}\) emissions of electric utilities in the US over the period from 2001 to 2014, using a random-coefficient, random-directional-vector directional output distance function (DODF) model. The main feature of this model is that both its coefficients and directional vector are allowed to vary across firms, thus allowing different firms to have different production technologies and to follow different growth paths. Our Bayes factor analysis indicates that this model is strongly favored over the commonly used fixed-coefficient DODF model. Our results obtained from this model suggest that the average annual shadow price of \(\hbox {CO}_{2}\) emissions ranges from $61.62 to $105.72 (in 2001 dollars) with an average of $83.12. The results also suggest that the firm-specific average shadow price differs significantly across electric utilities. In addition, our estimates of the shadow price of \(\hbox {CO}_{2}\) emissions show an upward trend for both the sample electric utilities as a whole and the majority of the individual sample electric utilities.     

A trend filtering method closely related to $$\ell _{1}$$ trend filtering

The filtering method developed by Kim et al. (SIAM Rev 51:339–360, 2009), \(\ell _{1}\) trend filtering, is attractive because it enables us to estimate a continuous piecewise linear trend. This paper introduces a new filtering method closely related to \(\ell _{1}\) trend filtering in order to contribute to the accumulation of knowledge on \(\ell _{1}\) trend filtering. We show that the piecewise linearity, which is the key feature of \(\ell _{1}\) trend filtering, is derived from the new filtering. For this reason, we refer to the filtering as ‘pure’ \(\ell _{1}\) trend filtering. We also demonstrate some other miscellaneous results concerning the new filtering.     

Work Hours and CO2 Emissions: Evidence from U.S. Households

The degrowth movement proposes worktime reduction policies to help high-income countries meet their climate goals while supporting full employment. However, the work hours elasticity of carbon emissions remains uncertain despite a growing number of empirical analyses. This paper estimates the impact of work hours on emissions using household data from the United States. We calculate the carbon intensity of goods using input-output tables from the Bureau of Economic Analysis, which we combine with spending data from the Bureau of Labor Statistics to estimate carbon footprints for a representative sample of U.S. households. There is strong evidence that households with longer work hours emit more CO₂, but our household-level estimate of the work hours elasticity of carbon emissions is lower than most country-level estimates. Our results suggest that differences in work hours account for a small fraction of differences in per capita carbon footprints across high-income countries.

Highlights

• Households with longer work hours have significantly larger carbon footprints.

• Our estimated household-level work hours elasticity is smaller than most country-level estimates.

• Work hour reduction policies likely generate modest reductions in carbon emissions.

     

Carbon Dioxide Emissions in the U.S. Economy: A Structural Decomposition Analysis

This paper provides an empirical analysis of the impact of various influences on carbon dioxide emissions. It incorporates methodological refinements of input-output structural decomposition analysis, which is the examination of economic change by means of a set of comparative static variations in key parameters of I-O tables. The analysis is performed using a two-tiered KLEM model, which allows for estimation of substitution and technological change effects within and between input aggregates. The model is used to decompose the sources of change in CO₂ emissions in the U.S. over the 1972–82 timeframe using hybrid energy/value tables for the initial and terminal years. Results show the significant effect of substitution within the energy sector and between energy and other inputs as the leading causes of the decline in carbon dioxide emissions.     

Imputed Rent and Income Inequality: A Decomposition Analysis for Great Britain, West Germany and the U.S.

This article deals with income advantages derived from owner-occupied housing and their impact on the personal income distribution. Using micro-data from the British Household Panel Study (BHPS), the German Socio-Economic Panel (SOEP), and the U.S. Panel Study of Income Dynamics (PSID) we find distinct cross-national differences in terms of the prevalence and extent of imputed rent. Results from inequality decomposition analyses show this overall impact to be the net effect of two conflicting changes: On the one hand there is increasing income inequality between the groups of owneroccupiers and renters, respectively, and, on the other hand, we find inequality to be decreasing within the group of those owner-occupiers who own outright. When focussing on imputed rent as a means of old-age provision, our results for all three countries show an income advantage for, as well as a poverty reducing effect among the elderly. The empirical findings support the claim for the need of an improved harmonization of this non-cash income component especially for the purpose of cross-national comparative research.     

美中贸易收支与人民币汇率关系:实证分析

针对美中贸易收支与人民币汇率问题,经研究发现,汇率变动对贸易收支的影响是值得怀疑的,仅仅依靠人民币汇率变动是无法解决美中贸易逆差问题的。1994—2002年年度数据实证显示,美中贸易收支与人民币汇率(名义或实际汇率)之间没有稳定的关系;1998—2003年月度数据计量表明,美中贸易收支与人民币汇率之间没有长期稳定的协整关系。并且,依据美中两国月度统计数据计量的结论是相一致的。因此,“人民币升值论”或许更多的是基于政治而非经济因素,人民币汇率浮动并不能解决美中贸易逆差问题。     

中国对外直接投资的母国碳排放效应研究——基于2003～2011年省级面板数据

选用2003~2011年我国30个省的面板数据,基于联立方程组模型,考察了中国对外直接投资(outward foreign direct investment,OFDI)对本国CO2排放量的影响。研究表明:我国对外直接投资的增加将会给本国带来正的规模效应、产业结构效应以及负的技术效应。综合三大效应的结果显示,我国加大对外直接投资,增加了国内的CO2排放量。数据表明,我国的对外直接投资每增加1%,国内CO2排放量将会增加0.5009%,其主要原因在于具有减排作用的技术效应远低于其余两大效应,据此提出了增加技术寻求型直接投资和调整产业结构的政策建议。     

银行资产证券化的直接动因——来自美国银行控股公司的经验证据

资产证券化已经成为现代银行的一种重要融资手段。尽管有许多文献对银行资产证券化的动因在理论方面做出了分析和解释,但是其经验证据却相对缺乏。本文采用能够处理面板数据的计量经济方法对资产规模大于10亿美元的美国银行控股公司的资产证券化数据进行了分析。经验结果显示,银行控股公司进行资产证券化主要是出于增加流动性和降低融资成本的目的。我们的结果对于推进我国银行资产证券化有一定的参考价值。     

基于指数分解的居民生活用能碳排放影响因素研究

运用对数均值迪氏指数(LMDI)因素分解法,构建了居民生活用能碳排放影响因素模型,并运用该模型对2000~2012年中国居民生活用能碳排放影响因素进行了实证研究。研究发现,造成我国居民生活用能碳排放快速增长的最主要原因是人均消费水平的提高,其次是居民能源消费支出占总生活消费支出比重的增加。能源消费结构的改变促进了居民生活用能碳排放量的减少,是抑制我国居民生活用能碳排放量减少的主要力量。能源价格的不断上升,也在一定程度上促进了居民生活用能消费及碳排放量的降低。     

生产性服务业、制造业与经济增长的动态关系——基于中美两国数据的实证分析

选取1978—2013年中国的年度数据、1960—2015年美国的年度数据,运用向量自回归模型对中美两国的生产性服务业、制造业与经济增长的动态关系进行模拟和分析。研究结果表明:美国生产性服务业的发展对制造业发展和经济增长的短期效应比中国要强,且作用时间更快;中国的经济增长对生产性服务业发展的短期效应强于美国,且减弱速度更为缓慢;生产性服务业和制造业自身的发展是影响制造业发展的最主要因素;生产性服务业的发展是影响其自身发展和经济增长的最主要因素。     

中美工业制成品贸易调整成本的实证研究——基于边际产业内贸易视角

从边际产业内贸易角度,分别运用A指数和S指数对1992-2008年间中美19个行业的工业制成品贸易调整成本进行研究,并进行Daniels检验.研究结果表明,中美工业制成品绝大多数处于产业间贸易,总体调整成本较高;高技术产业的贸易调整成本相对较低;中等技术产业的调整成本呈逐渐减少趋势;低技术产业的调整成本一直处于相对较高的状态.这些结论为国家制定相关经济政策提供了决策参考.     

工业能源消费碳排放影响因素研究——基于STIRPAT模型的上海分行业动态面板数据实证分析

文章估算了1994-2008年上海市工业分行业能源终端消费的二氧化碳(CO2)排放量,对其演变趋势进行了总结分析,并基于改进的STIRPAT模型和广义矩估计方法分别对碳排放规模和强度的影响因素进行了实证分析。结果显示:黑色金属行业作为第一排放大户,其排放走势对工业部门整体排放趋势具有关键性的影响;碳排放规模和强度与劳均产出之间分别呈现出N型和倒N型曲线关系;煤炭消费比重对碳排放规模和强度均具有显著的促进作用,研发强度和能源效率对其均表现出显著的限制作用,而投资规模对碳排放规模和强度分别具有显著的促进和抑制作用;碳排放的变化具有明显的滞后效应,劳均产出和能源效率是对碳排放产生长期影响最强的两个因素。     

Predicting U.S. jet fighter aircraft introductions from 1944 to 1982: A dogfight between regression and TFDEA

Since its inception in 2001, technology forecasting using data envelopment analysis (TFDEA) has been used with a number of applications. This paper presents a formal comparison of TFDEA with a previously published application from Technological Forecasting and Social Change by Joseph Martino. Using the data and multiple-regression model from Martino, we compare results obtained from TFDEA to those previously published. Both techniques predict the first flights of fighter jets introduced between 1960 and 1982 by using first-flight data of aircraft introduced between 1944 and 1960. TFDEA was found to better predict the first-flight dates than the multiple-regression forecast. These results indicate that TFDEA may be a powerful new technique for predicting complex technological trends and time to market for new products.