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Abstract

Using a dynamic national computable general equilibrium model, we investigate the impact of carbon tax and energy efficiency improvement on the economy and environment of China. The Chinese social account matrix is presented based upon the latest input-output table (2012 IO table) and other data. The business as usual (BAU) scenario is designed according to several forecasts about China by 2030, followed by six policy scenarios, including different levels of carbon tax and technological progress as well as their combinations. The results show that carbon tax will frustrate the overall economic growth slightly. The CO2 emission will be 13.81% lower in 2030 compared to BAU case if the carbon tax scheme is carried out at at rate of 200 RMB/ton of CO2. Technological progress will stimulate the economic growth, enrich the household and government income, increase total investment and make most sectors prosperous with the exception of energy industries.

 

 

Abstract

The effectiveness of building energy codes is studied taking a long-run perspective. We focus on regulation´s impact on energy demand in high-end and low quality residences, i.e. the diffusion and the entry of "green" buildings in the housing market. We develop a measure for regulation intensity and apply this to a panel-error-correction regression model for energy requirements of a large sample of German apartment houses built between 1950-2005. We show that regulation is effective in saving energy. In particular, regulation pushes investors in the low quality housing market segment towards the technological frontier. Indirectly, it also affects the high-end housing market segment.

Abstract

In this paper, we reassess the impact of inequality on growth, The majority of previous papers have employed (system) GMM estimation. However, recent simulation studies indicate that the problems of GMM when using non-stationary data such as GDP have been grossly underestimated in applied research. Concerning predetermined regressors such as inequality, GMM is outperformed by a simple least square dummy variable estimator. Additionally, new data have recently become available that not only double the sample size compared to most previous studies but that also address the substantial measurement issues that have plagued past research. Using these new data and an LSDV estimator, we provide an analysis that both accounts for the conditions where inequality is beneficial or detrimental to growth and distinguishes between market-driven inequality and redistribution. We show that there are situations where market inequality affects growth positively while redistribution is simultaneously beneficial.

Abstract

In this article, we investigate two types of asymmetries, that is, the asymmetry of conditional volatility and the asymmetry of tail dependence in the crude oil markets. We employ the two different sample datasets in which each dataset covers the time period of stable and unstable oil prices, individually. A variety of different copulas and three asymmetric GARCH regression models are used in odrder to capture the two types of asymmetries. In particular, we extend the TBL-GARCH model proposed by Choi et. al. (2012) to the asymmetric GARCH regression type model. The findings from the two different apporaches are congruent, in that there is no asymmetry of tail dependence and no asymmetric conditional volatility in crude oil returns over the two different sample periods. Our study reconfirms the findings of Aboura and Wagner (2016) by showing that asymmetric conditional volatility relates to asymmetric tail dependence.

Abstract

The Gini coefficient is widely used in academia to discuss how income inequality affects development and growth. However, extremely different Lorenz curves may provide different development and growth outcomes while still leading to the same Gini coefficient. This paper studies the development effects of “mean division shares,” i.e., the share of income held by people whose household disposable income per capita is below the mean income (mean income share) and the share of the population with this income (mean population share) using panel data. Our analysis explores how this income share and population share impact development and growth. It shows that the income and population shares affect growth in significantly different ways and that an analysis of these metrics provides substantial value compared to that of the Gini coefficient.