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Empirical Economics

Erschienen in:

01.12.2013

A smooth coefficient model of carbon emissions

verfasst von: Michael S. Delgado

Erschienen in: Empirical Economics | Ausgabe 3/2013

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Abstract

I use a semiparametric smooth coefficient model to estimate a generalization of the emissions convergence models derived from the green Solow model proposed by Brock and Taylor (J Econ Growth 15:127–153, 2010). Parametric estimates of simple homogeneous coefficient convergence models suggest that there may be heterogeneity in emissions convergence across different subsamples of observations. The semiparametric models confirm that there is heterogeneity across countries in coefficient estimates; however, such heterogeneity does not appear to be substantial enough to qualitatively influence the estimates derived from the parametric models. Hence, I find that (i) the green Solow model is a robust framework for analyzing carbon emissions convergence and (ii) carbon emissions are converging across a large sample of countries. My results suggest that international agreements that assign pollution rights based on population levels may be agreeable to many nations.

Vorheriger Artikel Futures price dynamics of CO2 emission allowances

Nächster Artikel The value of a statistical life in Chile

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Aldy (2006), for example, notes the high correlation between carbon emissions and income.

Perhaps a normative climate change perspective would be to encourage low emissions nations to maintain a low level of emissions, while high emissions nations reduce their emissions levels, i.e., convergence to a low level of emissions. In general, evidence of convergence does not necessarily imply that the steady state level of emissions to which the countries are converging is relatively low or high.

There exist several other papers focusing on convergence in carbon emissions, albeit not in the absolute or conditional beta-convergence sense. Westerlund and Basher (2008) and Panopoulou and Pantelidis (2009), for example find evidence of per capita carbon emissions convergence using panel unit root tests and a nonlinear time-varying factor model, respectively. McKibbin and Stegman (2005), Stegman (2005), Aldy (2006), and Barassi et al. (2008), for example, do not find evidence of carbon convergence.

The conditional variables in the conditional convergence equation of Strazicich and List (2003) include the natural logarithm of per capita GDP and its square, the average price of gasoline, the population density measured in 1978, and the average winter temperature.

Durlauf et al. (2001) make a similar generalization of a standard Solow specification in the GDP growth context.

It is important to recognize two limitations of the green Solow model. The first is that parameters in a reduced form specification such as the convergence equations discussed below are not policy invariant and should be interpreted with such an insight in mind (Lucas 1976). The second is the debate about whether the estimated convergence parameter in beta-convergence regressions can be informative about the dynamics of the distribution of emissions; see Friedman (1992), Quah (1993), and Bliss (1999) for important discussions. See, also, Van (2005) for an important empirical assessment of the dynamics of the world distribution of carbon emissions.

It is important to note that Brock and Taylor (2010) argue that the $\log [1-\theta _i]$ term in the conditional convergence equation is zero for per capita carbon emissions measured prior to 1998.

In later sections, I define $Z$ to be a matrix that includes both country and time indicators. In these models, I use the ordered discrete kernel function given by

$$\begin{aligned} K(z,z_i) = \left\{ \begin{array}{ll} 1&\quad \text{ if}\quad z = z_i\\ h^{|z-z_i|}&\quad \text{ if}\quad z \ne z_i \end{array} \right. \end{aligned}$$

(12)

to smooth the time indicator, and note that $K(z,z_i)$ becomes a product kernel function defined as the product of kernel weights for both the country and time indicators.

The semiparametric smooth coefficient model has previously been applied by Durlauf et al. (2001), Mamuneas et al. (2006), and Hartarska et al. (2011) to name a few.

In a cross-sectional regression, each country has only one observation, so a discussion of a bandwidth equal to zero implies regression using only one datapoint and hence does not make much sense. However, it is important to note that $h \rightarrow 0$ as $n \rightarrow \infty $ and $nh \rightarrow \infty $ is a requirement for consistency in semiparametric and nonparametric kernel estimation.

Note that the sample size is larger for the cross-sectional regressions in the first three columns of Tables 4 and 5 than for the primary cross-sectional sample. Regressions run using the original 158 countries yield qualitatively identical results to those in Tables 4 and 5.

The bandwidths for the year indicator are 0.6328 and 0.7872 for the absolute and conditional convergence models, respectively.

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Barassi MR, Cole MA, Elliott RJR (2008) Stochastic divergence or convergence of per capita carbon dioxide emissions: re-examining the evidence. Environ Res Econ 40:121–137CrossRef

Bliss C (1999) Galton’s fallacy and economic convergence. Oxf Econ Pap 51:4–14CrossRef

Brock WA, Taylor MS (2010) The green Solow model. J Econ Growth 15:127–153CrossRef

Durlauf SN, Kourtellos A, Minkin A (2001) The local Solow growth model. Eur Econ Rev 45:928–940CrossRef

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Zhang R, Sun K, Delgado MS, Kumbhakar SC (2012) Productivity in China’s high technology industry: regional heterogeneity and R&D. Technol Forecast Soc Change 79:127–141CrossRef

Titel: A smooth coefficient model of carbon emissions
verfasst von: Michael S. Delgado
Publikationsdatum: 01.12.2013
Verlag: Springer Berlin Heidelberg
Erschienen in: Empirical Economics / Ausgabe 3/2013
Print ISSN: 0377-7332
Elektronische ISSN: 1435-8921
DOI: https://doi.org/10.1007/s00181-012-0658-1

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