economics

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Energy Policy. Vol. 24, No. 1, pp. 31-37. 1996 Copyright © 1996 Published by Elsevier Science Ltd

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Population growth and global CO 2 emissions

A secular perspective

Tom Knapp Department o f Economics, Penn State University, Lehman, PA 18627, USA

Rajen Mookerjee Department o f Economics, Penn State University, Monaca, PA 15061, USA

C o n s i d e r a b l e scientific effort h a s b e e n applied to t h e q u e s t i o n o f w h e t h e r w o r l d w i d e fossil fuel c o m - b u s t i o n a n d t h e r e s u l t a n t e m i s s i o n o f C O 2 (as well as e m i s s i o n s o f o t h e r g r e e n h o u s e gases) will c a u s e a d i s c e r n i b l e e n h a n c e m e n t o f the g r e e n h o u s e effect in t h e next century. A m o r e precise u n d e r s t a n d - ing o f t h e c o n t r i b u t i o n o f h u m a n activity to potential global w a r m i n g (vis-h-vis n a t u r a l c l i m a t i c vari- ability) is o f critical p o l i c y interest. S u r p r i s i n g l y little research has b e e n d e v o t e d to e s t a b l i s h i n g t h e u n d e r l y i n g statistical r e l a t i o n s h i p b e t w e e n h u m a n activities and C O 2 e m i s s i o n s , i n this paper, w e ex- p l o r e t h e n a t u r e o f the r e l a t i o n s h i p b e t w e e n global p o p u l a t i o n g r o w t h a n d C O 2 e m i s s i o n s by e m - p l o y i n g t h e test o f c a u s a l i t y d e v e l o p e d by G r a n g e r o n a n n u a l data for 1 8 8 0 - 1 9 8 9 , as well a s m o r e c o m p r e h e n s i v e e r r o r c o r r e c t i o n a n d c o i n t e g r a t i o n m o d e l s . T h e results s u g g e s t a l a c k o f a l o n g - t e r m e q u i l i b r i u m r e l a t i o n s h i p , b u t i m p l y a s h o r t - t e r m d y n a m i c r e l a t i o n s h i p f r o m C O 2 to p o p u l a t i o n g r o w t h . Keywords: Cointegration; Carbon emissions; Global warming

M u c h scientific effort has b e e n applied to the question o f whether increases in the atmospheric concentrations o f C O 2 a n d o t h e r g r e e n h o u s e g a s e s due t o h u m a n a c t i v i t y will cause discernible global w a r m i n g - an acceleration o f the g r e e n h o u s e effect in the next century. However, there re- mains uncertainty as to the extent o f w a r m i n g which might take place. Further k n o w l e d g e about the linkage b e t w e e n h u m a n activity and global w a r m i n g is certainly o f interest to p o l i c y makers. I

A s s e r t i o n s b y s o m e scientists that a c c u m u l a t i o n o f g r e e n h o u s e gases will cause (or is already causing) a mean- i n g f u l i n c r e a s e in g l o b a l m e a n t e m p e r a t u r e s has led to widespread interest in measures to curtail h u m a n C O 2 emis- sions relating to fossil fuel use (as well as the curtailment o f

I F o r a t h o r o u g h n o n - t e c h n i c a l d i s c u s s i o n o f t h e g r e e n h o u s e e f f e c t , s e e C l i n e ( 1991 ), H e l m a n d S c h n e i d e r ( 1 9 9 0 ) .

emissions o f other greenhouse gases), so as to avoid poten- tially disruptive climatic changes. 2 However, there remains a lack o f scientific consensus as to m a n y aspects o f the po- tential for global warming. Moreover, c o n s i d e r a b l e uncer- tainty remains as to the s o c i o e c o n o m i c impact o f potential climate changes. 3

Regarding scientific uncertainty, m o s t a t m o s p h e r i c sci- entists agree in principle that the c o n t i n u e d a c c u m u l a t i o n o f h u m a n - i n d u c e d c a r b o n e m i s s i o n s will u l t i m a t e l y c a u s e global w a r m i n g . However, c o n s i d e r a b l e d i s a g r e e m e n t re-

2The 1990 report of the lntergovernmental Panel on Climate Change (IPCC), formed by the United Nations Environmental Programme (UNEP) and the World Meteorological Office described the potential for global warming, which in part motivated the Climate Change Convention, signed by a number of nations at the Earth Summit at Rio de Janeiro in 1992, call- ing for restrictions on CO 2 emissions. 3For a full treatment of the economic uncertainties, see Schelling (1992).

31

32 Population growth and global C O 2 emissions: T Knapp and R Mookerjee

m a i n s as to the t i m i n g and m a g n i t u d e o f such events, in- cluding w h e t h e r a discernible w a r m i n g has already begun. 4 This remaining scientific uncertainty has contributed to the difficulty experienced b y policy makers in developing pol- icy responses, i f any. 5 For example, the U S A has taken a cautious position r e g a r d i n g c o m m i t t i n g to the curtailment o f C O 2 emissions.

It is unlikely that the scientific uncertainty will be elimi- nated in the near future. T h e principal tool utilized in cli- matic r e s e a r c h is large-scale structural m o d e l s , k n o w n as g e n e r a l c i r c u l a t i o n m o d e l s ( G C M s ) w h i c h utilize super- c o m p u t e r s to p e r f o r m simulations o f global weather. G C M s are massive, difficult to construct, c o s t l y to run and the cor- rect specification o f the m o d e l s is still subject to consider- able debate. S u b s t a n t i a l e f f o r t s are u n d e r w a y t o w a r d m a k i n g the m o d e l s m o r e realistic and m o r e accurate. For example, recent efforts toward improving the specification o f the m o d e l s focus upon issues such as natural variability within the climate system, the role o f c l o u d s and oceans, and the effect o f pollutants such as sulphur dioxide. 6 The incomplete scientific evidence creates a policy dilemma: if h u m a n - i n d u c e d g r e e n h o u s e gas emissions are indeed suffi- c i e n t to c a u s e g l o b a l w a r m i n g , t i m e m a y be sufficiently short for steps to be warranted to reduce fossil fuel usage even t h o u g h the scientific evidence is incomplete. 7 The cost o f policies directed at reducing emissions are substantial, so i f global w a r m i n g does not occur, the waste o f effort would be substantial.

Given that the scientific precision o f G C M s is lagging b e h i n d the perceived n e e d to make policy decisions regard- ing fossil fuel emissions policy, it is not surprising that re- s e a r c h e r s have b e g u n to utilize s i m p l e t i m e series t e c h - niques in order to provide s o m e insight into the underlying

4We might recall the testimony of climatologist James Hansen before Congress in the hot summer of 1988 indicating he believed that global warming had already begun. A sense of the scepticism among atmospheric scientists on the issue is provided by Lindzen (1990). 5An update from the IPCC is due in November of 1995. Preliminary re- leases from the IPCC has reaffirmed the position that global CO 2 emis- sions must be stabilized or even curtailed below 1990 levels in order to avoid 'dangerous' climate change. Considerable debate is ongoing as to the validity of this position and the procedures used by IPCC to produce its conclusions. For coverage of these issues, see, for example, Dickson (1994). 6Each of these factors is of considerable importance for improving the re- alism of GCMs, and the recent literature is substantial. Here are but a few papers of note for the interested reader. Maskell et al (1993) provides a brief overview of recent aspects of GCM development. A recent example of the modelling of natural variability within the climate system is in- cluded in Stouffer et al (1994). A representative example of research which compares the results of alternative GCMs is shown by Cess et al (1993). Ocean behaviour is the focus of work by Kumar et al (1994) and Covey ( 1991 ). 7Clearly the concern over timely results has had an impact upon the re- search agenda. In developing a revision of greenhouse warming to 2100, Schlesinger and Jiang (1991) explores the impact of delaying plans to cur- tail greenhouse gas emissions by ten years and finds the impact upon warming to be small. However, he concludes: 'To us this small penalty does not indicate that we should "wait and see" and do nothing during this decade - quite the contrary. The study of the greenhouse effect, both the- oretically and observationally, should be accelerated into a "crash pro- gramme" so that we do not squander the time that nature has given us to obtain realistic understanding of the climate response to increasing con- centrations of greenhouse gasses' (p 221).

relationship between global temperatures and other poten- tially relevant variables. T h e story is a n a l o g o u s to the de- bate in e c o n o m i c s b e t w e e n builders o f structural m o d e l s and a d v o c a t e s o f s i m p l e r t i m e series m e t h o d s u c h as A R I M A and VAR. I f the simpler ' b l a c k b o x ' m o d e l s pro- duce superior forecasts, the underlying assumptions o f the structural m o d e l s are called into question.

To date, economists have yet to contribute to this statisti- cal aspect o f the global w a r m i n g debate. Instead, they have l a r g e l y f o c u s e d u p o n the i m p a c t o f h y p o t h e t i c a l c l i m a t e changes upon e c o n o m i c activity, o r on estimating the eco- nomic impact o f policies which might be adopted to reduce greenhouse gas emissions, such as c a r b o n taxes. A n u m b e r have b e e n p u b l i s h e d in this j o u r n a l ; o t h e r s i n c l u d e Schelling (1992), N o r d h a u s (1991) and Cline (1992).

Atmospheric scientists have been exploring these issues either using very simple time series techniques familiar to e c o n o m i s t s , o r f r e q u e n c y d o m a i n t e c h n i q u e s w h i c h are m o r e f a m i l i a r to t h o s e with a b a c k g r o u n d in p h y s i c s . N e w e l l a n d M a r k u s ( 1 9 8 7 ) a n a l y s e the r e l a t i o n s h i p b e - t w e e n M a u n a L o a C O 2 m e a s u r e m e n t s and w o r l d p o p u l a - tion, and conclude there is a significant fit, using m e r e l y a simple correlation coefficient. A particularly fruitful area o f research is the correlation between measures o f solar activ- ity a n d g l o b a l t e m p e r a t u r e s . 8 R e i d ( 1 9 8 7 ) uses a s i m p l e e q u a t i o n to fit m e a s u r e s o f v a r i a t i o n in s o l a r a c t i v i t y to measures o f globally averaged sea surface temperatures and finds a close fit. He uses graphical analysis to p o r t r a y the close fit between sunspot n u m b e r s and sea surface tempera- tures, and suggests a causal linkage. Friis-Christensen and L a s s e n ( 1 9 9 1 ) a n a l y s e s u n s p o t s a n d g l o b a l t e m p e r a t u r e a n o m a l i e s u s i n g spectral a n a l y s i s , u s i n g d a t a f o r 1860 through 1990, and c o n c l u d e that the fit is strong e n o u g h to cast doubt upon the prevailing scientific consensus c l a i m i n g to link temperature c h a n g e s and g r e e n h o u s e gas emissions. Hansen and L e b e d e f f (1988) graphically portray e v i d e n c e that ( v e r i f y i n g m o r e s o p h i s t i c a t e d a n a l y s i s b y o t h e r re- s e a r c h e r s ) v o l c a n i c a c t i v i t y a n d El N i n o ( P a c i f i c s t o r m ) events significantly impact the global temperature record.

In the m o s t comprehensive study b y atmospheric scien- tists to date, Kuo e t a l (1990) use spectral analysis to s h o w that changes in the m o n t h l y M a u n a L o a C O 2 data lag those in the m o n t h l y global m e a n temperature series. K u o e t a l c o n c l u d e that m o r e c o m p l e t e (ie multivariate analysis) is warranted b e f o r e c o n c l u s i o n s can be drawn r e g a r d i n g the causal relationship between atmospheric C O 2 accumulation and global temperatures. The authors suggest e x p a n d i n g the m o d e l t o i n c l u d e s u n s p o t s , El N i n o effects, h u m a n C O 2 emissions and other variables (see for e x a m p l e Schonwiese, 1991). In a response to the Kuo et a l paper, Marston e t a l (1991) also use spectral analysis on C O 2 and temperature data and argue that the above result indicates that there m a y be a cumulative feedback process, and that global w a r m i n g could build upon itself, once initiated. T h e policy implica-

SEconomists may recall efforts to link sunspots to economic activity dat- ing to Jevons (see Sparks, 1974).

Population growth and global CO 2 emissions: T Knapp and R Mookerjee 33

tion suggested by Marston is that if this process is already started, then policy aimed at reducing greenhouse gas emis- sions may prove fruitless.

Clearly, the multivariate aspects o f the modelling frame- work suggest that more research is needed on these issues before reasonable inferences about causality can be made. Because these methods are not meant to supplant the more sophisticated GCM models, caution is certainly in order when interpreting the results from these very simple mod- els. However, the results from this type o f purely statistical modelling have certainly had an impact upon the debate over global warming. For example, the 1989 Marshall Insti- tute Report, Scientific Perspectives on the Greenhouse Problem cites the sunspot-global temperature link in argu- ing that our understanding o f the global temperature-green- house gas relationship is not precise enough to warrant action to reduce worldwide fossil fuel usage and therefore CO 2 emissions.

What role have the time domain techniques o f statistics and e c o n o m e t r i c s served in this research? While atmo- spheric scientists occasionally utilize univariate ARIMA models to portray the behaviour o f certain variables, most o f these researchers work with frequency domain methods. Indeed, Kuo et al argue that the frequency domain is su- perior to time domain methods for applications in the atmo- spheric sciences. What literature exists using time domain methods applied to the atmospheric sciences? In an exer- cise aimed at portraying new techniques in time series ( i e a paper that was not particularly devoted to atmospheric sci- ences) Young (1991) present a VAR analysis o f monthly global CO 2 concentrations with global sea surface tempera- tures, and find significant twoway causality. In the journal Climatic Change, Granger and Engle (1987) present a sur- vey o f relevant time series techniques aimed at atmospheric scientists.

The present paper represents the first stage in this en- deavour. In this paper, we study the relationship between global population growth and industrial CO 2 emissions for the period 1880 to 1989. To date, research efforts have util- ized population as a right-hand side variable in models which predict CO 2 emissions (see for example Edmonds and Reilly, 1986). From a visual inspection o f industrial CO 2 emissions data, we can easily infer that fluctuations in economic activity such as world wars, depressions, and oil crises leave an imprint upon the record, as explored by EI- liott (1983).

The nature o f the causation between population and CO 2 emissions, as well as the underlying long-term trend, i f any, between these variables is relevant to a number o f policy concerns. From a technical perspective, models which use population as an independent variable in predicting CO 2 emissions (such as Smil, 1990) assume unidirectional causality from population to CO 2, which has yet to be fully explored. While most observers readily accept this causal assumption, there is reason for caution. The implicit model underlying this causality perspective holds that population interacts with economic development, which is tied to in- dustrialization and thereby CO 2 emissions. Alternatively,

one might simply believe that population is a valid proxy for any number o f underlying variables. However, there is controversy as to the causal relationship between popula- tion growth and eco n o m i c development. This has led to quite contrary policy prescriptions concerning the validity o f population growth policy in general. As described by Moffett (1994), modern Malthusians hold that 'excessive' population growth causes a level o f economic development insufficient to sustain that population. Others hold that the problem is insufficient economic development, and that if anything, laissez-faire population growth is appropriate be- cause it stimulates economic growth. The argument is that policies which promote economic development will ulti- mately reduce 'excessive' population growth, as rising in- comes reduce the demand for children. If we accept the plausibility o f the latter position, then we must consider that, in effect, CO 2 emissions, tied to eco n o m i c growth, may 'cause' resultant changes in population. Judgements as to the nature o f the causal relationship between population and CO 2 emissions should therefore influence one's sense o f the relative importance o f controlling population growth as a component in a policy aimed at curbing CO 2 emis- sions. As described below, the nature o f the long-term rela- tionship (the existence o f a stable c o m m o n trend) is also relevant for the specification o f policies for abating CO 2 emissions, and the role o f population control within such a policy.

Consider the case where population and CO 2 emissions are cointegrated, meaning they share a long-run c o m m o n trend. If true, controlling population growth is the key fac- tor in global CO 2 abatement policy. Is this likely the case? There is good reason to suspect that population and CO 2 emissions are not cointegrated. As described by Schipper (199 l) and Ogawa (1991), energy intensity and energy effi- ciency across nations and across time are crucial to explain- ing CO 2 emissions. In a panel study o f 108 countries, H o l t z-Eak i n and Selden (1995) find a diminishing mar- ginal propensity to emit CO 2 (MPE) with respect to per capita GDP. The inverted U-shape o f the MPE function suggests that the path o f future C O 2 emissions will be in- creasingly dominated by LDCs with higher emission prop- ensities. As a group, these nations are experiencing high growth rates in both economic activity and population.

Taken together, these lines o f research suggest that pop- ulation and CO 2 emissions are not cointegrated, given these intervening influences. Policies aimed at dampening the growth o f CO 2 emissions in LDCs should therefore address the interaction between economic development and popula- tion growth, as well as the behaviour o f the energy sector in these nations.

An objective o f the present study is to provide indirect evidence that might be o f use o f policy makers in the light o f the scientific uncertainty, and to motivate increasing ef- forts to study these issues from an econometric perspective. Following the suggestion by Kuo et al (1990), the objective o f this study is to initiate a series o f research efforts which apply time domain econometric techniques to various sets o f data in order to assess the underlying long run statistical

34 Population growth and global CO 2 emissions: T Knapp and R Mookerjee

relationship between human activity (ie fossil fuel combus- tion and other factors) and long-term mean global tempera- ture c h a n g e s , while controlling for natural effects, in- cluding sunspots, El Nino events, volcanic activity, and sulphur dioxide emissions.

The paper is arranged as follows. The next section pre- sents a description o f the data and then the m e t h o d o l o g y used in the study. The third section contains the empirical results, while the fourth section concludes the paper.

Data sources and methodology

Much o f the data for this study was found in Trends '91 which is produced b y the CO 2 Information Analysis Cen- ter, Oak Ridge N a t i o n a l L a b o r a t o r i e s . The global CO 2 emission data represent estimates o f total emissions mea- sured in tonnes o f carbon from fossil fuel burning, cement production and gas flaring, which are based on the UN en- ergy statistics and US Bureau o f Mines cement manufac- turing data. For a detailed description o f carbon data see Elliot (1983). The values are expressed in tonnes o f carbon emitted per year. This source also provided world popula- tion estimates for 1950 to 1989, which are derived from published reports b y the UN. World population estimates for 1 8 8 0 - 1949 proved to be difficult to obtain. Various au- thors p r o v i d e estimates for certain years in this interval, but no consensus set o f estimates exists for the entire time period. The procedure used to develop estimates o f world p o p u l a t i o n is d e s c r i b e d below. C o n s e n s u s e s t i m a t e s for 1850, 1900 and 1925 are found in Willcox (193 I, 1940) and the UN (1960) provides estimates for 1920, 1930, 1940 and 1950. T h e d i f f e r e n c e s in these values w e r e distributed evenly, which imposes a declining growth rate. An alterna- tive method would be to fit an exponential function to the available data and interpolate the interdecadal values (see for example Westing, 1981). This alternative method was not e m p l o y e d , as the c h o i c e b e t w e e n the two m e t h o d s seemed arbitrary. 9

Methodology

To explore the nature o f the relationship between global population growth and C O 2 emissions, we employ the test o f causality as developed b y Granger (1969). While contro- versy still pervades the causality literature (see Jacobs et al, 1979; C o n w a y et al (1984)), the direct test o f G r a n g e r c a u s a l i t y has b e e n found to be the m o s t efficient (see Guilkey and Salemi, 1982).

The standard direct test o f Granger causality is based on the following regressions:

n

Z ~ t = OE O at- ~ ~xiZ~Xt-i -]- ~ ~ y i A y t - i ~- ~'t i=l i=l

(1)

where A is the first difference operator and Ax and Ay are stationary series. The null h y p o t h e s i s that y does not Granger cause x is rejected i f the coefficients of ~yi in Equa- tion (1) are jointly significant, based on a standard F test and vice versa. Four possible outcomes are: (1) there is uni- directional causality from x to y; (2) there is unidirectional causality from y to x; (3) there is bidirectional causality be- tween x and y; and (4) x and y are causally independent.

A recent more comprehensive test o f Granger causality is also employed. This test developed b y Granger (1986) and Engle and Granger (1987) is based on error correction models, derived from cointegrated properties o f time series variables. Formally, two variables are cointegrated or have long-term equilibrium relationship i f they share a c o m m o n trend. This alternative framework to the standard Granger causality test outlined above, allows for the possibility that the lagged level o f a variable x, can potentially help explain the current c h a n g e in another variable y, even i f past changes in x do not. Causality between variables sharing a common trend m a y not be detected b y the standard Granger causality test. In this comprehensive Granger causality test framework, we may, as in the standard G r a n g e r tests ob- serve unidirectional or bidirectional causality; however, i f the two variables are cointegrated (or share a c o m m o n trend) then the possibility o f no causality is ruled out.

The test o f causality on cointegrated variables uses the following error correction equation:

n n

z ~ t = a O + E f l x i Z ~ X t _ i + E f l y i A y t _ i + ~ l I . ~ t _ l + E t ( 2 ) i=l i=l

where x t a n d y t are cointegrated first differenced time series, and where t.t t is the lagged value o f the error term from the cointegration equation:

X t = Oy t "4- ].l t ( 3 )

Equation (2) is the standard Granger causality test equation augmented by the term lat_q, which is the error correction model. The inclusion o f It, provides for an additional chan- nel through which potential causality between x and y can be detected. Thus the rejection o f the null hypothesis that y does not cause x in (2), is based not only on the finding that the ~yi are jointly significant, but also i f the coefficient o f ~t-1 is significant. Hence, in contrast to the standard test, the error correction approach allows for y to cause x even i f lagged changes in y are not jointly significant.

9A referee suggested we try alternative methods of interpolating the miss- ing population values and then conduct sensitivity analysis to compare the alternative methods of interpolation. However, we decided not to pursue this due to the following reasoning. Because we are undertaking causality tests, we should err on the side of conservatism in 'fitting' the population data in order to avoid spurious results. Moreover, cointegration techniques search for the long-term relationships among the data, which should not be affected by the method chosen to interpolate missing data.

Empirical results

As discussed earlier, the data on global population and CO 2 emissions are annual and cover the period 1880 to 1989. Table l reports the time series properties o f the two series for the probable order o f difference stationarity. The data

Population growth and global CO 2 emissions: T Knapp and R Mookerjee 3 5

Table I T i m e series p r o p e r t i e s o f g l o b a l CO2 e m i s s i o n s and p o p u l a t i o n 1 8 8 0 - 1 9 8 9 ( a n n u a l ) a

A D F test Levels First differences W i t h c o n s t a n t W i t h c o n s t a n t and t i m e trend W i t h c o n s t a n t W i t h c o n s t a n t a n d t i m e t r e n d

C O 2 ~ ) . 7 3 1.96 ~ . 9 1 *** 7 . 8 8 * * * P o p u l a t i o n 1.29 - 1 . 2 8 ~ . 5 8 " 3 . 6 6 * *

aAI1 v a r i a b l e s are m e a s u r e d in l o g a r i t h m s . Th e c r i t i c a l v a l u e s for the a u g m e n t e d D i c k e y - F u l l e r test ( A D F ) are those found in M a c K i n n o n (1990). * * * S i g - n i f i c a n t at the 1% level. * * S i g n i f i c a n t at the 5 % level. * S i g n i f i c a n t at the 10% l e v e l ( l o g a r i t h m i c t r a n s f o r m a t i o n o f the d a t a w a s d o n e to e n s u r e a n o r m a l d i s - t r i b u t i o n , b a s e d on the J a c q u ~ B e r a test statistics)

T a b l e 2 C o i n t e g r a t l o n r e g r e s s i o n s o f global C O 2 e m i s s i o n s and p o p u l a t i o n a

Coefficient o f C o n s t a n t C O 2 P o p u l a t i o n R 2 D W A D F

1880--1989 ( a n n u a l ) 18.69 0.401 0.9427 1.05 1.33 -43 -47 2.34 0.9426 I. 15 2.05

1 9 5 0 - 1 9 8 9 ( a n n u a l ) 17.63 0.535 0.9631 I. 10 ~ ) . 4 8 31-40 - 1.79 0.9637 I. 16 -.4).077

aAll v a r i a b l e s are m e a s u r e d in l o g a r i t h m s

for both series were converted to logarithmics, since the non-logarithmic data was not normally distributed based on the J a r q u e - B e r a test. Tests o f n o n - s t a t i o n a r i t y are con- ducted using the augmented Dickey-Fuller test (ADF). The tests are conducted using only a constant and then a con- stant and a time trend, since there appears to be no consen- sus on this issue (see Dickey e t al, 1986).

According to the test statistics reported in Mackinnon (1990), non-stationarity cannot be rejected for the levels o f both population and C O 2 at the 1, 5 and 10% levels, how- ever, the ADF tests on the first differences o f the two series allow for rejection o f non-stationary for both series for all three levels o f significance regardless o f whether the ADF test is based on a regression employing only the constant term or the constant and time trend term. Based on these findings we estimate the cointegration equations using uni- differenced data (non-stationary) and the error correction equations with first differenced data (stationary).

Table 2 reports the results o f the cointegration equations between population and CO 2 emissions. These equations are run using data for the entire period 1880 to 1989, and for the more recent subperiod 1950 to 1989, to see i f there were any changes in the long-run relationship between the two variables. The cointegration equations for both periods are run in both directions, since a p r i o r i both directions are equally valid. Besides the e s t i m a t e d coefficients on the population and CO 2 variables, Table 2 also reports the R e and Durbin Watson statistic for the cointegrating equations. In all the estimated cointegration equations the R 2 is high and the Durbin Watson statistic is not too low. The last col- umn reports the ADF statistics for the residuals o f the coin-

tegrating regressions. None o f the ADF statistics is signi- ficantly negative; thus we cannot reject the non-stationary o f the residuals. Since cointegration requires s t a t i o n a r y residuals we can conclude there is no detectable long run equilibrium relationship between population growth and CO 2 emissions over the period 1880 to 1989, nor over the m o r e recent period f r o m 1950 to 1989. Ordinarily this would eliminate the need to use error correction models to detect for causality; however, as noted by Hendry (1986) cointegration regressions that exhibit high R 2 and D u r b i n - Watson statistics that are not too low, are reasonable candi- dates for the error correction approach. Our cointegration regressions as reported in Table 2 generally pass these cri- teria, and hence we employ error correction models to de- tect causality between population and CO 2 emissions.

Table 3 reports the F-statistics for the standard Granger causality tests o f whether population growth causes C O 2 emissions or vice versa. T h e s e results in turn p r o v i d e a basis for comparison for the results from error correction models. Only the results o f Granger causality tests using two, four and eight lags on both variables are reported. Longer lag lengths were also employed without any change in the results. C a u s a l i t y tests are c o n d u c t e d o v e r t w o p e r i o d s (1880001989) and (1950--89), to detect for any changes in the short-term dynamic relationship between the two variables. For the period from 1880 to 1989 there is a clear unidirectional causality from CO 2 emissions to popu- lation growth. This is an interesting finding in that it vali- dates population research scientists' call for the use o f CO 2 emission data as a reliable predictor o f population growth (see Newell and Marcus, 1987). For the 1950 to 1989 period,

Table 3 F-statistics for G r a n g e r c a u s a l i t y between global C O 2 e m i s s i o n s and p o p u l a t i o n a

L a g s D e p e n d e n t v a r i a b l e 2 4 8 C o n c l u s i o n

188(~ 1989 C O 2 1.372 1.128 0.899 P o p u l a t i o n - C O 2 P o p u l a t i o n 6 . 3 3 4 * * * 3.187** 1.734* C O 2 ~ p o p u l a t i o n

1 9 5 f f 8 9 C O 2 4.20** 2.572 1.161 P o p u l a t i o n C O 2 P o p u l a t i o n 9 . 5 8 0 * * * 5 . 9 8 2 * * * 1.562 C O 2 --~ p o p u l a t i o n

~The a r r o w s h o w s the d i r e c t i o n o f c a u s a l i t y . A s t r a i g h t line s u g g e s t s a lack o f c a u s a l o r d e r i n g b e t w e e n the t w o v a r i a b l e s . * * * S i g n i f i c a n t at the 1% level. * * S i g n i f i c a n t at the 5 % level. * S i g n i f i c a n t at the 10% level.

36 Population g r o w t h a n d g l o b a l C O 2 emissions: T K n a p p a n d R M o o k e r j e e

T a b l e 4 T e s t statistic for c a u s a l i t y b e t w e e n global C O 2 e m i s s i o n s and population using e r r o r c o r r e c t i o n m o d e l s based on cointegration r e g r e s s i o n s a

Residual from Residual from D e p e n d e n t C O 2 on population on v a r i a b l e population C O 2

1880-1989 CO 2 1.394 0.639 Population 2.036* 1.673

195Oqg9 CO 2 1.613 1.124 Population 1.892* 1.066

aFor both time periods, the t-statistics refer to first the residuals from the CO 2 on population cointegration regression, and then to the reverse cointe- gration regression. *Significant at the 10% level.

o p m e n t a n d C O 2 e m i s s i o n s is o f c r i t i c a l i m p o r t a n c e i f w e are to b e t t e r u n d e r s t a n d the r e l a t i v e i m p o r t a n c e o f p o p u l a - t i o n p o l i c y in a c o m p r e h e n s i v e C O 2 a b a t e m e n t strategy.

In f u r t h e r r e s e a r c h , w e s h a l l e s t i m a t e a V A R m o d e l o f a n n u a l g l o b a l m e a n t e m p e r a t u r e s w h i l e c o n t r o l l i n g f o r a host o f p o t e n t i a l l y r e l e v a n t factors. B y s t a t i s t i c a l l y i s o l a t i n g natural versus h u m a n a c t i v i t y i n d u c e d factors in e x p l a i n i n g g l o b a l t e m p e r a t u r e f l u c t u a t i o n s , s u c h r e s e a r c h m i g h t s e r v e to c o n t r i b u t e to the d e b a t e o v e r g l o b a l w a r m i n g p o l i c y in light o f the u n c e r t a i n t y h i n d e r i n g the s c i e n t i f i c u n d e r s t a n d - ing o f the p o t e n t i a l for h u m a n i n d u c e d g l o b a l w a r m i n g .

h o w e v e r , b i d i r e c t i o n a l c a u s a l i t y is d e t e c t e d b e t w e e n g l o b a l p o p u l a t i o n g r o w t h a n d C O 2 e m i s s i o n s . T a k e n t o g e t h e r t h e s e f i n d i n g s a r e i n t e r e s t i n g , in t h a t t h e y r a i s e s e r i o u s d o u b t s a b o u t the use o f p o p u l a t i o n g r o w t h as an e x p l a n a - t o r y v a r i a b l e for C O 2 e m i s s i o n in s t u d i e s o f g r e e n h o u s e ef- fects ( s e e O g a w a , 1991).

T a b l e 4 r e p o r t s t h e e r r o r c o r r e c t i o n m o d e l f i n d i n g s o f c a u s a l i t y , i n s t e a d o f t h e s t a n d a r d G r a n g e r c a u s a l i t y t e s t findings. T h e tests are c o n d u c t e d w i t h r e s i d u a l s from the co- i n t e g r a t i o n e q u a t i o n o f p o p u l a t i o n on C O 2 a n d the r e v e r s e c o i n t e g r a t i o n r e g r e s s i o n o f C O 2 o n p o p u l a t i o n , f o r b o t h p e r i o d s 1880 to 1989 a n d 1950 to 1989. F o r b o t h p e r i o d s

t h e r e is w e a k c o n f i r m a t i o n o f o n e - w a y c a u s a l i t y f r o m C O 2 e m i s s i o n s to p o p u l a t i o n , u n l i k e t h e s t a n d a r d G r a n g e r c a u s a l i t y t e s t w h i c h s h o w b i d i r e c t i o n a l c a u s a l i t y b e t w e e n C O 2 a n d p o p u l a t i o n in 1950 to 1989 p e r i o d .

Conclusion

T h i s p a p e r u s e s b o t h t h e s t a n d a r d G r a n g e r c a u s a l i t y f r a m e - w o r k a n d the m o r e c o m p r e h e n s i v e c o i n t e g r a t i o n a n d e r r o r c o r r e c t i o n m o d e l s to e x p l o r e the d i r e c t i o n o f c a u s a l i t y b e - t w e e n g l o b a l p o p u l a t i o n g r o w t h a n d C O 2 e m i s s i o n s for the p e r i o d i 880 to 1989. I n t e r e s t i n g l y , the results s u g g e s t a l a c k o f a l o n g - t e r m e q u i l i b r i u m r e l a t i o n s h i p b e t w e e n p o p u l a t i o n g r o w t h a n d C O 2 e m i s s i o n s , even t h o u g h a p r i o r i such a re- l a t i o n s h i p is w i d e l y a c c e p t e d in the e x t a n t l i t e r a t u r e . S e c - o n d , u s i n g b o t h s t a n d a r d G r a n g e r c a u s a l i t y t e s t s a n d e r r o r c o r r e c t i o n m o d e l s t h e r e s u l t s s u g g e s t a s h o r t - t e r m d y n a m i c r e l a t i o n s h i p f r o m C O 2 e m i s s i o n s to p o p u l a t i o n g r o w t h . T h i s f i n d i n g c a s t s s e r i o u s d o u b t s on r e c e n t s t u d i e s that use p o p u l a t i o n g r o w t h as an e x p l a n a t o r y v a r i a b l e in e x p l a i n i n g g r e e n h o u s e e f f e c t s , w h i c h i n c l u d e C O 2 e m i s s i o n s . T h e f i n d i n g s a l s o s u g g e s t s o m e v a l i d i t y f o r t h o s e p o p u l a t i o n s c i e n t i s t s w h o have c a l l e d for t h e use o f C O 2 e m i s s i o n s as a r e l i a b l e p r e d i c t o r o f p o p u l a t i o n growth. T h e l a c k o f c o i n t e - g r a t i o n b e t w e e n C O 2 e m i s s i o n s a n d p o p u l a t i o n s u g g e s t s that c o n t r o l l i n g p o p u l a t i o n g r o w t h is s u r e l y not the sole fac- t o r in a b a t i n g future C O 2 e m i s s i o n s . T h i s result a d d s w e i g h t to t h e i m p o r t a n c e o f f o c u s i n g u p o n the e n e r g y s e c t o r in the d e v e l o p e d a n d l e s s d e v e l o p e d n a t i o n s in p o l i c i e s a i m e d at u n d e r s t a n d i n g the p a t h o f C O 2 e m i s s i o n s into the n e x t c e n - tury. F u r t h e r m o r e , c a u s a l i t y test results, e s p e c i a l l y t h o s e for the 1950--89 p e r i o d , s u g g e s t that a b e t t e r u n d e r s t a n d i n g o f the d y n a m i c s a m o n g p o p u l a t i o n g r o w t h , e c o n o m i c d e v e l -

Acknowledgements

We w i s h to t h a n k Tom B o d e n o f C D I A C / O R N L for p r o v i d - ing the d a t a for this s t u d y a n d t h a n k s also to an a n o n y m o u s r e f e r e e for s u g g e s t i o n s w h i c h p r o v e d v e r y useful.

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