Explaining the cyclic behavior of freight transport CO2-emissions in Sweden over time

Transport Policy 23 (2012) 79–87

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Explaining the cyclic behavior of freight transport CO2-emissions in Sweden over time Fredrik Eng-Larsson a,n, Karl-Johan Lundquist b,1, Lars-Olof Olander b,1, Sten Wandel a,2 a b

Lund University, Department of Industrial Management and Logistics, SE-22100 Lund, Sweden Lund University, Department of Social and Economic Geography, SE-22100 Lund, Sweden

a r t i c l e i n f o

abstract

Available online 15 July 2012

Economic growth is often considered to be the main factor behind, and tightly coupled to, the increase in freight transport work and its energy use. Recent research has quantified the relative contribution from underlying factors like value density of products, transport intensity and carbon intensity of fuel. In this work we rely on the theory of economic growth cycles in order to explain the dynamic behavior of some of these indicators. Focusing on the current growth cycle, we analyze Swedish data in a Shapley decomposition model, and the behaviors of the underlying factors are confronted against the growth cycle theory and recent findings in micro logistics.Our results suggests that the different and changing relations between growth and emission over the growth cycle indicate that the observed development in emissions is far from linear and cannot be explained straightforwardly by economic growth. The impact of the respective factor, and the relation between them, changes over time and results in different degrees of decoupling. The general trend is that micro-oriented factors tend to be more important in the rationalization period while macro-oriented factors have a stronger impact during the transformation period. We suggest that our approach might be useful not only for analyzing historical data, but also for medium-term and long-term scenarios for freight transport development and CO2 emissions. & 2012 Elsevier Ltd. All rights reserved.

Keywords: Freight transport emissions Growth cycles Decomposition Macro-logistics

1. Introduction Today, transportation is one of the major sources of greenhouse gas emission. According to the latest assessment from the IPCC, on a global level, transportation accounted for 23% of all energy-related greenhouse gas emissions in 2004 (IPCC, 2007). Freight transport accounts for roughly a third of this figure (IPCC, 2007), and its share is continuously growing as greenhouse gases from transportation are growing in absolute numbers (World Business Council for Sustainable Development, 2004). In Sweden, as well as in many other countries in Europe, most industrial sectors are now seeing reductions in greenhouse gas emissions, and from 1990 to 2008 the overall emissions declined by 11%. The major exception is the transport sector, with freight transport greenhouse gas emissions increasing by 28% over the same period (Swedish Environmental Protection Agency, 2010). Following several other countries, Sweden has committed to a maximum global temperature increase of 21 as compared to pre-

n

Corresponding author. Tel.: þ46 46 222 8172; fax: þ 46 46 222 4615. E-mail addresses: [email protected] (F. Eng-Larsson), [email protected] (K.-J. Lundquist), [email protected] (L.-O. Olander), [email protected] (S. Wandel). 1 Tel.: þ46 46 222 9794; fax: þ 46 46 222 8401. 2 Tel.: þ46 46 222 8172; fax: þ 46 46 222 4615. 0967-070X/$ - see front matter & 2012 Elsevier Ltd. All rights reserved. http://dx.doi.org/10.1016/j.tranpol.2012.05.014

industrial levels, which requires a reduction in yearly global CO2emissions by 70%–90% by 2050 (MVB, 2008). As pointed out by several transport researchers in the past (e.g. Piecyk and McKinnon, 2010), such a reduction may not be necessary or even desired in every sector of the economy. Some sectors may more easily achieve larger reductions, thus relieving others and optimizing global abatement costs. Nevertheless, if any reduction in CO2-emissions from freight transport is to be achieved, the mechanisms that influence these emissions need to be well understood for properly designing political control measures. One way to disentangle the complex structure of these underlying mechanisms is by decomposing the aggregate change into changes in a number of underlying factors, and calculate the relative contribution of each of these factors at different points in time according to some allocation scheme. The method has previously been used extensively in economics and energy studies in order to explain changes in, for example, energy consumption based on changes in energy efficiency and industry structure. By approaching in this manner, ‘‘the underlying mechanisms of the change [can be] better understood. It allows the success of past policy to be better assessed. It also provides a basis for evaluating future changes in the aggregate’’ (Ang et al., 2003, p. 1564). Although the decompositions are most widely used in energy research (see review by Ang and Zhang, 2000), the transportation research field has seen several decomposition studies since

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mid-1990s. These have been both from a rather aggregate, macroeconomic, perspective (e.g. Schipper et al., 1997; Lakshmanan and Han, 1997; Greening et al., 1999; REDEFINE, 1999; Tapio, 2005) and from a logistics and transportation perspective (e.g. McKinnon and Woodburn, 1996; McKinnon, 2007). Recently, some studies have made contributions in connecting these areas, investigating both macroeconomic development and logistical changes which may lead to changes in aggregate measures. Recent contributions analyze what underlying factors that have driven the aggregate increase in road freight transport work in Denmark (Kveiborg and Fosgerau, 2007), road freight energy use in the UK (Sorrell et al., 2009) and greenhouse gas emissions from freight transport in Canada (Steenhof et al., 2006). These authors have investigated the changes over time and have, through different methods, quantified the relative contribution from some underlying factors (e.g. value density of products, transport intensity, carbon intensity of fuel) to the growth in the aggregate measure. The general conclusion has been that the growth in economic output has been the main factor behind the increase in freight transport work and its energy use. Despite this, little effort has been put into explaining the dynamic behavior of the underlying factors as they vary with the changes in economic growth rates. That is, although one can notice differences in the contribution from a certain factor at different points in time, focus has been predominantly on end-state figures (yearly averages) rather than dynamic behavior. This paper extends previous research by specifically addressing the dynamics of the observed development in Sweden 1990–2008. It has been shown that growth in freight transport work as well as growth in economic activity (GDP) seems to follow a cyclic pattern, longer than a short business cycle, with transport growth rates being higher than the growth in GDP in times of strong economic growth and much lower in times of slower growth (Lundquist and Olander, 2010a). That is, although the growth in GDP affects the level of freight transport emissions, other factors play an important role and their individual effects are different in times of strong and slow economic growth. In order to explain this behavior, we turn to economic growth cycle theory that focuses on growth trends much longer than short business cycles. The aggregate is decomposed using a Shapley decomposition method (see e.g. Albrecht et al., 2002), and the behavior of the underlying factors are confronted against the growth cycle theory and recent findings in logistics. The analysis aims to uncover the changing roles of different macro- and micro-oriented factors, and how the interplay between them change, during the periods of the growth cycle and result in very different character and degree of decoupling between economic growth and CO2-emissions. A better understanding of these underlying mechanisms will be useful in developing more reliable forecasts and subsequent policy measures. Policies developed and implemented in times of strong growth, with its own characteristics, may most likely be less suitable in times of slower growth, and vice versa. The next section discusses the basic conceptual ideas of growth cycles including a tentative discussion on how they may be related to the development of freight transports and emissions. The third section presents the specific model used in this paper and the fourth section contains the empirical analysis explaining growth in freight transport CO2-emission during different periods of the growth cycle. In the final section we summarize our main findings and draw some conclusions for further research.

2. Growth cycles 2.1. An overview When examining GDP over time in real prices, it is often concluded that the long-term economic development is linear,

steadily increasing, only interrupted by temporary exogenous shocks. However, focusing on the variations in the annual growth rates, another pattern emerges, showing cyclic patterns in economic growth lasting about 40 years, and interrupted by severe structural crises. The first observations of similar wave-like economic development were made by Kondratieff (1926) based on prices, inflation and interest rates. In his work on ‘‘Business Cycles’’ some years later, Schumpeter (1939) expressed his esteem of Kondratieff’s work and added new knowledge to his observations. The origin of the cyclic patterns may be found in General Purpose Technologies (GPT), technological shifts, and macro-innovations (Bresnahan and Trajtenberg, 1995; Lipsey et al., 2005). They create discontinuity and structural breaks between two growth cycles. Historically, these kinds of technologies have been connected to communication, transportation, and energy – technologies which may be used in many economic fields, creating dynamic development blocks in combinations with other innovations and technologies (Van Duijn, ¨ 2000; Freeman and Louca, 1983; Freeman and Perez, 1988; Schon, 2001; Perez, 2009). Corresponding GPTs and macro-innovations in our time are telecommunications, Internet, control instruments and computer-aided biotechnology and new materials. Explicitly connected to the long wave approach is also Kleinknecht’s (1987) argument, based on a Schumpeterian perspective, that economic growth, structural change and crisis are inseparably connected in ¨ (1994, 2010) who argues that periods of growth history and Schon and crisis in modern capitalism occur in certain regularities resulting in long cycles of growth, shared by many economies, although with time lags due to degrees of modernity and institutional differences. Empirical observations on national, sectorial and regional level ¨ supporting long cyclic change in Sweden could be found in Schon (1998, 2006) and Lundquist and Olander (2009). Kander and ¨ (2007) have also based on Swedish data analyzed the Schon changing relation between energy and capital from a long term cyclic growth perspective. Empirical analyses of the post war development of the US manufacturing industry by Keklik (2003) are shown to be consistent with long term cyclic growth. Furthermore, empirical evidence on regional growth cycles could be found for Italy (Mastromarco and Woitek, 2007) and the USA (Owyang et al., 2009). Although interpreted from a Marxist and not a Schumpeterian perspective Marshalls (1987) explorative analysis of long waves and regional development in the UK is another example providing empirical support. For a number of decades these insights were of limited interest to neo-classical growth theory. However, since the structural crisis in the late seventies, there has been a growing body of literature focusing on the importance of radical innovation and complementarities for long term growth and restructuring. Important contributions to the understanding of the origins of wave-like Schumpeterian economic development have emerged within endogenous growth theory represented by for instance Aghion and Howitt (1998, 1999). They have suggested explanations in formal models to why major technological changes tend to create long term cyclic growth patterns. Related to this vein of literature is the theoretical development on concepts like path dependency (Arthur, 1989; David, 1990), technology skill complementarities (Goldin and Katz, 1998, 2008) and technology waves (Gordon, 2000). These concepts were put forward for a better understanding of the economic growth and structural changes of the 1980s and the1990s. The renewed interest in neo-Schumpeterian perspectives on growth and economic transformation is also mirrored in economic geography (Boschma and Frenken, 2006; Frenken, 2007; Martin, 2010) where evolutionary economics are combined with traditional spatial perspectives providing new micro-theoretical foundations for the understanding of long term growth and decline of regions (Boschma et al., 2009; Boschma and Martin, 2010).

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Based on many years of empirical investigations on Sweden, the theoretical framework of growth cycles has been consolidated in aggregate and regional reference cycles, specifying mechan¨ 2000, 2006; Lundquist isms behind the course of events (Schon, et al. 2008a, 2008b, 2008c; Lundquist and Olander, 2010b; Svensson Henning, 2009). Departing from this research we will briefly discuss the long term impact of technology shift on growth rates, economic behavior and structural change and from there on address freight transport and emissions. Although our purpose in this paper is restricted to analyze the Swedish case it is reasonable, from the quoted literature and the empirical observations outside Sweden, to hypothesize that several of these findings concerning cyclic behavior in time series and differences in trend periods also apply in other countries, albeit with variations due to structural and institutional differences. For Sweden, the current and the previous cycle are identified in Fig. 1. The previous one had its roots in the late 1930s, progressed after WW2 into the 1950s and the 1960s. It then slowed down and reached its lowest level during the structural crisis in the late part of the 1970s. The current cycle has progressed far more than half way at present. If this cycle follows the previous pattern it will continue to decelerate and reach a new bottom level within a few years. For comparison, the longterm cyclic trend for the UK economy has been included in the figure. Although there are some differences in the amplitude of the waves and exact timing the two economies rhyme over time. Annual GDP growth rate variations are not the only way to identify these cycles. Productivity rates and investments in buildings, land and machinery follow for instance distinct patterns of their own and can be linked to the course of events during a cycle. Since GDP can be affected by political events and public sector size, annual growth rates, productivity rate development and the character of the investment patterns within the competing sectors often give us the sharpest picture of the cycle and its periods. ¨ (1994, 2010) and Lundquist and Olander Following Schon (2010b) a growth cycle consists of two sub-periods, a transformation and a rationalization period. Transformation is a period of rather dramatic change into new growth directions, based on radical technology shifts, but also on uncertainty. Rationalization is the following harvest period when most industries make use of the new technology base, adding incremental innovations, facing additional demand, but finally also increased competition, international overcapacity and struggle for survival. These sub-periods

7

Annual growth

6

GDP Transformation

Rationalization

Transformation

Rationalization

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Table 1 ¨ (1994, 2010) and Lundquist Characterization of the two periods (based on Schon and Olander (2010b). Transformation

Rationalization

GPT initiation New industries GPT diffusion Supply-driven industries Development blocks Slow productivity growth Bottle necks Building investments

Diffusion of competence Technology standardization Decomposition of production Demand-driven industries Consumption growth Rapid productivity growth Credit market expansion Machinery investments

are displaying very different economic behaviors and growth characteristics (see Table 1). The diffusion of GPTs and macroinnovations lead to investments directed towards new areas of production in the transformation period. They provide new bases for reallocation of resources between industries. Growth and new technology will increase rapidly in new industries and disseminate to old industries over time. In this period productivity growth will mainly come out of resource reallocations between industries and not from actions taken within industries. However, production will grow much more than productivity for a long time due to shortages in the supply of competence and to the need of learning the complicated processes that takes place. The new technology very often causes a downward shift in the relative prices of technology intensive goods. At first in the rationalization period (15–20 years) all services and demand driven industries are at full swing whereas manufacturing industries fall relatively behind. The period also means concentration of resources to the most productive units within each industry (Lundquist et al., 2008a; 2008b). New technology from the transformation period is standardized and diffused more effectively also to older industries. Investments are more directed to the reduction of costs. In rationalization, mergers between companies will increase, economies of scale will be more significant, and the competition and integration in foreign markets will increase. Demand from emerging economies will be more important to advanced economies than home market expansion from new technology and new industries. All this evolves gradually. Rationalization means also declining employment. The whole process will eventually lead to falling demand and overcapacity in international production. Debt crises coming from over consumption will end with a structural crisis. Sluggish growth rates and falling profits will after a while make societies and economies prepared to once again change into new growth directions. 2.2. Growth cycles, freight transport and CO2  emissions

5 4 3 2 1 0 -1 1950 1953 1956 1959 1962 1965 1968 1971 1974 1977 1980 1983 1986 1989 1992 1995 1998 2001 2004 2007 2010

-2

Previous growth cycle

Current growth cycle

Fig. 1. Annual growth rates (green line) in GDP in Sweden 1950–2010. Wavelet method, eliminating short term business cycles, is used to calculate the long-term cyclic trend (black line). Predictions for the last two years are based on cyclic trends for the period 1830–2008. Trend for the UK (dashed line) as comparison. (Source: Maddison Data Base. For interpretation of the references to color in this figure caption, the reader is referred to the web version of this article.)

Growth cycles entail considerable changes in production and in the ways in which it is organized and localized compared to previous cycles. Changes will also be different in the transformation and rationalization periods. Based on the referred growth cycle literature above this might be a good starting point for a hypothetical discussion on growth characteristics, transport work, transport technology and their final consequences for CO2-emissions over time. Hence, focus will be on the characteristics of the current growth cycle, especially on when the transformation period culminates and the rationalization period takes over and proceeds. 2.2.1. Transformation GDP annual growth rates are generally lower in the transformation period than in the rationalization period. The growth seen in the transformation period comes mainly out of new manufacturing

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industries and corresponding producer services (Lundquist et al., 2008b). The rise and establishment of new industries and new producer services imply a dramatic widening gap between weight of total manufacturing production and value-added, particularly since old manufacturing industries are lagging behind for a long time thereby adding very little value added to the economy. The new industries that arise during the transformation period are entrepreneurial and more concerned about growth than efficiency, and it can therefore be hypothesized that their supply chains are also less efficient and geographically less constrained. As the industry progresses, the supply chains will become increasingly well structured, but in the early transformation period, with untried ways of organizing production, less efficient solutions are likely to be implemented since frequent disturbances and changes in the production process and supply networks call for more flexible logistics networks including more truck and air transportation (see e.g. Lee, 2002).With this, the amount of ton kilometers and emissions per value added will be larger than for the incumbent old industries. It can therefore be hypothesized that during the transformation period, growth in freight transport emissions will be primarily driven by increased transport intensity and a shift to faster modes of transport, while the increased value density has a strong moderating effect on the aggregate levels. 2.2.2. Rationalization Economic growth in rationalization is, as compared to transformation, propelled by a wider scope of driving forces. New manufacturing industries get rationalized, decreasing their relative prices and increasing their relative volumes. Older industries expand their markets and services due to increased real wages and expanding credit markets. In the manufacturing industry, growth is more based on transformed traditional production than entirely new products, with smaller changes in characteristics and weight. In the service industries, growth is dominated by relatively ‘‘heavier’’ industries such as wholesale and retailing. The importance of customer services will increase in the rationalization period, a trend likely to generate more time controlled distribution systems and direct deliveries, increasing transport frequency and pushing vehicle kilometers upward. However, raw materials of various kinds will increase their shares of production and international trade since many countries, even technological laggards, take full part of the economic surge in rationalization. Rail and sea transports become more important modes of transport under these circumstances (see e.g.Woodburn and Whiteing, 2010), which should moderate the emission increase. Although the development of aerodynamics, vehicle weights and non-fossil fuels follows their own technological trajectories, hardly internal to the growth cycle perspective, the adoption of energy-saving technology is facilitated in the rationalization period, due to cost-cutting tendencies and the fact that the credit markets expand until the crisis is imminent, which facilitates investments in new fleets. It can therefore by hypothesized that during rationalization, growth in freight transport emissions will be primarily driven by increased economic activity, while increased operational efficiency and shifts to less polluting modes of transport countervail the total growth. We suggest that the relations between the growth cycle and freight transports/emissions will be put to an empirical test and evaluated. Andersson and Elger (2012) have already found empirical evidence for Sweden that suggest that the relationships between freight transportation measures and real economic variables differ across time horizons (Andersson and Elger, 2012). However, they did not link to growth cycle theory and

provided no conclusive theoretical explanation to their findings. The importance of non-linear macrogrowth and economic transformation to freight transports/emissions need to be explored in depth in order to match the rather well researched relation between firm-logistics/technological development and freight transports/emissions.

3. Our decomposition model In order to make such an assessment we will rely on a decomposition analysis. The decomposition may be carried out in a number of ways and several methods have been suggested in previous research. A good general overview on the topic is given by Ang and Zhang (2000) and Ang (2005). Some of the commonly used decomposition methods, e.g. the arithmetic mean Divisia Index method, are convenient to work with and have been widely applied also in the transport research field (e.g. Kveiborg and Fosgerau, 2007). Although computationally simpler, the methods leave a (sometimes large) residual term which may, in cases, distort the results and ‘‘leave a new puzzle for the reader’’ (Sun, 1998, p. 87). In order to come to terms with these methodological problems, several refinements of existing methods have been suggested in which ‘perfect decomposition’ is achieved, i.e. decomposition which does not leave any residual term. Examples are the Logarithmic Mean Divisia index method (see e.g. Ang and Liu, 2001; Sorrell et al., 2009), the refined Laspereyes index method (Sun, 1998) and the Shapley value decomposition method (Albrecht et al., 2002). A discussion on different perfect decomposition methods is given by Ang et al. (2003). For our decomposition we choose to rely on the method referred to as Shapley decomposition (Albrecht et al., 2002) or refined Lespeyres method (Sun, 1998; Ang and Zhang, 2000; Ang et al., 2003). Although computationally heavier than other ‘perfect’ decomposition methods (e.g. the log-mean Divisia Index method) we choose this approach as it provides a simple, intuitive, interpretation based on Shapley (1953): each factor’s contribution to the aggregate is the mean of its marginal contributions for all possible, equally probable, combinations of the given factors. For a more thorough description of the methodology we refer to the introduction by Albrecht et al. (2002) and the subsequent discussion in Ang et al. (2003). Data was gathered from Statistics Sweden, the Swedish Institute for Transportation and Communication analysis, the Swedish Environmental Protection Agency and the Swedish Energy Agency. Data depict transports within Sweden undertaken by Swedish as well as foreign enterprises (see Appendix Table A1). We decompose the freight transport CO2-emissions into changes in economic activity (G) as measured in GDP, inverse value density (T/G) in terms of transported ton per GDP, transport intensity (TK/T) in terms of ton kilometers per transported ton, traffic intensity (VK/ TK) in terms of vehicle kilometers per ton kilometers, and finally the emissions factor (C/VK) in terms of CO2-emissions per vehicle kilometer. This is similar to previous studies, but due to the lack of accurate data for modes other than road transport some modifications had to be made. For example, the handling factor often considered in road freight studies (McKinnon and Woodburn, 1996; REDEFINE, 1999) is only found as part of our larger value density measure, and the length of haul (e.g. McKinnon and Woodburn, 1996) is reflected within our transport intensity measure. For the same reason, a disaggregation into industry sectors and commodity groups (as in Kveiborg and Fosgerau, 2007) has not been made. An overview is given in Table 2. The decomposition follows the logic of the Kaya Identities often used in energy studies (Kaya, 1990), but for this study we

F. Eng-Larsson et al. / Transport Policy 23 (2012) 79–87

Total freight transsport CO2 emission

Table 2 Factors used in the decomposition. Notation Subscript in graphs

Description

C

Emissions

G T/G TK/T TKm/TK VKm/TKm

GDP Val_Dens Tsp_Int Mode_sp Tfc_Int

Freight transport CO2 emissions Economic activity (GDP) Inverse value density Transport intensity Modal split Traffic intensity of mode m

Cm/VKm

Emf

Emission factor of mode m

GDP, fixed prices 160

Unit

Transformation

Index 100 = 1990

150 kg CO2 SEK Ton/SEK Ton km/ton Fraction Vehiclekm/ ton km Kg CO2/ vehicle km

In order to assess the change in the aggregate caused by changes in the structural factor as well as by the other factors, the change in C (from Eq. (1)) is decomposed in additive form according to ð2Þ

The effect from each factor was then found by means of Shapley decomposition, in which each factor i’s effect on the aggregate change was found according to X 9S9!ðn9S91Þ!  ðvðS[ i ÞvðSÞÞ n! S D N\fig

140 130 120 110 100

1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008

80

m

DC emissions ¼ DC GDP þ DC V al_Dens þ DC Tsp_Int þ DC Mode_Sp þ DC Tf c_Int þ DC Emf

Rationalization

90

also add a structural factor and investigate changes in the abovementioned factors for three different modes (m) of transport: rail, road, and water. This allows us to represent total national freight transport CO2-emissions (C) as X ðG  T=G  TK=T  TK m =TK  VK m =TK m  C m =VK m Þ ð1Þ C¼

DC i ¼

83

Fig. 2. Development of GDP in fix prices and Freight Transport CO2 emissions.

Table 3 The contribution (Shapley values) to change in freight transport CO2-emissions from the six factors relative base year emissions at three time intervals: the transformation period (1990–1999), the rationalization period (1999–2008), and total period. factor

Transformation 1990–1999

Rationalization 1999–2008

Total period 1990–2008

DCGDP (%) DCVal_dens (%) DCTsp_int (%) DCMode (%) DCTfc_int (%) DCemf (%) DCemissions (%)

16.94  33.52 30.64 11.36  14.98 5.26 15.70

26.00  14.40 6.00  3.59 6.23  9.43 10.81

47.02  50.18 37.58 7.21  7.78  5.65 28.20

ð3Þ

where S D N is the set of all factors N set to influence the aggregate, and V(S) the value of the aggregate when the factors in S use data from year T, while all other factors use the value of year 0 (c.f. Ang et al., 2003).

during different periods of the growth cycle, a decomposition of the emissions must be made. The results of this decomposition are found in Table 3. In the following sections, we confront the results of the decomposition with the hypothesized development of the growth cycle perspective.

4. Explaining growth in freight transport CO2-emissions

4.1. The total period

During 1990–2008, the total growth in freight transport CO2emissions in Sweden was 28%. In Fig. 2 this development is depicted alongside GDP for the same period. The total Swedish GDP has grown considerably stronger, close to 50%, and clearly exceeds the growth in emissions. In the most recent year of the period the decoupling between growth and emissions is particularly strong. Certainly, the financial crisis as an early warning of a coming structural crisis explains most of the strong last year dip, but the underlying structural trend is visible in the figure. It is visible from Fig. 2 that a certain correlation between GDP and CO2-emissions exist. But the relation between them is different in the two periods of the growth cycle. GDP growth rates are generally lower in transformation than in rationalization, while freight transport CO2-emissions increase at a much stronger pace in transformation than in rationalization. The different and changing relations between economic growth and freight transport emissions over the growth cycle indicates that the observed development in emissions is far from linear and cannot be explained straightforwardly by economic growth. As reported in the quoted literature and discussed in the theoretical part of this paper, several other factors – macro-oriented as well as microoriented – have an effect on this development. In order to further investigate the drivers behind the observed development

Looking at the total period in Table 3, the three macro-oriented factors reveal the greatest impact on total freight transport emissions. Growth in GDP has the highest impact (þ 47%) followed by transport intensity ( þ38%). The overall impact from these factors is partly counterbalanced by the very strong structural change measured through an increase in value density (decrease in inverse value density, 50%). This structural change, which includes the shift to a more knowledge-based manufacturing industry as well as a general trend of ‘‘servicification’’ within the economy (Lundquist et al., 2008b; Lundquist and Olander, 2009), is probably the single most important factor holding back freight transport emissions in Sweden. As mentioned, data cannot be disaggregated to give a safe answer to this. Microoriented factors, related to new technological, organizational, and managerial solutions in transport and logistics, are subordinated to the macrooriented factors when looking over the total period. The total effect of modal split, mainly through the increase in road transports, has contributed to a moderate increase in emissions ( þ7%) while factors connected to traffic intensity (capacity of vehicles and degree of utilization of capacity) display a weak opposite effect ( 8%). These findings are well in line with previous studies (Sorrell et al., 2009; Kveiborg and Fosgerau, 2007; Ahman, 2004).

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GDP

vkm/tonkm

GDP

vkm/tonkm

ton/GDP

CO2/vkm

ton/GDP

CO2/vkm

tonkm/ton

CO2

CO2 tonkm/ton Transformation

160 150 140 130 120 110 100 90 80 70 60

140

Rationalization

130 120 110 100

2007

2008

2006

2004

2005

2002

2003

2001

2000

1999

1998

1996

1997

1995

1994

1993

1991

1992

1990

90

Fig. 3. Development of five underlying factors (structural factor excluded) and the aggregate from 1990 to 2008. Indexed values, base year 1990¼ 100.

In this study, rather than the end-state figures, we are interested in the dynamics over the investigated period. In Table 3 and Fig. 3 it can be seen that different factors play different roles during the growth cycle. The impact of the respective factors and the relation between them change over time. The general trend is that macrooriented factors tend to have a stronger impact on the development in the transformation period, while micro-oriented factors have a stronger impact during the rationalization period. This is consistent with the conceptual framework of growth cycles discussed in previous sections, and in order to further explain this development we will now investigate each period in isolation. 4.2. Transformation In Table 3 it is seen that the changes in value density and transport intensity demonstrate a considerably stronger impact in the transformation period than in the rationalization period. This is expected from a growth cycle perspective. During the 1990s, the transformation of the Swedish economy was mainly driven by a strong restructuring of the manufacturing sector (Lundquist et al., 2008a). Strong growth of new knowledge intensive industries, technological revitalization of traditional industrial sectors, and out-phasing of obsolete and stagnated industries, resulted in a dramatic increase of value added per ton produced in the Swedish economy. This manufacturing-related restructuring of the Swedish economy is the single most important factor holding back the growth rates of CO2 emissions. On the other hand as indicated in Fig. 4 this restructuring had an induced effect leading to a strong increase in transport intensity. One plausible interpretation is that the new industries as a growing part of the economy most probably had to establish supply chains not used before, trying to find suppliers for new components wherever they could be found. Unforeseen bottle necks in production and deliveries appeared and forced the new industries to change suppliers repeatedly without considering optimal locations, costs or logistic efficiency. The soaring increase in transport intensity more or less leveled out the benefits spawned from the structural change during the transformation period. The positive impact from modal split ( þ11%) was entirely a result of increased road transport. This should be expected, as the rise of new supply chains and experimental ways of organizing production also meant increased demand for flexible transport arrangements. While changes in transport intensity and the increased road transport had a positive impact on emissions during the transformation period, changes in traffic intensity had a moderating

80 70 60 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 Fig. 4. Development of five underlying factors during the transformation period 1990–1999.

effect (  15%). This differs from our hypothesis as it suggests an improvement in transport resource utilization in a period where firms are more concerned about growth than efficiency. However, several exogenous shocks during the period may explain this development. One is regulation. In Sweden, new regulation allowing heavier trucks was introduced in the mid-1990s. This caused an increase in both vehicle capacity and average payload, increasing efficiency in road transportation in Sweden (Ahman, 2004; REDEFINE, 1999). Another explanation is the changes in the logistics market. With Sweden’s entry in the European Union, the less than truck load transport industry in Sweden consolidated into a few dominating companies that had 85% of the market. These large players optimized their traffic networks into fewer terminals served by fewer and larger vehicles with a higher fill ¨ rate than before (Transportindustriforbundet ,2010; Berglund et al., 1999). Studies of other countries over the same time-period show similar results (Kveiborg and Fosgerau, 2007). Hence, owing to these changes, the aggregated measures show signs of efficiency improvements already in the transformation period of the Swedish growth cycle. Lastly, the emission factor shows a positive impact on the emission growth ( þ5%). This is expected, both since the transformation period is characterized by growth rather than efficiency improvements, but also due the larger vehicles that were introduced in the period. Since growth was strong, the vehicle fleet was not updated with new innovations in aerodynamics, tire inflation, fuel types and engines fast enough to slow down emission growth. The larger vehicles and the larger loads consumed more energy and consequently emitted more emissions. The efficiency improvements in vehicle utilization were thus slightly offset by the increase in emissions per kilometer. 4.3. Rationalization As seen in Table 3 and Fig. 3 the relation between macro- and micro-factors looks very different in the rationalization period as compared to the transformation period. In Fig. 5, the development for the rationalization period is depicted. The most important difference to the transformation period is that the interplay and complementarities between macro-factors and microfactors results in a clear decoupling between economic growth and emissions, despite the high GDP growth rates that characterize the rationalization period up until the global financial crisis.

F. Eng-Larsson et al. / Transport Policy 23 (2012) 79–87

GDP

vkm/tonkm

ton/GDP

CO2/vkm

tonkm/ton

CO2

140 130 120 110 100 90 80 70 60 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 Fig. 5. Development of five underlying factors during the rationalization period, 1999–2008.

The higher GDP growth rates in the rationalization period also had stronger impact on total freight transport CO2-emissions, 26% as compared to 17% in the previous period. But rather than growth rate per se, it is the forces propelling growth, and its effects on other macro- and micro-factors, that is of importance for the total emissions. As showed by Lundquist and Olander (2010b), these driving forces are quite different in the different periods of the cycle. Understanding these forces is a key dimension for better understanding the relation between growth and long term development of transport emissions. Growth in the rationalization period is a result of consolidation and rationalization within industries. This process leads to increased productivity in general and strong growth for a wide spectrum of services in particular. From this point of view, the emergence of the ‘weightless economy’ in Sweden was mainly a result of fundamental manufacturing restructuring in earlier periods of the growth cycle, a process more or less completed around the turn of the millennium (Lundquist et al., 2008a, 2008b). This is a major reason to why the value density factor’s impact stabilized during the rationalization period (Fig. 5). Further, with the higher economic growth of the rationalization phase, a strong increase in global demand for natural resources and raw material (especially from emerging economies that come strongly in the second half of the growth cycle) resulted in a resurgence of traditional Swedish industries such as mining, steel and forestry. This path of development is not only reflected in a strong increase of the value added but also in terms of the soaring transport costs of these sectors (Lundquist and Olander, 2009). This partly counterbalanced the overall trend of ‘servicification’ and hampered further increases in value density for the total economy. The total effect is a strong decrease in the impact from the value density factor, falling from  34% to  14% for the rationalization period (Table 3). It should be noted though that the remaining negative impact on emission growth is due to the general ‘servicification’ of the Swedish economy and not a result of further weight losses in manufacturing. Owing to the increase in raw material industry, the value density in manufacturing has most likely decreased. In Table 3 it is seen that the change in transport intensity during the rationalization period has a positive impact on

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emissions ( þ6%), but so less than during the transformation period when it was the single most important factor. This development is consistent with the hypothesis put forward in Section 2. During the rationalization period, the pressure to increase scale economies and productivity in a wider spectrum of the economy spill over in a general demand for rationalization and efficiency in companies’ supply chains. As showed by Lundquist and Olander (2009), this development is mirrored by decreasing logistics and transport costs for Swedish companies, especially in the manufacturing sector, starting in the late 1990s. This process resulted in a redistribution of the nation’s total transport costs, from manufacturing industries to service industries, particularly to the retail and wholesale industry. The overall effect was a slower increase in ton kilometers per ton transported during the rationalization period and hence decreased growth rates of emissions 1999–2008. In Table 3 it can also be seen that, in line with the above, the modal split factor has shifted from a rather strong positive impact to a slightly negative impact (  4%) over the growth cycle, implying that a greater share of the total ton kilometers were transported by means of rail and ship and less by means of allroad solutions. This should be expected for two reasons. First, since the new production is rationalized and supply chains become increasingly well-structured, the need for flexible transport solutions is reduced. Second, the expansion of older manufacturing industries and the revival of raw material production lead to a stronger demand for rail- and seabound transport solutions, since these are commonly preferred by heavy industry that benefits from the inherent scale economies of these transport ¨ modes (Transportindustriforbundet, 2010). The end result was a negative contribution to freight transport emissions over the period. The impact from traffic intensity changes over the cycle from negative to positive (6%). This indicates either falling capacity of vehicles and/or lower degree of utilization of capacity during the rationalization period. The positive impact may in part be explained by the increased importance of customer service on the commodity market that was suggested in the theory section. ¨ It has been shown by Transportindustriforbundet (2010) that smaller distribution vans increased their share of total road traffic during this period, a development likely induced by increased demand on shipment frequency and just-in-time delivery. From a supply chain perspective, one can therefore notice that while transports are becoming increasingly well organized and consolidated in industries far back in the supply chain (industries far from the consumer) during the rationalization period, another set of driving forces increase traffic work closer to the consumer. It is however important to underline that the traffic intensity over the total period has proved to have a significant negative impact on the growth rates of emissions and the level has been more or less constant since the beginning of the millennium. Finally the emission factor that showed positive influence during the transformation period has shifted to have a lowering effect on emissions during rationalization (  9%). As in the transformation period, the connection to the vehicle load cannot be discarded. However, the decrease in the emission factor is more likely related to technological breakthroughs in the transport sector, many of them recognized and introduced to the market already in the late 1980s and early 1990s. It is with a considerable time delay, when technology has matured and the receiver capacity in the industry has reached proper levels that these technologies and new ways of organizing transports pay off in terms of negative impact on emissions. The effect of these efforts coincides with strong economic incentive for firms and industries to decrease cost in general in the rationalization period. From this point of view the negative impact on emission is more a

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side effect of cost saving strategies than deliberately attempts to decrease CO2-emission. This is supported by findings of Lundquist and Olander (2009) showing overall decreasing transport cost especially during the first years of the millennium shift.

5. Conclusions Economic growth is considered to be the main factor behind the increase in freight transport work and its energy use. Recent research has quantified the relative contribution from underlying factors like value density of products, transport intensity and carbon intensity of fuel. However, thus far focus has not been on the dynamic interrelations between GDP, structural change, other underlying factors and final effects on CO2-emissions during specific eras of economic development. Focus has rather been on end-state figures and randomly chosen time-periods. The theory of growth cycles, emanating from heterodox economics, economic history and economic geography aims to explain the dynamic relations between GDP variations over long time and underlying economic behavior. Therefore, based on this research, we have aimed to find out whether this theory could contribute to the understanding of the interrelations and variations of GDP, freight transport work and CO2 emissions over long time. Focusing on the current growth cycle, its division into periods and gathered knowledge of the economic behavior during these periods, some hypotheses were established on relations between economic growth, structural change, other underlying factors and CO2-emissions. Swedish data was analyzed in a Shapley decomposition model. The hypotheses were quite eligible when confronted with the numerical findings. We found for instance expected differences between the growth cycle’s two periods concerning economic growth in relation to the amount of CO2 emissions. We also found expected differences in how macro-factors (GDP, value density, transport intensity) and micro-factors (modal split, traffic intensity, and emission factor) contributed to emissions in the periods. In summary our results suggest that the different and changing relations between growth and emission over the growth cycle indicate that the observed development in emissions is far from linear and cannot be explained straightforwardly by economic growth alone. It is not the growth rate per se that is most important but what propels growth and its effects on other macro and micro variables related to transport work and emissions. The impact of respective factors and the relation between them changes over time and results in different degree of decoupling. The general trend is that micro-oriented factors tend to be more important in the rationalization period which is consistent with the conceptual framework of growth cycles discussed above. We suggest that our approach might be useful not only for analyzing historical data in Sweden as well as in other countries, but also for construction and evaluation of medium-term and long-term scenarios for freight transport development and CO2emissions. It is very likely that for instance some countries turn out to be very early and strong in technology acceptance and transformation, whereas other countries are late in this respect but strong in rationalization. In spite of these national variations we expect to find similar tractable impacts on freight transport and emissions outside Sweden. This has to be put to a test. The theory of growth cycles can tell us reasonably well what will happen with economic growth, structural change and economic behavior up till around 2020. In combination with well-founded assumptions of technological progress in vehicle construction and energy consumption there will be a good chance predicting the level of CO2 emissions for that point of time, then knowing where we stand having the next cycle before us. For the years to come

Table A1 Data

Source

Comment

GDP Transported Tons

Statistics Sweden Swedish Institute for Transport and Communication analysis (SIKA) Swedish Institute for Transport and Communication analysis Swedish Institute for Transport and Communication analysis; Swedish Maritime Administration.

GDP in Real numbers

Ton kilometers Vehicle kilometers

CO2emissions from freight transport

Swedish Environmental Protection Agency

Road traffic data was gathered from the yearly transport survey. Rail data from operational reporting to the Rail Administration. Water traffic in domestic waters from model estimations made by the Maritime Administration and SIKA, with vehicle km estimates based on ton km/ ton. Data gathered from the Inventory Report for IPCC, allocated to different transport modes according to Swedish Energy Agency’s estimates in ‘‘Transportsektorns ¨ energianvandning’’, which were interpolated for missing years. Estimates based on oil-product use within Sweden.

after 2020 our knowledge of the current cycle will be of very little use. However, the theory might be used as an intellectual imperative teaching us to think deeper into what could be the possible outcome of quite another economic era, on freight transport work and emissions.

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