Effects on the manufacturing, utility and construction industries of decarbonization of the energy-intensive and natural resource-based industries

Sustainable Production and Consumption 21 (2020) 1–13

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Research article

Effects on the manufacturing, utility and construction industries of decarbonization of the energy-intensive and natural resource-based industries Fredrik N.G. Andersson Department of Economics, Lund University, Scheelevagen 15b, 220 07 Lund, Sweden

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Article history: Received 7 August 2019 Received in revised form 6 October 2019 Accepted 27 October 2019 Available online 30 October 2019 JEL classification: Q40 Q50 Q53 Q54 D22 Keywords: Value chain Carbon Climate Energy Innovation Economic effects

a b s t r a c t Decarbonizing the energy-intensive and natural resource-based industries is possible but may substantially increase the cost of production. Whether such cost increases will reduce economic welfare depends on how the downstream industries respond to the higher cost for intermediate goods. In this paper, we explore how downstream industries in the EU15 responded to upstream carbon technology shocks and prices shocks during the period 1998–2014. Our results show that downstream industries do not respond to technology shocks directly but that they do respond to price shocks. A 5 percent upstream price increase is followed by a 4 percent increase in capital investments, 3 percent increase in productivity and a 4 percent reduction in the carbon intensity among manufacturing industries. The utilities and construction industries respond primarily by increasing prices and reducing wages. Prices increase by approximately by 1 percent and real wages fall by approximately 2 percent following a five percent upstream price increase. © 2019 Institution of Chemical Engineers. Published by Elsevier B.V. All rights reserved.

1. Introduction Some of the most carbon-intensive industries are the mining, metal, mineral and plastic industries, sometimes referred to as energy-intensive and natural resource-based industries (ENRIs). They are responsible for roughly 20 percent of all industrial sectors’ emissions but account for only 8 percent of total output. Decarbonizing the ENRIs is technically possible, but it is likely to increase the cost of production substantially. According to some studies, production costs may increase by 50 percent or more (Palm et al., 2016; Åhman et al., 2017; Vogl et al., 2018). Unlike the manufacturing industries, decarbonization of the ENRIs has few direct economic co-benefits. Whereas the manufacturing industries can partly finance the higher production costs through innovation of new products with higher value added or expansion into new markets, ENRIs are more or less stuck producing similar products but at a higher cost (Åhman and Nilsson, 2015; Andersson and Nilsson, 2016; Åhman et al., 2017; Bataille et al., 2018; De Pee et al., 2018). Higher production costs are thus likely to be passed on throughout the value chain, leading to higher costs of materials elsewhere in the economy. Whether higher material E-mail address: [email protected].

costs will reduce economic welfare depends to a large degree on how the rest of the economy responds to the price shock. The ENRIs are primarily upstream industries producing materials that are used as intermediate goods by downstream industries, chiefly the manufacturing, utilities, and construction industries, which in turn produce for end-users (i.e. consumers, the government, etc.). How end-users are affected by the decarbonization of ENRIs thus depends on how the downstream industries respond to the higher costs of materials. Theoretically, the response is uncertain and depends on several factors (for a detailed discussion, see, e.g., Smale et al., 2006; Skelton and Allwood, 2013). For example, it depends on whether the upstream and downstream industries are technologically integrated, in which case changes in the upstream industries have a direct effect on the downstream industries’ production technology. It also depends on how much of the cost increase the ENRIs pass on to the downstream industries. The downstream industries can respond in several different ways to higher costs (see e.g. Perloff, 2006; Smale et al., 2006; De Bruyn et al., 2010). They can pass on the cost to their customers in form of higher prices or try to absorb the cost increase by cutting other production costs through, for example, reducing waste or lowering wages. Investing in new and more efficient capital to offset some of the

https://doi.org/10.1016/j.spc.2019.10.003 2352-5509/© 2019 Institution of Chemical Engineers. Published by Elsevier B.V. All rights reserved.

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F.N.G. Andersson / Sustainable Production and Consumption 21 (2020) 1–13

cost of higher materials is another possible response. They could also respond by innovating new products with higher value added that can carry higher production costs. These different responses have different effects on end-users and thus on economic welfare. Investments in new, more efficient capital or investments in productivity and new products bring with them economic benefits that may offset some of the higher cost of materials. Higher prices or lower wages, on the other hand, may amplify the negative effects by reducing living standards. Decarbonizing the ENRIs is necessary to reach the climate targets. Understanding how decarbonization affects the rest of the economy is clearly important for our understanding of the decarbonization effect on economic welfare. In this paper, we test empirically how downstream industries responded to upstream carbon technology shocks, and upstream price shocks, using EU15 data covering the period from 1998 to 2014. The purpose is to explore whether a future decarbonization of ENRI, which may lead to substantially higher production costs, will negatively affect the rest of the economy and in the end consumers. We focus on the aggregate macroeconomic effects and rely on sector-level data from the World Input–Output Database (WIOD). Downstream industries are divided into three industrial sectors—manufacturing, utilities and construction—to test whether the response differs among the three. In the modeling, we begin by testing whether the ENRIs and their downstream industries are technologically integrated. We then determine whether ENRIs’ price shocks have generated downstream changes to production methods, prices and costs. Based on our econometric results, we then simulate the potential response of downstream industries to a relatively large ENRI price shock using impulse–response figures to illustrate the potential effects of the decarbonization of the ENRIs, which lead to a substantial increase in the cost of production. The rest of the paper is organized as follows. In Section 2, we present the five hypotheses that we will test in our empirical modeling. The methodology and data are presented in Section 3, and the results are discussed in Section 4. Section 5 concludes the paper. 2. Upstream carbon technology and downstream firms: Five hypotheses The ENRIs are primarily upstream industries that produce materials, which are turned into products for end-use by the so-called downstream industries. The downstream industries are primarily manufacturing industries, the utilities industry and the construction industry. Because upstream and downstream industries are interrelated through the value chain, a technological shock or a price shock in one part of the value chain may affect industries in other parts of the chain. There are primarily two channels through which a shock can transition through the value chain. The first channel is a collaboration channel. Close collaborations across the value chain are not uncommon. For examples, industries collaborate in developing new technology and/or products (Vachon and Klassen, 2006). A technological change in one part of the chain is then likely to be followed by a similar change in another part of the chain (Adner and Kapoor, 2009). In other words, we should expect the decarbonization of an ENRI to go hand in hand with the decarbonization of its downstream industries if they co-operate on developing technology. Not all industries collaborate across the value chain. Nevertheless, a shock in one part of the chain may still affect other industries in the chain through the economic ties between the various industries. The second channel stresses this indirect economic channel. Here upstream industries affect downstream industries primarily through markets and not least through prices. When

the prices of intermediate goods change, downstream industries’ economic conditions change, causing them to respond. In terms of a price increase, the response can be to cut costs, cut waste, increase prices, or invest and innovate. The downstream industries’ choice among the possible responses has fundamentally different effects on end-users. Some of the responses, such as innovation, can have a positive welfare effect on end-users. Higher prices, on the other hand, would have a negative welfare effect. To test how downstream industries respond to the decarbonization of the ENRIs, we develop five testable hypotheses. The first hypothesis is based on the first direct channel. Several studies show that collaboration across the supply chain is relatively common (Harrison and New, 2002; Li et al., 2006; Ou et al., 2010). Firms co-operate throughout the supply chain to gain access to knowledge, skills and experience from other industries to which they otherwise would not have access (Bayona et al., 2001; Miotti and Sachwald, 2003). Collaboration may reduce the risk that is involved in all innovations (Tether, 2002; Belderbos et al., 2004; Naghavi and Ottaviano, 2010). For environmental innovation, the risks are often high, as the innovations tend to be more complex and carry few economic benefits (see, e.g., De Marchi, 2012; Green, 2012), in which case collaboration is essential to reduce the risk level (Andersen, 2002; Simpson et al., 2007; Purba and Holt, 2018). Co-operation can take the form of subcontracting, strategic alliances or joint ventures (McLoughlin, 1999; Håkansson and Waluszewski, 2002; Calia et al., 2007). Based on these arguments, our first hypothesis is: H1: The ENRIs and their downstream industries engage in technological collaborations. A change in the ENRIs’ carbon technology is followed by a change in the downstream carbon technology. Although collaborations can be beneficial, they are not always successful. Differences in expectations and communication difficulties across firms and industries are two factors that make collaboration difficult (Skippari et al., 2017). Moreover, firms engage in collaborations primarily when the expected gains are high (Menon and Menon, 1997; Bowen et al., 2001). For most environmental innovations, the expected gains are relatively small (Carter and Carter, 1998; Bowen et al., 2001), so we should not expect a high level of collaborations around green innovations. This does not rule out the possibility of the decarbonization of the ENRIs affecting the downstream industries. The decarbonization of ENRI can still affect them if it causes a change in the prices of materials (Bas and Causa, 2013). Empirical studies show that upstream material-producing firms often pass on a large share of their cost increases to their downstream customers (De Bruyn et al., 2010; Obendorfer et al., 2010). Expensive decarbonization leading to higher production costs is then followed by higher production costs for the downstream industries. This leads us to our second hypothesis: H2: Decarbonization of the ENRIs affects only downstream industries when the ENRIs increase their prices. How downstream industries respond to the higher cost of intermediate goods depends on a wide range of factors, for example the shape of the demand curve and thus firms’ ability to affect the prices that they charges their consumers (see, e.g., Benassy, 1991; Atkeson and Burstein, 2008; Nakamura and Zerom, 2010). Monopolistic competition is a common market form, not least among manufacturing firms. Under monopolistic competition, firms produce similar products; however, these products are differentiated by quality, brand and design.1 Under monopolistic competition, firms have some market power to set prices and make economic 1 De Loecker et al. (2017) find that firms have market power and that this power has increased over time. Polemis and Fotis (2015) also find that firms have some market power; however, they argue that it is relatively weak.

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profits, at least in the short run (Perloff, 2006). Firms are faced with the choice of either absorbing the cost increase or increasing their prices. Cost absorption can take the form of cutting, for example, labor costs through lower wages. Thus, our third and fourth hypotheses are: H3: Downstream industries respond to the higher material costs by increasing their prices. H4: Downstream industries respond to the higher material costs by cutting other production costs through, for example, reducing wages. In the long term, a firm has a greater ability to adjust its production though investments and innovation (Perloff, 2006). It can invest in new, more efficient capital and/or improved technology/productivity (Smale et al., 2006). More efficient capital reduces, for example, the use of materials and energy. New capital may also allow the firm to produce new products with a higher value that can carry a higher production cost. Innovation can have a similar effect to capital investments in allowing the firm either to produce new, higher value-added products or to reduce material waste/use. Empirical studies of the EU emission trading system (ETS), for example, confirm that an increased cost of emissions may lead to innovation in low-carbon-emission technologies to lower the use of fossil energy (for a review, see, e.g., Marcantonini et al., 2017). Higher material costs can have a similar effect. On the other hand, studies of the EU ETS also find that the effect appears to vary across industries. The literature argues that the degree to which a firm innovates is governed by two strong factors: competitive pressure and innovation ability. The higher the competitive pressure, the more the firm innovates (Porter, 1990; Galdon-Sanchez and Schmitz, 2002). The firm’s ability to innovate new products and invest in new capital are also important (Skelton and Allwood, 2013). Compared with utilities, manufacturing firms have greater scope to innovate new products and enter new markets (Åhman and Nilsson, 2015; Andersson, 2018), tending to produce a more standardized product with less international competition (Tang, 2006; Caldera, 2010). This leads us to our fifth and final hypothesis: H5: Downstream industries respond to higher material costs by investing in new and more efficient capital and by innovating to increase their economic profitability. The effect is strongest for manufacturing industries, in which competition is higher, and less strong for the utilities and construction industries, in which the competitive pressures are smaller. 3. Empirical analysis We test the five hypotheses using aggregate industry data from the EU15 covering the period from 1998 to 2014. Countries that joined the EU after 1995 are excluded from the analysis, because they were, at least for part of the period, transition economies and not full market economies.2 3.1. Econometric models We employ two related econometric methods to test the hypotheses. First, we perform a one-way Granger causality test to determine whether an upstream shock leads to a downstream response. Strictly speaking, the Granger causality test is not a causality test, because it cannot detect instantaneous causality; it only reveals whether a change in one variable is followed by a change in another variable in a later time period (for a detailed 2 Cyprus and Malta were market economies prior to joining the EU, but they are excluded due to their relatively narrow industrial base.

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discussion, see, e.g., Granger, 1969). In our case, this limitation is not of great concern, as instantaneous innovation in both industries is unlikely. It is more reasonable to assume that it takes time for downstream industries to respond. Another potential problem with the test is that it may yield spurious results if both variables respond to a common shock and the responses to the shock occur slowly over several years. This problem is commonly solved by including additional control variables to control for such shocks. To this end, we include several macroeconomic control variables to capture aggregate changes in economic conditions, such as the demand and the real oil price, to control for changes in the price of fossil fuels. Because we have a panel data model, we can use the panel data structure and include fixed effects for unobserved common time shocks, further reducing the potential problem with common shocks. The Granger causality test provides a simple overview of the relationship between the ENRIs and the downstream industries, but it does not reveal the sign, size or timing of any potential effects. Therefore, in the second step, we estimate impulse– response functions using a local projection method, which will quantify and time any potential effects (see, e.g., Da Rocha and Solomou, 2015; Romer and Romer, 2017). To model the effect of the decarbonization of the ENRIs, we need two variables: a measure of ENRIs’ carbon technology and the prices of materials. We let the carbon intensity, that is, the carbon dioxide emissions per unit of output (CO2 /output), represent an aggregate proxy for carbon technology. It is an inverse measure of technology in the sense that more advanced carbon technologies are represented by a lower carbon intensity. Prices are measured using the industrial price deflator for the ENRIs. A limitation of the price deflator is that it captures all price changes and not just those price changes that are due to a change in the carbon technology. However, it is unlikely that downstream industries should respond differently to a price shock depending on whether the prices have changed due to new technology or for some other reason. Therefore, using a general price deflator should not affect our conclusion that an upstream cost shock affects downstream industries. Nevertheless, as a robustness analysis, we estimate two sets of models. In the first set, we include the price deflator and model the effect of general price changes. In the second set, we replace the price deflator with an interaction variable between the price deflator and the carbon technology to isolate a price change related to a change in the carbon technology. We expect the results from the two sets to be similar. Six different dependent variables are used to test the hypotheses: (i) downstream industries’ carbon technology (i.e., carbon intensity) is used to test Hypothesis 1; (ii) downstream industries’ relative prices are used to test Hypothesis 3; (iii) downstream industries’ real wages are used to test Hypothesis 4; (iv) downstream industries’ employment is also used to test Hypothesis 4; (v) downstream industries’ real capital investments are used to test Hypothesis 5; and vi) downstream industries’ total factor productivity (TFP) is used to test Hypothesis 5. According to H2, we expect a significant result only when there is an upstream price shock and we reject H1. All the variables are transformed into growth rates by taking their log and first difference (i.e. zt = ln(Zt ) − ln(Zt −1 )) to ensure that the data are stationary and that traditional statistical tests can be applied.3 For the first set of Granger causality tests, 3 A potential concern due to taking the first difference of the data is that we econometrically focus the analysis on the short-term fluctuations in the data and not the long-run trends. However, our data sample is too short to explore the long-run effects fully, and the loss of not being able to model the long run is small compared with the gain of being able to apply standard statistical tests to the hypotheses.

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F.N.G. Andersson / Sustainable Production and Consumption 21 (2020) 1–13

in which we model the effect of carbon technological innovations and general price changes, we estimate the following test regression: ydit =

6 ∑

βyl ydit −l +

l=1

6 ∑

βel eudit −l +

l=1

6 ∑

βpl pudit −l

l=1

+ βx xit + fdt + fi + fd + edit

(1)

where y is one of the six dependent variables, d denotes a downstream industry, u denotes an upstream industry (i.e., the ENRIs), i denotes the country, t denotes the time and l is the lag length. We lag all the explanatory variables up to six years. The exact lag length is determined by an information criterion. Carbon technology is denoted by e. According to H1, we expect downstream firms to respond to changes in upstream e. However, according to H2, downstream firms only respond to upstream e shocks if the prices change. To test the hypothesis, we include the upstream relative price, p, which is defined as the upstream deflator normalized by the GDP deflator. According to H1, we expect βel ̸ = 0 and βpl = 0. According to H2, we expect βel ̸ = 0 and βpl ̸ = 0. The vector x contains the macroeconomic control variables, which are used to control for common shocks. They include: the GDP growth to control for aggregate demand shocks, the log change in the real exchange rate to control for variations in foreign demand, and the log change in the real oil price to control for changes in the cost of fossil fuels. By using panel data, we can also include a set of fixed effects to control for unobserved heterogeneity and common time shocks. Our model includes fixed effects for industry fd , country fi and time fdt . The latter is allowed to vary across industries. The fixed effects for industry and country control for systematic differences across industry and country that are stable over time. The fixed time effects control for common shocks affecting all countries and industries equally, such as a global business cycle and/or the global financial crisis of 2008/09. In the second set of Granger causality tests, we replace the deflator with an interaction effect between the carbon technology and the deflator (p×e) to isolate price changes that are correlated with changes in ENRIs’ carbon technology. The second test regression is given by 6

ydit =

∑ l=1

6

βyl ydit −l +

∑

6

βel eudit −l +

l=1

+ βx xit + fdt + fi + fd + edit

∑

βepl eudit −l × pudit −l

l=1

(2)

In both (1) and (2) we model the general effect of a decarbonization of ENRI. We do not separate between different causes of the decarbonization such as a policy intervention or voluntary actions taken to reduce energy consumption to enhance competitiveness levels. During the period a major policy change is the introduction of the EU Emissions Trading System (ETS). Studies such as Laing et al. (2013) and Martin et al. (2016) have found that the ETS did have a significant impact on carbon emissions in the covered industries, whereby a relatively important part of the reduction in the carbon intensity is likely to be policy driven.45 Studies also show that upstream industries pass on the cost increase to downstream industries suggesting that part of 4 The correlation between changes in the carbon intensity and the OECD’s environmental policy stringency index Botta and Kozluk (2014) is between 0.3 and 0.5 depending on industry indicating that policy is an important driver behind changes in the carbon intensity but not the only one. 5 We can observe a trend change in the carbon intensity in our data around the time of the introduction of the ETS supporting similar to previous studies.

any relative price change is also due to the ETS (Alexeeva-Talebi, 2011; Joltreau and Sommerfeld, 2019). Unfortunately, our data does not allow us to specifically separate between policy induced improvements in carbon technology from other causes and we assume that the effect of an upstream carbon innovation is the same irrespective of the cause. Our two models are dynamic, as lagged values of the dependent variable are included on the right-hand side. In a panel data setting, an OLS may provide biased parameter estimates. The bias is mostly a problem for the estimates of the dynamic effects and the constant, and it is less of a problem when it comes to the other parameters. Thus, in our case, any potential bias is of less concern. Nevertheless, we estimate (1) and (2) using a system GMM estimator to avoid obtaining biased parameter estimates (see Blundell and Bond, 1998).6 We begin the analysis by pooling all the downstream and upstream industries, that is, assuming (i) that the upstream industry that the shocks come from has no bearing on how the downstream industry responds and (ii) that all the downstream industries respond in the same way. These two assumptions are relaxed later by allowing the response to differ depending on whether the analysis considers manufacturing, utilities or construction. The size, sign and timing of the effects are estimated using impulse–response functions from a local project method. We assume that a shock occurs in year zero. We then track the downstream response for up to six years after the shock occurred. To obtain the impulse responses, we first estimate the following model: ydit +h − ydit −1 = αdi +β1h euit +β2h puit +β4h ydit −1 +ρ xit + fdt + fi +εdit (3) The parameter β1h tells us the response of the dependent variable h periods into the future following a change in the upstream carbon technology in period t. The effect from upstream industries’ relative price is given by β2h . In the second set of estimations, when we study the effect of a carbon technology price shock, we use the following model specification: ydit +h − ydit −1 = αdi + β1h euit + β3h euit × puit

+ β4h ydit −1 + ρ xit + fdt + fi + εdit

(4)

where the carbon technology price effect is given by the parameter β3h . Impulse–response figures are generated by assuming a certain improvement in e coupled with a certain price change p. The parameters illustrate by how much the dependent variable responds to the shock in each given year after the shock occurred. The estimated responses are plotted in a series of figures together with the estimated confidence bounds. 3.2. Data Industry-level data are collected from the WIOD, an EU-funded project to construct harmonized input–output data across all EU countries and 12 other major economies, such as the United States and China. The WIOD divides the economy into 35 industrial sectors, from agriculture to manufacturing and private and public services. In the database, there are 5 energy-intensive 6 The system GMM estimator is designed for panels with a relatively large number of cross-sections and few time periods, thus corresponding to our panel, which consists of several industries from 15 different countries. Alternative estimation methods are Dumitrescu and Hurlin’s (2012) method and biascorrected OLS (see, e.g., Kiviet, 1995). However, these methods are designed for panels with a relatively large number of time observations, which we do not have.

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natural-resource based industries that we define as representing the upstream ENRIs: (i) mining and quarrying; (ii) the manufacture of paper and paper products; (iii) rubber and plastic; the (iv) manufacture of non-metallic mineral products; and (v) the manufacture of basic metals and fabricated metal products. Between 85 and 92 percent of their output are used as intermediate goods by 6 downstream industries. Of these industries, 4 are manufacturing industries (the manufacture of computers and electrical products, manufacture of machinery and equipment, manufacture of transport equipment and manufacture of furniture and other manufacturing). The other two industries are the utilities industry and the construction industry. A detailed description, including Rev4 codes, is shown in Table A.1 in Appendix. The WIOD contains detailed information about the value chain and individual data for each industrial sector. The only variable missing for our modeling purposes is productivity. To obtain a measure of TFP, we estimate, for each downstream industry, a Cobb–Douglas production function with constant returns to scale, and we let the residual from that regression represent TFP. This measure of TFP represents increases in the real economic value that are not caused by greater use of either capital or labor. Such an increase can come about either through more efficient use of capital and labor or from a change in production from products with a relatively low value to products with a relatively higher value. In the estimation, we allow the capital/labor elasticities to vary across industries, but they are assumed to be the same across all EU15 countries.7 A limitation of the WIOD is that data have been released in different waves. Each wave covers slightly different years, and not all variables are included in each release. The data on CO2 emissions, which we use to estimate the carbon intensity, come from the 2013 wave and cover the years 1998–2009. Most economic indicators, such as capital and labor, come from the 2016 release and cover the period 2000–2014. The only indicators that are available for the full period are output, value added and prices. In the empirical modeling, we include as many data as possible in each estimation. Thus, the years that are included in the analysis depend on the number of time lags that we include in the respective estimations.8 4. Econometric results 4.1. Granger causality tests We begin with the results from the Granger causality tests. The results are shown in two tables: Table 1 (general price increase) and Table 2 (carbon technology price increase). The top half shows the results from the test of whether a change in the upstream carbon technology affects the downstream industries, that is, H1. The bottom half of the tables shows the results from the test of whether an upstream price change affects the downstream industries, that is, H2, and how they respond, that is, H3 to H5. The tables report the p-values from the tests, and all the p-values of less than 5 percent are highlighted in bold. First, we consider the results for a general price change, namely the results from test equation (1). We find no support for our first hypothesis that changes in ENRIs’ carbon technology affect downstream industries’ carbon technology. The support for Hypothesis 2 is stronger. Upstream price shocks lead to a 7 The respective capital/labor elasticities are estimated using a regression model with the real value added as the dependent variable and capital and labor as the explanatory variables. 8 As a robustness check, we restrict the data sample to cover only the overlapping years 2000–2009, with no major effect on the results. However, reducing the number of observations reduces the significance levels, so we try to include as many data as possible in the regressions.

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downstream response. The response differs for the manufacturing, utility and construction industries. For the utilities and construction industries, there is a significant effect on real wages. For the manufacturing industries, these is a significant result of change for real capital investments and TFP. All the industries record a change in prices (the effect on utilities is not quite significant at the 5 percent significance level). For manufacturing and construction, there is also a significant effect on carbon technology. Next, we turn to the results for a carbon technology price change, specifically the results from test equation (2) with the interaction variable between the carbon technology and the price deflator. Unlike our previous results, we now find two cases in which upstream carbon technology has a direct effect on downstream industries. However, the effect is not on downstream industries’ carbon technology but on capital investments and prices. The effect is statistically weak, and there is no firm evidence of the upstream carbon technology having any direct impact on the downstream industries. As before, the support for H2 is stronger. Upstream price shocks trigger a downstream response. Manufacturing firms respond by changing their investments in real capital, whereas utilities and construction firms are more likely to change their wages and prices. So far, we have assumed that all the upstream industries generate the same downstream response. Next, we allow each upstream industry to generate a different downstream response. These results are summarized in Table 3 (general price change) and Table 4 (carbon technology price change). Here, we limit the effect of price shocks, as we find no effect of carbon technology integration. There are some variations in the results. The mineral industry generates the largest downstream response, followed by the mining and paper industries. These results are probably explained by the fact that 44–61 percent of the intermediate goods that downstream industries buy from the five ENRIs come from the mineral industry, followed by the mining and paper industries. In other words, the variations in results, at least in part, reflect the economic importance of the respective upstream industries. The more the downstream industry purchases from them, the greater their impact, as expected. In summation, we find support for the ENRIs affecting the downstream industries through prices, but there is no evidence of carbon technological integration/collaboration. The response differs depending on whether we are studying manufacturing, utilities or construction. Manufacturing industries are more likely to invest and innovate, while the utilities and construction industries are more likely to adjust their prices and wages. The results are largely unaffected by whether we study a general price increase or a carbon price increase. This result indicate that the source of the price change is unimportant. The important factors is that prices that thus costs have changed. 4.2. Impulse responses We now turn to the impulse–response analysis. To generate impulse–response figures, we must first assume that there is a carbon technology shock and a price shock. We can then track how those shocks affect the downstream industries using the estimated results from Eqs. (3) and (4). The size of the assumed shocks will affect the estimated size of the effects. We are primarily interested in the sign and the timing of the effects, whereby the exact size of the shock is of less importance. We can choose any size of the shock as long as it is within the range observed in the data on which the model is estimated. The accuracy of the estimated response is reduced when too large shocks that

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F.N.G. Andersson / Sustainable Production and Consumption 21 (2020) 1–13 Table 1 Granger causality test, general price change (p-values). Pooled upstream industries. Carbon technology

Relative prices

Real wages

Employment

Real capital investments

TFP

.342 .056 .264 .296

.233 .071 .628 .310

.093 .542 .511 .593

.237 .120 .122 .431

.023 .309 .000 .007

.687 .990 .044 .119

.000 .000 .372 .284

.020 .018 .452 .582

Employment

Real capital investments

TFP

.104 .144 .293 .829

.131 .213 .130 .457

.016 .220 .638 .947

.126 .221 .236 .661

.010 .005 .015 .829

.148 .106 .050 .551

.031 .010 .584 .984

.063 .133 .411 .471

No direct carbon technology effect βe = 0 All downstream industries Manufacturing industries Utilities Construction

.249 .283 .083 .897

.369 .284 .359 .061

No direct price effect βP = 0 All downstream industries Manufacturing industries Utilities Construction

.091 .019 .083 .000

.000 .000 .053 .000

Note: p-values of less than 5 percent are highlighted in bold. The results are based on test Eq. (1). Table 2 Granger causality test, carbon technology price effect (p-values). Pooled upstream industries. Carbon technology

Relative prices

Real wages

No direct carbon technology effect βe = 0 All downstream industries Manufacturing industries Utilities Construction

.273 .270 .435 .056

.465 .016 .872 .080

No price effect βCP = 0 All downstream industries Manufacturing industries Utilities Construction

.352 .140 .502 .000

.000 .000 .000 .081

Note: p-values of less than 5 percent are highlighted in bold. The carbon price effect is estimated indirectly as the interaction between carbon technology and prices. The test results are based on test Eq. (2). Table 3 Granger causality test, general price change (p-values). Individual upstream industries. Carbon technology

Relative prices

Real wages

Employment

Real capital investments

TFP

.007 .053 .002

.014 .513 .538

.010 .318 .611

.018 .298 .141

.545 .219 .226

.232 .526 .052

.002 .192 .551

.067 .877 .078

.077 .049 .000

.390 .042 .044

.005 .100 .004

.230 .485 .265

.843 .013 .032

.037 .076 .181

.000 .647 .457

.046 .288 .990

.059 .162 .037

.174 .162 .000

.022 .520 .719

.169 .444 .864

Upstream industry: mining Manufacturing industries Utilities Construction

.002 .097 .000

.003 .023 .001

Upstream industry: metals Manufacturing industries Utilities Construction

.018 .875 .127

.000 .718 .094

Upstream industry: minerals Manufacturing industries Utilities Construction

.021 .026 .032

.000 .000 .030

Upstream industry: paper Manufacturing industries Utilities Construction

.004 .234 .000

.000 .002 .007

Upstream industry: plastics Manufacturing industries Utilities Construction

.199 .068 .002

.000 .012 .000

Note: p-values of less than 5 percent are highlighted in bold. The test results are based on test Eq. (1).

fall outside the range observed in the data are applied.9 Consequently, we choose shocks that are within the observed range but at the upper limit of the distribution to represent a relatively large shock. We assume that there is a carbon technology shock of 5 percent (i.e. a 5 percent reduction in the carbon intensity) and 9 See, for example, Lucas Jr. (1976) for a discussion.

that the price deflator increases by 5 percent. These shocks are relatively large as judged from the historical outcome illustrated in Table 5. The maximum relative price increase in our sample is between 14 percent (plastics) and 44 percent (mining). The ninetieth percentile is close to 5 percent (between 2.7 and 11.6 percent depending on the upstream industry). The carbon technology shock is close to the historical average. The assumed shocks are smaller than the cost increase of 50 percent or more

F.N.G. Andersson / Sustainable Production and Consumption 21 (2020) 1–13

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Table 4 Granger causality test, carbon technology price change (p-values). Individual upstream industries. Carbon technology

Relative prices

Real wages

Employment

Real capital investments

TFP

.043 .234 .979

.946 .125 .167

.000 .732 .797

.018 .483 .775

.304 .688 .449

.608 .511 .374

.007 .537 .925

.074 .248 .799

.006 .349 .643

.281 .062 .005

.000 .393 .014

.002 .668 .522

.000 .008 .010

.399 .305 .331

.653 .215 .754

.021 .467 .762

.073 .216 .000

.775 .059 .016

.015 .787 .054

.626 .322 .945

Upstream industry: mining Manufacturing industries Utilities Construction

.012 .406 .000

.270 .095 .114

Upstream industry: metals Manufacturing industries Utilities Construction

.708 .167 .328

.052 .377 .266

Upstream industry: minerals Manufacturing industries Utilities Construction

.000 .150 .000

.000 .002 .000

Upstream industry: paper Manufacturing industries Utilities Construction

.010 .427 .050

.055 .495 .000

Upstream industry: plastics Manufacturing industries Utilities Construction

.000 .254 .069

.872 .321 .036

Note: p-values of less than 5 percent are highlighted in bold. The carbon technology price is estimated indirectly as the interaction between carbon technology and prices. The test results are based on test Eq. (2). Table 5 Historical relative price changes and changes in carbon intensity (percentages). Relative prices

Mining Metals Minerals Paper Plastic

Carbon emissions per unit of output

Mean

Standard deviation

Max.

P90

Mean

Standard deviation

1.2 −0.4 −0.8 −1.3 −0.8

9.7 6.0 4.4 4.3 4.2

44.0 24.5 30.5 24.0 14.0

11.6 6.0 2.7 3.1 3.1

−4.9 −3.5 −6.5

12.5 10.8 8.1 19.5 4.0

estimated by some studies. However, we are modeling year-onyear changes in prices and technology, and it is unlikely that the cost will increase by 50 percent in one year. It is more likely that the full shock will manifest over a set of years. We begin with impulse responses for a general price shock (see Fig. 1) followed by a carbon technology price shock (see Fig. 2). These figures show the average response of all the downstream industries (i.e., pooled results). We focus specifically on manufacturing industries in Fig. 3 and Fig. 4. The estimated confidence bounds for the utilities and construction industries are wide, and all the yearly effects fall within the bounds, indicating that there is uncertainty regarding the yearly effects. The indication of the Granger causality analysis is that these two downstream industries are also affected by a price shock from the ENRIs but that the year-on-year dynamics are uncertain. We thus exclude these figures from the paper. As a complement to the yearly effects, we estimate the cumulative effect over the six years (see Table 6). Here, we also include the utilities and construction industries. Judging from the figures, time has a clear effect on the results. Following the shock of downstream industries’ investments in real capital during years 1 to 3 (Fig. 1a), the increase in capital is followed by an increase in TFP (Fig. 1b), mainly in years 3 to 5. The relative prices (Fig. 1c) and real wages (Fig. 1d) both increase during years 4 and 5. The carbon intensity (i.e., the inverse of carbon technology) (Fig. 1e) declines in period 4, whereas there is no significant change in employment (Fig. 1f), indicating that the output increases and production become more capital intensive as a result.

0.5 −4.0

Fig. 1a. Response of real capital investments to a general price increase.

New capital is followed by lower emissions per unit of output and higher TFP, which, in turn, are followed by higher prices and increased wages. In other words, the newly installed capital is more efficient in terms of both carbon and productivity. Greater productivity leads, in turn, to higher real wages and is possibly also linked to the higher prices that the firm charges. Given that the price increase first occurs after four years, it is unlikely that it is related directly to the increased cost of production coming from the upstream price shock. Instead, the effect of new products with a higher value added (i.e. the higher TFP effect) is more likely.

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F.N.G. Andersson / Sustainable Production and Consumption 21 (2020) 1–13

Fig. 1b. Response of TFP to a general price increase.

Fig. 1f. Response of employment to a general price increase.

Fig. 1c. Response of relative prices to a general price increase.

Fig. 2a. Response of real capital investments to a carbon price increase.

Fig. 1d. Response of real wages to a general price increase.

Fig. 1e. Response of carbon intensity to a general price increase.

The impulse responses for a carbon technology price shock are shown in Fig. 2. The estimated effects follow a similar pattern to the previous results for a general price change, but the effects are smaller and the confidence bounds wider. One major difference is that real capital investments (Fig. 2a) are statistically insignificant in these estimations, but the estimated effect follows a pattern similar to that seen in the previous estimations. The TFP (Fig. 2b) and relative prices (Fig. 2c) show a somewhat stronger effect statistically. For relative wages (Fig. 2d), we also note that there is a statistically negative effect on real wages in year 2, before they recover in years 4 and 5. We found a similar tendency in our previous results, but the effect was not statistically significant. The carbon intensity (Fig. 2e) declines in year 4, as the TFP begins to increase. For employment, there is no effect (Fig. 2d). In summation, there are differences from the first set of impulse responses, but the tendency is the same. There are higher investments in real capital initially, followed by improved carbon technology and greater economic value reflected in a higher TFP and higher real wages. Turning to manufacturing industries specifically (see Fig. 3 [general price shock] and 4 [carbon technology price shock]), we find that most of the pooled results are driven by the four manufacturing industries, which is unsurprising, given their relative size. The pattern from the pooled impulse responses are maintained, with an initial response in capital investments followed by a higher TFP and an increase in prices. The carbon technology also improves as the new capital becomes operational and the TFP increases. The relative prices increase following the improved TFP in years 3 to 5. The real wages are initially reduced, but they then recover as the TFP increases. These results hold irrespective of whether we use the price changes or the interaction variable between carbon technology and prices.

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Fig. 2b. Response of TFP to a carbon price increase.

Fig. 2f. Response of employment to a carbon price increase.

Fig. 2c. Response of relative prices to a carbon price increase.

Fig. 3a. Response of real capital investments in the manufacturing industries to a general price increase.

Fig. 2d. Response of real wages to a carbon price increase. Fig. 3b. Response of TFP in the manufacturing industries to a general price increase.

Fig. 2e. Response of carbon intensity to a carbon price increase.

The cumulative effect over the six years is shown in Table 6. It is estimated by summing the estimated yearly effects. The pooled result shows a cumulative increase in capital investments of 3.6 percent, while the TFP increases by 2.4 percent, the prices increase by 1.9 percent and the carbon technology improves such that the carbon intensity is reduced by 3.7 percent. The changes in employment and real wages are small. Though these estimates may appear large, a 5 percent relative price increase is a large price increase by historical standards, which may explain the relatively strong response from the downstream industries. Turning to the respective industries, most of the cumulative results are driven by manufacturing industries. The utilities

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F.N.G. Andersson / Sustainable Production and Consumption 21 (2020) 1–13 Table 6 Estimated cumulative effect of a 5 percent general price increase. Capital

Capital

TFP

Relative price

Real wage

Carbon technology

+1.9 +1.8 +1.0 +1.1

+0.3 +0.2 −2.8 −1.5

−3.7 −4.3 +0.2 −1.1

Employment

General price increase of 5% All Manufacturing Utilities Construction

+3.6 +4.0 +0.2 0.0

+2.4 +3.2 −0.3 +0.3

0.0

−0.3 −1.6 +0.2

Price increase of 5% coupled with a 5% reduction in carbon intensity All Manufacturing Utilities Construction

0.9

+1.4 +0.2 −0.1

+1.6 +0.7 −0.3 0.0

+2.4 +2.5 +1.3 +2.0

Fig. 3c. Response of relative prices in the manufacturing industries to a general price increase.

Fig. 3d. Response of real wages in the manufacturing industries to a general price increase.

Fig. 3e. Response of carbon intensity in the manufacturing industries to a general price increase.

−0.7 −1.3 −1.4 −0.1

−1.2 −3.9 −0.5 −0.3

+0.1 +0.2 −1.2 +0.3

Fig. 3f. Response of employment in the manufacturing industries to a general price increase.

and the construction industry respond primarily with price increases and real wage reductions. There is also a reduction in employment among utilities. The manufacturing industries increase prices by more than utilities and the construction industry, which is an indication that part of the price increase is due to the higher TFP (i.e., new products with a higher value added). An increase in price due to a new higher value-added product does not necessarily reduce welfare, as the price increase is motivated by a new and better product. At least part of the increase in manufacturing prices does not reduce welfare for end-users. The results for the carbon technology price shock are similar to the results for the general price shock. The main difference is that the magnitude of the estimated effect is different: the price increases are larger and the capital and TFP effects are smaller. In addition, the estimated carbon technology effect on the downstream industries is smaller than in the previous results. The size of the effect of the carbon technology price change is, of course, dependent on the assumed level of the price increase and the assumed reduction in the carbon intensity. In summation, the impulse–response analysis adds more details to the Granger causality tests by including a time dimension in the analysis and showing the size of the effects. The timing of the manufacturing industries’ response indicates that the immediate response, to invest in new real capital, increases the economic productivity and reduces the carbon intensity. Higher productivity, in turn, allows a firm to increase its prices, most likely because part of the productivity increase comes from new products for which consumers are willing to pay more. Thus, this price increase does not reduce economic welfare, as it is motivated by a product with a higher value.

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Fig. 4a. Response of real capital investments in the manufacturing industries to a carbon price increase.

Fig. 4d. Response of real wages in the manufacturing industries to a carbon price increase.

Fig. 4b. Response of TFP in the manufacturing industries to a carbon price increase.

Fig. 4e. Response of carbon intensity in the manufacturing industries to a carbon price increase.

Fig. 4c. Response of relative prices in the manufacturing industries to a carbon price increase.

Fig. 4f. Response of employment in the manufacturing industries to a carbon price increase.

5. Conclusions

irrespective of whether they were caused by policy interventions such as the EU ETS, or voluntary actions by the industries to improve their energy efficiency and competitiveness levels. We find no evidence of carbon technology integration across the value chain. Upstream technological change has no effect on the downstream industries, except when prices change, in which case the downstream industries respond. The fact that downstream firms respond to price signals indicate that the source of the price change is unimportant, i.e. whether the upstream firms decarbonization was caused by voluntary actions or policy interventions. Specifically we find that manufacturing firms respond

Transitioning energy-intensive and natural resource-based industries (ENRIs) from fossil-based to fossil-free production methods is a technological, economic and political challenge. Although it is technically possible, the productions costs are likely to increase, potentially decreasing economic welfare. The sizes of any potential macroeconomic effects depend on how the downstream industries respond to the decarbonization of the ENRIs. Using historical data from the EU15 for the period 1998–2014, we test the historical response to changes in the upstream carbon technology. We study the effect of all changes in carbon technology

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F.N.G. Andersson / Sustainable Production and Consumption 21 (2020) 1–13 Table A.1 Industry description. Industry description

Rev.4 code

Upstream industries Mining and quarrying Manufacture of paper and paper products Rubber and plastic Manufacture of non-metallic mineral products Manufacture of basic metals and fabricated metal products

B C17 C22 C23 C24, C25

Downstream industries Manufacture of computer, electronic, electrical and optical products Manufacture of machinery and equipment n.e.c. Manufacture of motor vehicles, trailers, semi-trailers and other transport equipment Manufacturing of furniture, other manufacturing. Repair and installation of machinery equipment Electricity, gas, stream and air conditioning supply. Water collection, treatment and supply. Construction

by investing in new capital, which increases their economic productivity and reduces the carbon intensity. However, our results also indicate that the output levels increases and the net effect on emission levels are close to zero. In other words, economic activity increases while emission levels remain constant. Part of the productivity increase in the manufacturing sector is likely to be caused by innovation in new products with higher value added, which potentially explains the price increase that follows the increase in economic productivity. On the other hand, utilities and construction firms respond mainly by increasing their prices and reducing their real wages, and we find no support for higher investment or productivity improvements. There are three potential factors that may explain the differences in the way in which the manufacturing industries respond compared with the utilities and construction industries. First, we are studying a relatively short time period. In particular, utilities tend to have relatively long investment cycles compared with manufacturing firms. It is possible that utilities invest in new capital and enhance their productivity, but the effect first occurs several years into the future. Second, the market conditions, not least competitive pressures, are likely to have an effect. Higher levels of competition in the manufacturing sector prevent manufacturing firms from pushing on the cost increase to their consumers. Instead, the firms innovate and invest to make themselves even more productive when the material costs increase. Utilities and construction firms commonly face less competition and can, to a higher degree, pass on the cost increase to their consumers without losing customers. The third potential factor is the ability to innovate. The products of utilities, and to some extent construction firms, are more standardized than those of manufacturing firms, and the scope for either innovating or updating the product to increase its value is more limited for utilities and construction firms than for manufacturing firms. The overall conclusion from our study is that the decarbonization of the ENRIs is unlikely to have any major economic welfare effects on end-users. Technological change without any change in production costs will not affect the macroeconomic welfare. If the cost of materials increases, welfare is reduced, but some of the negative effects are offset by the downstream manufacturing industries’ ability to innovate. In other words, one should not exaggerate the negative welfare effects of the decarbonization of ENRIs, which increases the cost of materials in the economy. Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

C26, C27 C28 C29, C30 C31, C33 D35, E36 F

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