Energy performance and capital expenditures in manufacturing industries

Little is known about how firms change energy consumption over time. Yet, to meet global climate change targets, understanding how changes in firm investment impact environmental performance is important for policymakers and firms alike. To investigate the environmental performance of firms, we measure the energy consumption and efficiency of firms in the Netherlands’ manufacturing industries before and after large capital expenditures over the 2000 to 2008 period. Unique to this data set is that firm investment is decomposed into the following three streams: investment in buildings only, investment in equipment only, or a simultaneous investment in both buildings and equipment.We find that firms increase energy consumption when experiencing a simultaneous investment. However, after large capital expenditures, energy efficiency increases. Further decomposition by firm types suggests that the building capital investments of firms active in high-tech, energy-intensive, and low laborintensive industries do not coincide with energy efficiency improvements while energy efficiency does increase with capital expenditures in equipment. From a policy perspective, it is important for regulators to understand firm investment and production processes, which help regulators understand when and where energy efficiency increases are feasible across firm types and expansionary production strategies. Firms, regulators, and other third parties may work together to develop an energy efficiency plan in line with investment strategies, including enhanced transparency by firms, energy efficiency subsidies, and R&D tax credits, for innovation. Targeted agreements may work to cooperatively improve energy performance.


Introduction
Firm output in the industrial sector is very energy intensive. In the US, industrial sector energy consumption in 2010 was 625 million tons of oil equivalent, or one third of total consumption (US Energy Information Administration, 2014). Energy consumption in the EU is approximately 320 million tons of oil equivalent (European Environment Agency, 2014). Policy makers have sought to change this energy intensity by incentivizing capital investments towards energy efficiency through stricter building codes and more stringent equipment standards in the industrial sector (Acemoglu et al., 2014;Acemoglu et al., 2009). Early results from Aroonruengsawat et al. (2012) and Jacobsen and Kotchen (2013) document that improvements in residential building codes -that impact new and redevelopment construction -decrease energy consumption by four to six percent. Papineau (2013) has shown similar results for commercial buildings. These studies suggest that stimulating capital investments embodying recent general technical change in energy efficiency for industrial production and construction are ways in which policy could aim to attain environmental goals.
Firm environmental performance is often measured in the literature and by policy makers by looking at the output of firms across various toxicity-weighted pollutants -carbon, nitrate, sulfate oxides. Overall, studies find that firms are decreasing their pollution over time (Cole et al., 2005;Earnhart, 2004), but there are some sectors in which firms are not achieving environmental goals (Gamper-Rabindran and Finger, 2013). However, we do not know the sources of environmental performance surrounding firm investment activities: is it equipment, the buildings or some combination of both? In addition, the literature on energy performance in buildings has documented evidence of increased property value and decreased energy consumption after new building development, but importantly these findings are divided from research investigating capital expenditures at the firm level (Eicholtz et al., 2010;Dalton and Fuerst, 2017). By taking a firmlevel view of environmental performance that includes buildings and equipment as production factors we are identifying firm-level challenges and capital expenditure areas for technological progress in meeting environmental performance benchmarks.
To understand firm-level environmental performance we employ a multi-period event study to measure the following outcomes. First, we investigate how the energy performance of firms active in the energy intensive industrial sector develops around episodes of unusually large capital investments -so called investment spikes (Power, 1998). Specifically, we compare the energy consumption and energy efficiency of firms before, during, and after investment spikes in buildings or equipment, or investment spikes in both capital types simultaneously. As such, the analysis allows us to assess whether and how upgrading the capital stock of industrial firms contributes towards attaining environmental goals concerning energy use and thus the emission of greenhouse gasses. Second, we assess how firms' operational efficiency develops around these same capital investment spikes. By comparing the simultaneous development of firms' energy performance and of their operational efficiency surrounding capital investment spikes, we investigate if environmetal goals conflict with -or rather complement -competitive goals. As such, we add to literature adressing whether firms are able to combine "lean and green" production techniques (e.g., King and Lenox, 2001;Klassen and Wybark, 1999;Telle and Larsson, 2007).
We begin our analysis by developing a theoretical framework to guide our empirical analysis, which looks at firm investment spikes across time periods and the arbitrary timing in investment spike event patterns across firms to guide our empirical analysis of firm capital expenditures and energy performance. The framework also enables us to understand how energy efficiency is related to productivity of capital, price developments and a change in production technology. To operationalize this model, we employ a micro-level panel dataset provided by Dutch Statistics (CBS) covering the period 2000-2008. The dataset contains information concerning investment in buildings and equipment. In addition, it provides data on production statistics and the energy expenditures of firms. By identifying large capital expenditures we pinpoint major events at the firm level (Power, 1998). Importantly, we distinguish between expenditures in buildings and equipment. Furthermore, we allow for single expenditure events where the firm only adjusts one type of capital, and we allow for a simultaneous expenditure where the firm makes a large investment in both buildings and equipment. The purpose of discriminating between single and simultaneous expenditures is that the latter are more likely to reflect expansionary efforts of the firm, whereas single expenditures more likely refer to replacement of depreciated physical capital.
Our identification scheme makes it possible to measure how energy performance of the firm behaves surrounding investment events. We examine firms in all sectors, but then decompose firms into their innovation, labor and energy intensities. This is particularly important for understanding and pinpointing problematic general sector trends and direct policy-making in specific sectors.
The main findings of our analysis are that when firms invest in both buildings and equipment simultaneously energy consumption increases. This result signals that firms engaging in a simultaneous investment are expanding the scale of their operations. Overall, energy consumption is increasing with increased production. However, new buildings and new equipment tend to incorporate technology consuming less energy. After investment has taken place energy efficiency improves. Operational efficiency of firms improves when investing in equipment but it decreases when investing in buildings. In particular, firms operating in energy and capital intensive industries and high-tech industries face financial damage when investing in buildings, which is an area where policy makers, engineers and firms can join to identify energy efficiency solutions.
The paper proceeds as follows. In the following section we provide a theoretical framework for firm capital expenditures that could impact energy performance. Then, we describe the data provided by CBS. In the next section, we explain our estimation strategy. Then, we depict our results for all sectors, provide an industry cluster analysis, analyze operational efficiency and consider a specific industry. We end with a conclusion and policy recommendations on the link between firm investment, energy performance, and operational performance.

Theoretical Framework
In this section we start with a framework to guide our empirical analysis of firm-level energy consumption and energy performance. The empirical strategy we employ is based on the notion of investment spikes. These represent large capital expenditures. Our aim is to identify such large expenditures as these most likely reflect major retooling or expansionary efforts of firms.
Investment data often also feature rather small expenditures. The model presented below does not account for these. However, it can be adjusted easily to capture these small investment as well without influencing the main results (Letterie and Pfann, 2007).
The presence of investment lumps can be explained as follows. Suppose that at time t a firm uses two capital inputs -the stock of buildings is given by and the stock of equipment is given by -to produce a non-storable output. We abstract from labour to ease exposition of the model, without affecting the theoretical insights obtained. The value of the firm is given by: The term Et (.) indicates that expectations are taken with respect to information available at time t.
The discount rate satisfies . The expression denotes net sales. We assume a Cobb-Douglas production technology with decreasing returns to scale, , where Y, e and ϕ denote production, energy use and total factor productivity, respectively. The price of energy is given by . The structural parameters satisfy . Energy is a fully flexible factor of production. The isoelastic demand function is given by , where . Then .
The term summarizes randomness in both total factor productivity and demand faced by the firm.
Upon investment the firm incurs adjustment costs given by: ( The function I(.) assumes the value 1 if the condition in brackets is satisfied and is equal to zero otherwise. The adjustment cost function allows for convex costs. Their size is reflected by the parameters and . Convex costs make large capital expenditures expensive and hence provide an incentive to firms to smooth investment over time. The prices of the capital investment are and , where for , . The purchase price for a unit of capital c is p c+ . When the firm sells one unit of capital the price received is p c-. As investment is l relaxed or if, equivalently, capital is increased by one unit. From equation (4) it can be seen that the shadow values represent the expected present discounted value of the marginal profit of capital minus the marginal adjustment costs in future periods. For we find that the first order condition for investment is: (5) .
The firm determines whether to change the stock of capital for by evaluating: (7) .
At the left hand side of equation (7) we see the expected benefits of investing. At the right hand side the cost associated with the firm's decisions is depicted. The expression approximates the benefits due to which we obtain a closed-form solution. This expression holds exactly in a continuous time framework with one production factor. Substituting equation (6) in equation (7) we observe that adjusting the stock of capital is profitable if . Hence, changing the amount of capital occurs if This equation reveals that if the net benefits of adjusting capital do not go above a certain threshold, the firm abstains from adjusting. The thresholds are due to the presence of fixed adjustment costs and , for instance. Investment becomes less likely, all else equal, with larger fixed costs.
Importantly, larger fixed costs also increase the size of the investment. In fact, with a larger threshold the left hand side of equation (8) must be higher and this expression affects the size of investment as can be seen from equation (6).
We expect that if at least one of the capital goods is subject to fixed adjustment costs, flexible input factors like energy will display a lumpy adjustment pattern as well (Abel and Eberly, 1998).
With more capital present in the firm, the (expected) marginal profit of energy usage is higher, assuming the production technology has not changed. Hence, the firm is likely to increase energy demand while investing in capital. This can be seen as follows. Assuming energy is a flexible input factor, it is set at the value maximizing . The firstorder condition is given by where . So, with more capital present, energy demand has to increase as well to restore equality of the first-order condition in equation (9). As observed previously, with fixed costs present, our model predicts investment lumps taking place. When capital is adjusted this way, energy demand will follow a very similar pattern accordingly. In our empirical analysis we investigate the dynamics of energy usage in periods surrounding an investment spike.
Investment may not only affect the scale of a firm's operations. It may also imply production technology changes when new capital enters the firm (Acemoglu, 2015;Dunne et al., 1989;Hémous and Olsen, 2015). For instance, upon investment the parameters and of the Cobb-Douglas production function depicted above may be altered, which potentially affects the optimal mix of input factors. Using equation (9) it is possible to show that This means innovation of energy efficiency given by is driven by developments in productivity of capital, , price development, , and a change in production technology, , ceteris paribus. One would expect that governmental efforts to induce firms investing in more energy-efficient buildings and equipment, make that the parameter in the production function increases relative to the other two technology parameters and , implying the firm needs less energy to produce the same amount of output and in this case energy use decreases relative to the use of capital for a given level of output.
We conclude from this theoretical model that capital investment can impact energy efficiency of a firm by inducing a change in the production technology and a change in the productivity of capital. From this theoretical framework, the empirical analysis will aim at revealing a broad picture of the dynamics of energy efficiency before, during and after periods where large capital investment takes place.

Data Description
The Netherlands is a country that provides a particularly rich data environment for analysing We aim to discern whether new buildings and equipment lead towards cleaner technology.
A unique opportunity provided by Statistics Netherlands (CBS) data is that both firm investment and energy consumption data is collected at the firm level. Moreover, the data enables the decomposition of the type of capital investment, e.g., buildings and equipment. CBS collects data on production statistics and investment figures at the firm level on an annual basis. A random selection of all Dutch companies employing less than 50 people is sent questionnaires and all firms with 50 or more employees receive the survey. 2 The data sets on production statistics and investments of the manufacturing sector are merged using a firm specific identifier. We focus on regular firm investment intensity dynamics and not on extreme events like divestments or mergers.
For that reason we construct a balanced panel. In this way, the panel conservatively controls for firm entry and exit, major (dis)investment decisions like mergers, acquisitions, bankruptcies and/or geographic relocations. In addition, as we want to employ empirical data, imputed observations are disregarded. We also removed the 1% largest investment ratios to obtain the final data, in order to prevent our findings from being affected by extreme outliers. The panel data set isolates investment behaviour for 2000 to 2008. We have chosen to limit our data up until 2008, as from 2009 onwards CBS used another operational definition of the unit of observation. We disregard years prior to 2000 as the balanced nature of the data would imply an additional loss of observations we like to avoid. Our data concern 652 firms and the total number of yearly observations equals 5,868. This sample is representative of large firms, those with 50 or more employees in the Netherlands and our data has close to 30 percent of the large firm sample. 3 Firms have been found to conduct investment in a lumpy fashion. Rather than smoothing investment over time, micro-level data have revealed that investment by firms is often concentrated in short time episodes. In this study we will focus on such bursts of capital adjustment as these events represent major retooling or expansionary efforts of firms (Letterie et al., 2010). To investigate micro-level consequences of such events, various studies have proposed a definition of investment spikes. Some have employed an absolute spike definition. In that case, if the investment ratio is larger than an ad hoc value such as 0.2, the observation is refered to as an investment spike (see for instance Cooper et al., 1999;Sakellaris, 2004). A drawback of the absolute spike definition is that it is not well suited to capture sporadic bursts of investment. Furthermore, we have experienced that when using this absolute spike definition for buildings we find rather few spikes.
For that reason we employ an event identification scheme for investments in both equipment and buildings, which does not suffer from these disadvantages. This classification method is refered to as a relative spike definition (Power, 1998;Kapelko et al., 2015). This approach works as follows.
Let the superscript z denote a certain type of capital good, i.e. equipment or buildings. 4 Power (1998) defines an investment spike as an instance where the investment ratio of a firm ! in a certain year t, , exceeds the median investment ratio of that firm by a factor ", where = 1.75. 5,6 We adjust Power's definition, as otherwise it would be too lax. The reason is that in our data, if a plant experiences the occurrence of 5 or more 'zero investment years' even the smallest investment in one of the remaining years would be classified as a spike. We aim for a more conservative and 4 In our data, there is a separate variable for (total; i.e., including buildings and equipment) leasing expenses for the individual firms. We only considered acquisitions in our analysis. However, especially in the Dutch manufacturing sector, leasing expenses are only a fraction of total expenses/investment in new capital. In the full sample of manufacturing firms' investments in 2000 firms show mean building investments of 20.38, mean equipment investments of 82.21, and mean total leasing expenses of only 2.41 (all figures in thousands of Euro). 5 See Appendix A for a description of how the capital stock is constructed. 6 Studies have employed absolute and / or relative spike definitions (Nilsen and Schiantarelli, 2003;Letterie et al., 2004). We have experimented with all types. For instance, we have also investigated an absolute spike definition of 0.2 for equipment and 0.1 for buildings. Using this approach is providing similar results. Power (1998) tested three different values of , a low (1.75), medium (2.5) and high (3.25) threshold. She found that results obtained using various investment spike definitions were not really sensitive to the chosen value of . In our data results for the different levels of are also robust to the definition employed. We have also experimented with various rates of depreciation for buildings and equipment, again indicating our results are robust. Results are available upon request.
robust definition. Hence, our spike definition includes the constraint that the investment ratio also needs to exceed the depreciation rate for the asset in casu: The depreciation rate is denoted by . A strictly positive number for depreciation tends to limit the number of spikes because of the restriction in the spike definition. The depreciation rate for buildings is set at 0.02. We choose a conservative number for the commercial building sector in Europe (Bokhari and Geltner, 2014;Chegut et al., 2014). As we employ Dutch data the depreciation rate for equipment is 0.05 in line with Letterie and Pfann (2007). The depreciation rates we employ for buildings and equipment are consistent with the geometric depreciation approach employed by the US Bureau of Economic Analysis calculating the depreciation rate dividing the declining balance rate by the service life using the information provided by Görzig (2007)  Simultaneous investment spikes in both equipment and buildings signal significant expansion.
To measure unusual expansionary events at the level of the firm, we include a simultaneous investment spike variable: (12) .
So, the variable identifies the event of a simultaneous spike.
A set of descriptive statistics for the investment spikes (in buildings, equipment, or a combination of both) is provided in Table 1. We observe that the frequency of spikes is 8 and 11 percent for buildings and equipment respectively. Simultaneous spikes occur in 3 percent of the data. The mean investment rate for equipment during simultaneous spikes is higher than in the absence of a spike in buildings, hinting at an expansionary effort of the firm. Notes: Table 1 documents the distribution of investment rates by investment spike in all observations, buildings, equipment and simultaneously both. Percentage of total is a frequency measure representing the number of datapoints observed.
In Table 2 we depict the mean and standard deviation of energy usage, energy efficiency and operational efficiency under the scenarios of (i) all observations, (ii) no investment spikes, and in case of (iii) single spikes in buildings, (iv) single spikes in equipment and (v) simultaneous spikes.
Note that we have deflated production values and energy expenses by the annual production and    Table 2 reveals firms not experiencing a spike at all are relatively small as measured by the level of production and number of workers. Also firms only conducting a large investment in buildings tend to be rather small. Firms that are involved with large investments in equipment are larger on average. Firms just conducting major investments in buildings do not use a lot of energy, compared to firms investing in equipment.
With respect to energy efficiency we see from the bottom part of Table 2 that firms engaging in equipment spikes are more efficient in energy usage as reflected by energy consumption divided by the capital stock of equipment. In contrast, firms displaying building spikes are more efficient in energy usage as reflected by energy consumption divided by the capital stock of buildings. Firms not conducting major investment episodes typically belong to the least energy efficient groups, by all measures. Finally, we analyze the variable operational efficiency as measured by the ratio of cost to sales. Note that the cost variable does not include investment expenditures. It appears our measure of firm efficiency is relatively stable and independent of the investment profile of the firm.
To identify high-and low-tech industry categories in Dutch manufacturing we follow Raymond et al. (2006). A low-tech firm is characterized by a low propensity to engage in innovation-seeking activities like R&D and innovation subsidy achievement. Following Raymond et al., wood-based industries are characterized as distinctively non-innovative. Labor and energy intensity industry groupings are based on Ramirez et al. (2005). Table 3 identifies how the sample firms are classified by innovation, energy and labor intensity. High-tech and low-tech sectors represent 39 and 45 percent of the sample, respectively. High-tech firms are predominantly active in the Oil and coal, Chemicals, Transportation, and Machines and apparatuses sectors. High-, medium-and low-laborintensive industries reflect 22, 30 and 49 percent of the sample, respectively. Low-labor-intensive industries are split almost evenly between high-tech and low-tech industries. High-, medium-and low-energy-intensive firms represent 47, 21 and 33 percent of the sample data, respectively.
Energy-intensive firms can be found in industries like Food, drinks and tobacco, Paper and pulp, Oil, coal and chemicals, Non-metallic minerals and Metals. Notes: Table 3 documents the frequency of our sample by sector classification, innovation intensity and energy and labor intensity. Some sectors have been aggregated into bigger groups, as Statistics Netherlands (CBS) requires reported statistics to be based on some minimum number of firms to ascertain anonimity of findings. The SBI classification system is the Dutch equivalent of the United States SIC system. Innovation intensity classification based on Raymond et al. (2006) and energy and labor intensity classification based on Ramirez et al. (2005).

Methodology
The goal of this study is to obtain basic facts concerning the dynamics of energy consumption and efficiency surrounding events of major investment efforts conducted by firms. Our analysis is descriptive and non-parametric rather than structural and unables us to observe investment activity across capital types. Our methodology is in line with a common approach in the capital investment literature, when the objective is to obtain descriptive evidence of firm behavior in times of major investment episodes. Hence, we closely follow Sakellaris (2004), Letterie et al. (2004) andNilsen et al. (2009) and first identify events of large capital adjustments by firms. We use these events and look into what is happening with firm-level energy metrics in periods surrounding these events.
One way of looking at this event type of methodology identified by investment spikes is that such episodes reflect that a firm was hit by a large shock. The investment spike in itself represents the response of the firm to the shock. Alternatively, the firm may have been subject to a series of smaller shocks to which the firm has not responded yet due to the presence of fixed capital adjustment costs, for instance. The large investment event reflects that the firm has taken action now (Sakellaris, 2004).
We start investigating the dynamics of energy usage and energy efficiency surrounding investment spikes for firm-level dependent variables measuring -as denoted by DVi t -using the following empirical model: , , The parameter vector contains six elements; one for each of the components in . The vectors in equations (14) to (16) are designed in such a way that they can track the dynamics of the dependent variables in the time period surrounding a certain spike event. This works as follows.
The vector consists out of six elements for . The variables allow for identifying what happens to the firm's energy metrics between two years prior to, and two years post a certain firm investment spike event.
Let us consider the example of spikes in building investments. For example, the coefficient of measures the change in the dependent variable two years before a spike in buildings occurs.
The variable takes the value 1 if a spike occurs in year t+2 for investment in buildings (but not in equipment) and no spikes in buildings occur in years t and t+1. In all other conditions, it takes the

Results
In Table 5 we present our results reflecting the dynamics of firm level energy performance metrics surrounding investment spikes for the full sample. Based on these results we constructed Figures   1 and 2. These figures depict how three energy performance metrics behave around specific investment events for the full sample. We observe that energy consumption tends to increase by around 7 percent when a firm experiences a simultaneous spike. This result is statistically significant. Energy usage displays no major changes in case firms conducting a single spike.
Depicted in Figure 2, Energy Consumption per Capital Stock of Buildings, energy efficiency does improve by 4 percent after three or more years when a firm experiences a spike in buildings. In Figure 2, Energy Consumption per Capital Stock of Equipment shows that three or more years after either a simultaneous spike or a single spike in equipment the firm's energy efficiency improves by even 12 percent. Strikingly, before these spikes the energy efficiency metrics indicate relatively low performance, indicating that the firms' production processes have become more energy inefficient potentially due to working at full capacity or due to aging. Thus, we see from Figure 1 that firms use more energy after conducting major investment efforts in both buildings and equipment simultaneously. However, Figure 2 reveals they do use the energy more efficiently after the investments have taken place. Even though the scale of operations tends to increase, especially in case of a simultaneous spike, firms produce using more energy efficient processes. In fact, in the case of a single spike in buildings, the energy consumption per unit of buildings decreases.
Furthermore, if a single or simultaneous spike occurs including equipment, energy usage per unit of equipment decreases. probably an optimistic estimate of technological progress concerning energy use in the firm's production function. Nevertheless, based on this number we conclude that production technology has become more energy efficient in cases where a firm experienced a simultaneous spike. Similar findings hold if we would use results concerning energy efficiency and productivity of equipment (buildings) three or more years after a single spike in equipment (buildings). 7 *** Figures 1 and 2 about here *** 7 It is clear that smaller businesses are underrepresented in our sample. This is due to the specific sampling method used by CBS. We have for firms employing fewer than 50 fte a number of observations of N = 1,063 and for firms employing 50 fte or more N = 4,805. In terms of performance, small and larger firms are quite comparable. For instance, for the variable Total Costs / Sales the means of the two groups do not significantly differ from each other. In terms of our regression findings, we see that the patterns described for the overall sample are quite similar between smaller and larger firms, although statistical significance is mainly observed among the subsample of larger businesses. This is likely due to the relatively small sample of smaller firms. The main observations (1) firms increasing energy consumption after large simultaneous investments in buildings and equipment, (2) energy efficiency improving after large simultaneous investments, and (3) operational efficiency improving after investments in equipment, yet decreasing after investments in buildings are similar in both subsamples, although again not always accompanied with statistical significance in the small firm subsample. Results are available upon request.

Industry Cluster Analysis
The table in Appendix B presents the results of our analysis conducted on sample breakdowns by innovation, labor and energy intensity to obtain a more granular insight. Based on these results, we constructed Figures 3 to 5. In Figure 3 we depict the results distinguishing firms by innovation intensity. In this discussion we disregard the separate Wood sectors as these yield few statistically significant results in terms of what happens after the various spike events. We see from In sum, we find some differences across various types of industries as distinguished by innovation, energy and labor intensity. However, the result that major investments involving equipment (single and simultaneous spikes) are correlated with better energy efficiency is largely robust across sectors. The result that simultaneous spikes are associated with higher energy usage is found most strongly in Low Tech, High Energy Intensity and Medium Labor Intensity sectors.

Operational Efficiency
In Table 6 we report how efficiency of the firm evolves surrounding major investment events for the full sample, as well as for the sample breakdowns by technology, labor, and energy intensity.
In case of simultaneous spikes, we observe that the total cost to sales ratio does not change significantly. Operational efficiency tends to improve three or more years after an investment spike in equipment only, though investments in buildings do the opposite. In the full sample efficiency does not get worse by more than 1 percent. Notes: Table 6 presents the results of the estimation parameters for the economic performance surrounding investment spikes in equipment, buildings and simultaneously both. The dependent variable across regressions is on the horizontal row and has received a (natural) logarithmic transformation. Dependent variable: TC/S = Total Costs / Sales. Abbreviations: HT (LT) = High-(Low-) tech industries, W = Wood, HE (ME, LE) = High (Medium, Low) energy intensity industries, HL, (ML, LL) = High (Medium, Low) labor intensity industries. Parameter estimates, conditioned upon observing an investment spike, are documented by investment spike time and investment type. The models include year dummies, but omitted from table display. Statistical significance is reflected by: * p ≤ .10, ** p ≤ .05, *** p ≤ .01.

Operational Efficiency in Oil & coal and Chemicals
According to Table 6 sectors that are harmed most in terms of operational efficiency by investment in buildings are high-tech industries, industries with low labor intensity and those with high energy intensity. With these categories in mind, Table 3 informs firms operating in Oil & coal and in Chemicals face a statistically significant financial disincentive to invest in buildings. Operational efficiency decreases by more than 1.5 percentage points in such capital-intensive industries. This is confirmed by a separate analysis reported in Table 7 that we conducted for these sectors.
Operational efficiency decreases by 3.4 percent after three or more years after a spike in buildings when we group the observations for Oil & coal and Chemicals. This result is statistically significant. For these sectors we find that upon investment, energy use does increase, however only a single investment spike in equipment does so according to conventional statistical significance levels. Likewise, previous literature identified that in Chemicals firms increased energy consumption after participating in voluntary energy efficiency equipment programs (Gamper-Rabindran and Finger, 2013). Nevertheless, our results suggest that also in these industries implying that investment is associated with higher energy efficiency. We conclude that the sectors mentioned in this paragraph may enhance environmental performance by investing in appropriate capital goods. Nevertheless, appropriate financial incentives to do so seem absent. Notes: Table 7 presents the results of the estimation parameters for the dynamics of performance metrics surrounding investment spikes in equipment, buildings and simultaneously both. The dependent variable across regressions is on the horizontal row and has received a (natural) logarithmic transformation. Dependent variables: EC = Energy Consumption, EC/CB = Energy Consumption / Capital Stock Buildings, EC/CE = Energy Consumption / Capital Stock Equipment, TC/S = Total Costs / Sales. Parameter estimates, conditioned upon observing an investment spike, are documented by investment spike time and investment type. Statistical significance is reflected by: * p ≤ .10, ** p ≤ .05, *** p ≤ .01. The models include year dummies, that are however not displayed to save space.

Conclusion
Environmental performance is a core 21st century challenge and firms have an important role to play in increasing energy efficiency and decreasing toxic pollutants. Yet, there is limited evidence on the links between firm investment activities and how they can be used to change environmental performance outcomes. For firms, investment events are where they can plan for financial and environmental performance. Large investment spikes influence the productivity of firms, can impact hundreds of employees, change operational costs and influence the production of millions of manufactured goods. Hence, firms seek efficiency in equipment, buildings or the simultaneous use of both. We investigate how energy performance of Dutch manufacturing firms over the 2000 to 2008 period evolves in the time surrounding investment spikes in capital. Our contribution is to identify whether these investment spikes had an impact on energy consumption, assess whether or not firms improved energy efficiency in anticipation of the investment event or after an investment outlay occurred and identify the capital source where environmental performance is correlated with investment spikes.
From our multi-period event study, we find that when firms engage in large simultaneous investments in both buildings and equipment, energy consumption levels increase considerably by 6.4 percent in the first year after the spike and continues to rise to an average 7.4 percent in 3 or more years. For this result, we may be able to interpret a simultaneous capital expenditure as a signal of expansionary or replacement activities and potentially implies that when firms engage in growth activities there is correlation with energy consumption growth overall. This result correlates with expectations around firm investment, but it does not necessarily point to poor environmental performance from the building or equipment capital. In fact, we find that in cases of major investment spikes energy efficiency improves. Moreover, we find that these investments do not correlate with an increase in the total cost per sales after an investment is made. Notably, investments in equipment improve the operational efficiency by approximately 2 percent after three years from the investment spike. This suggests that industrial firms can decrease energy consumption whilst increasing operational efficiency -the so called "doing well by doing good".
We find that after investments energy efficiency in equipment improves. Our results for the overall sample imply that less energy is needed to produce a certain level of output once a firm invests. We show that after three years from the investment outlay energy efficiency increases by about 12 percent per unit of equipment. Though energy efficiency improves after installing new equipment capital, we observe some evidence of energy efficiency decreasing subsequent to investment in buildings in high technology, high energy and low labor industries by 1.7, 1.6 and 1.9 percent respectively. Especially firms operating in capital-intensive industries like Oil & Coal and Chemicals seem to be affected by stricter building codes.
Our results document a positive correlation with environmental performance after investment spike activity, which suggests firms investing in industrial buildings do so with energy efficiency as a primary objective for financial performance. Anecdotally, there is evidence of success in the industrial sector to meet energy efficiency performance targets. As an example, Prologis is a global industrial real estate company that is committed to decreased energy consumption to improve financial performance. 8 However, from a policy perspective lagged firm awareness, poor environmental performance knowledge on behalf of firms, poor equipment and(or) building standards knowledge or lagged policy implementation may lead to decreased planning for environmental performance at the time of investment spikes. Thus, more common policy approaches like voluntary agreements with firms may not be of much help in this context, but thirdparty verification or enforceable penalties when firms do not perform, subsidies or R&D tax credits could potentially yield better results (Earnhart, 2004;Cole et al., 2005;Gamper-Rabindran and Finger, 2013).
Currently, CBS data does not enable us to identify the effect of energy efficiency standards or codes in our results and more generally there are limitations around observing expansionary events by firms. In addition, we do not observe substitution of activities between plants that belong to the same company. Future research may also seek to observe expansionary investment in BRIC economies. However, our results have allowed us to observe that there is a correlation between energy related metrics and firms who have purchased new equipment capital. For policy makers to take note, we observe that growing firms become more energy efficient. Firms conducting large investments are planning for a future cash flow that is promising and invest in equipment where they expect to yield a profit maximizing positive financial return from such decisions. Furthermore, it is financially healthy firms having cash flow streams and/or access to the capital markets that are capable of investments at this magnitude. Sometimes firms need to finance capital improvement expenditures themselves due to capital market imperfections (Fazzari et al., 1988), but in the EU's current Horizon 2020 program there are numerous avenues to apply for financing and subsidies that support the purchase of new energy efficient equipment and building technologies. Potentially in the long-run, as the manufacturing sector switches towards environmental performing capital, then energy efficient firm production could replace firm production that is both less successful and energy inefficient. In the aggregate, technological progress in equipment and buildings both with an aim towards energy efficiency may be improved by replacement of unsuccessful firms by more energy efficient competitors (Acemoglu et al., 2014;Acemoglu et al., 2009).
In this way, policy makers should continue to recognize, convene and set standards towards industrial environmental performance. Environmental goals can be attained by advancing standards for materials, equipment, building codes and the emissions of pollutants without significant financial repercussions to firms. This view is consistent with studies observing that "lean and green" go hand in hand. Investing in environmental technology enhances manufacturing performance (Klassen and Wybark, 1999;Telle and Larsson, 2007) and lean production processes are complementary to environmental performance (King and Lenox, 2001). Future research is required to understand whether it is such evolutionary developments in industries, allowing countries to become less dependent on energy in the aggregate.