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Technical Appendix A2 Simulation results: impacts of increased energy efficiency

A2.1 Introduction

In the recent AEA report to the Scottish Government (Mitigating Against Climate Change in Scotland: Identification and Initial Assessment of Policy Options), one policy option suggested to reduce GHG emissions (in the context of reducing emissions from the electricity generation sector (Section 4.1.2)) is to reduce demand for electricity through efficiency improvements. However, as some of our recent work has shown (see Hanley et al, 2008, and Turner, 2008a), the relationship between efficiency in energy use and demand for energy may not be so straightforward. This work reflects the wider debate in the academic and policy communities regarding potential 'rebound' effects of improvements in energy efficiency.

Therefore, a useful starting point for the modelling work in this project is to use the economy-wide AMOSENVI computable general equilibrium ( CGE) modelling framework to examine the impacts on domestic energy use and emissions generation and on economic activity in Scotland as a result of an illustrative 5% improvement in energy efficiency introduced to different sectors of the economy. To aid comprehension we link our analysis to an environmental input-output ( IO) analysis for Scotland. As noted in Section 1 of this report, it is important to be aware (as in the case of all the simulations in this project) that the quantitative results of both the IO and CGE analysis must be qualified by the fact that both rely on the 1999 Scottish IO tables. These tables are used because the IO data for this year were developed to permit more useful economic environmental analyses (with inclusion of experimental data on physical energy use and emissions at the sectoral level and disaggregation of the electricity sector by generation type). However, these data are somewhat dated now, and the environmental augmentations are very experimental. Both of these factors will impact on the reliability of results for policy analysis. Nonetheless, it is hoped that analysis illustrating the potential usefulness of IO and CGE analysis in the area of climate change policy will draw attention to areas that may merit investment in improved data collection and reporting.

A2.2 The issue of potential 'rebound' effects

A more efficient use of energy is often cited as the key to increasing economic productivity and growth, whilst reducing environmental damage at the same time (a decoupling effect). In addition, a more efficient use of energy is generally less costly than switching to new forms of energy and takes less time to implement. However, while in principle the idea of doing more with less satisfies the criteria of increasing output whilst decreasing the environmental damage of economic activity, there is growing evidence in the academic and policy literature about the so called rebound effect and, at its extreme, the backfire effect. The presence of such effects, which can be observed from the results presented here suggests that any positive environmental effects of efficiency improvements may be partially or wholly offset in the presence of rebound or backfire effects. In such situations there is an offsetting increase in demand for energy when there is an improvement in energy efficiency. This is due to the impact on effective and actual (where there is domestic energy production) energy prices and the subsequent economy-wide response to changing prices. ¹³

Hanley et al (2008) and Turner (2008a) identify five distinct types of system wide effects in response to an energy efficiency improvement. These comprise (i) a need to use less physical energy inputs to produce any given level of output (the pure engineering or efficiency effect); (ii) an incentive to use more energy inputs since their effective price has fallen (the substitution effect); (iii) a compositional effect in output choice, since relatively energy-intensive products benefit more from this fall in the effective price; (iv) an output effect, since supply prices fall and competitiveness increases; and (v) an income effect as real household incomes rise. While (i) reduces energy demand, (ii)-(v) increase it.

However, our more recent research on the nature and magnitude of potential rebound effects in the Scottish and UK economies (Turner, 2008a) has drawn attention to another type of effect that may occur in response to changing prices, partially or wholly offsetting rebound effects in some cases, but with implications for economic development. This is occurrence of disinvestment in the energy supply sectors that may also occur as a response to improvements in energy efficiency. Disinvestment occurs because output prices in the energy supply sectors (domestic energy prices) fall in response to the increase in energy efficiency. Where profitability falls, capital rental rates (the return on capital) fall and disinvestment occurs. The presence of disinvestment helps to constrain/dampen rebound effects as the economy adjusts to improvements in energy efficiency. In short both rebound (increased demand) and disinvestment (supply-side contractions in capacity) effects occur in response to falling energy prices, though the latter solely in response to falling actual prices of domestically produced energy, and which type of effect dominates (as with rebound versus the pure efficiency effect) depends on the degree of price responsiveness throughout the system.

A2.3 Simulation strategy

Using the AMOSENVI energy-economy-environment Scottish CGE model we simulate a 5% improvement in energy efficiency as an exogenous (and costless) increase in energy-augmenting technological progress to all production sectors. While this may not be the most 'realistic' scenario to simulate, it allows us, in the first instance, to consider and develop an understanding of the basic underlying drivers of rebound effects. The work reported here builds on our previous work for Scotland, reported in Hanley et al (2006, 2008) and Turner (2008a), where the exogenous 5% improvement in energy efficiency is initially applied to all 25 sectors in the Scottish economy, and on our previous work for the UK (Allan et al, 2006, 2007a) where a similar shock is applied using the UK variant of AMOSENVI, UKENVI.

However, in the current project, we develop on our previous work by simulating the energy efficiency improvement on a sector-by-sector basis and in groups of sectors: the results are additive but taking this approach allows us to identify the key sectoral drivers of the rebound and backfire effects previously reported for Scotland. In each case, we examine the impacts on the five energy supply sectors, on total energy consumption and on CO2 generation in the Scottish economy, as well as the impacts on key variables such as GDP, total household consumption, employment, imports and exports. In general, we would expect that a beneficial supply-side policy such as an improvement in energy efficiency will lower the unemployment rate, increase real wages and have a positive impact on Scottish economic activity that is greater in the long run than the short run. However, in the simulations reported below, we see that the extent of the positive economic effects, and nature, magnitude and direction of effects on environmental indicator variables, depends on the type of activity targeted with the efficiency improvement. ¹⁴

It is important to note that the system-wide response to improvements in energy efficiency (or any shock) also depends on the configuration of the model. Due to the number of sectoral shocks reported in the energy efficiency simulations reported here we do not attempt the type of comprehensive parametric sensitivity analysis reported in Turner (2008a) for the 25 sector case, or even a more limited variant, such that reported in Hanley et al (2008) or for other simulations conducted in this project. The default model configuration is as explained in Hanley et al (2008); however, in summary, we assume elastic export and import direct and derived energy demands ( i.e. for an X% change in relative prices, demand will change by more than X%) but inelastic local intermediate demands ( i.e. for an X% change in relative prices, demand will change by less than X%). However, Turner (2008a) shows that as price responsiveness increases in any part of the system, rebound effects will also increase. We highlight this below in the case of the commercial Transport sector.

Selected simulation results are presented in the tables and charts below and (as with all simulations in this project) are reported in terms of percentage changes from base year values given by the 1999 SAM data for Scotland. This allows us to examine the impacts of this shock in isolation ( i.e. assuming no other changes in the economy). The base year (1999) SAM is assumed to represent a long-run equilibrium in the Scottish economy. The short and long run time periods that are referred to are conceptual. The short run is the first period after the shock where labour and capital stocks are assumed fixed at the sectoral level. In the long run, labour and capital stocks have fully adjusted to their desired sectoral values. We present results of multi-period (year by year) simulations, showing the process of adjustment to a new long-run equilibrium.

A2.4 Overview of results of energy efficiency shocks

Occurrence of rebound and/or backfire effects

If we have a rebound effect, this means that there is a fall in energy consumption in response to an increase in energy efficiency, but this is less than proportionate. For example, if energy efficiency increases by 5%, we would expect the direct (engineering) efficiency effect to be a 5% decrease in energy consumption. However, if the change in the effective and/or actual price of energy triggers substitution, output/competitiveness, composition and/or income effects (which all act to increase energy consumption) we would expect to see a decrease in energy consumption that is less than 5%. If, for example, energy consumption only falls by 2.5%, we have 50% rebound. However, if there is sufficient price responsiveness in the system (through direct and indirect, or derived, demands for energy) coupled with features such as the direct and/or indirect energy intensity of the sector targeted with shock, the increase in energy consumption may act to more than fully offset any pure efficiency gains, which would give us backfire effects (rebound effects of more than 100%). This will lead to an increase in energy-related emissions generation at the economy-wide level. As noted above, the strength of rebound effects is governed by the direct and indirect/derived elasticities of demand for energy throughout the economic system, as well as features such as direct and indirect energy intensities, openness to trade, elasticity of supply of factors of production etc. Backfire effects are observed for Scotland in previous work by Hanley et al (2008) and Turner (2008a). However, as explained below, these are mainly driven by the response to changes in energy efficiency in the electricity generation sectors, and are a function of the trade in electricity between Scotland and the Rest of the UK. The GDP, CO2 and rebound effects of targeting the energy efficiency shock at each sector, or groups of sectors, in turn (the simulations run specifically for this project) are shown in Tables A2.1 - A2.4 on the four following pages (followed by discussion of the results reported therein).

Table A2.1- Short and Long Run Impacts on GDP and CO2 from a 5% Increase in Energy Efficiency in Each Sector of the Scottish Economy

Table A 2.2- Short and Long Run Impacts on GDP and CO2 from a 5% Increase in Energy Efficiency in Selected Groups of Sectors in the Scottish Economy

Table A 2.3 - Short and Long Run Rebound Effects from a 5% Energy Efficiency Improvement Targeted at Each Sector of the Economy

Table A 2.4- Short and Long Run Rebound Effects from a 5% Energy Efficiency Improvement Targeted at Selected Groups of Sectors

Discussion of results

Table A2.3 shows that when the energy efficiency shock is introduced to either of the two electricity supply sectors, large backfire effects occur in the long run. However, if it is introduced solely to any one of the other 23 sectors, while some extent of rebound is observed in all cases, backfire is only observed in the Sea Fishing sector, and only in the long run in the case of electricity use. The first implication, and a key one in the current project, is that we do in fact observe reductions (or no net change) in CO2 emissions when energy efficiency increases in any sector except the electricity supply sectors (where backfire occurs for all types of energy use). Even in the case of Sea Fishing, where the largest electricity rebound effect is observed (see below), there is a net fall in CO2 emissions because other types of energy use are reduced.

Figure A2.1. Impact of a 5% increase in energy efficiency in the Non-renewable Electricity sector on key indicators

Figure A2.2. Impact of a 5% increase in energy efficiency in the Non-Renewable Electricity sector on environmental indicator variables

It is useful to look more closely at the cases where backfire is observed as the nature of this effect is different in each of the three cases. Backfire in the Non-renewable Electricity sector follows the patterns expected in the existing literature. This is the most directly energy-intensive production sector and, in our 1999 database, accounts for around 25% of total electricity use and around 20% of total non-electricity energy use in the Scottish economy. Table A2.1 and Figures A2.1 and A2.2 show that, while there is a significant positive impact on GDP (0.5% over the long run), the proportionate increases in all types of energy consumption at the economy-wide level are much bigger (2.1% for electricity and 1.6% for non-electricity energy consumption), with a resulting negative impact on all the key 'sustainability' indicators reported. In the case of Renewable Electricity, on the other hand, while the backfire results in Table A2.3 are also very large, this sector is much less energy intensive and accounts for (again, according to our 1999 database) only 0.2% of total non-electricity energy use and 4.5% of electricity use in the Scottish economy. Figures A2.3 and A2.4 show that, while both types of energy consumption rise over the long run - by 0.2% for electricity and 0.07% for non-electricity - these increases are much smaller than in the case of Non-Renewable electricity, with the large backfire effect driven by the fact that such a small share of energy use is directly affected by the shock. However, the impact on key indicator variables, such as the energy and CO2 intensity of GDP (see Table A2.1) is smaller when the shock is targeted at the Renewable sector. Thus, there is a trade-off to be considered - both positive economic and negative environmental effects are smaller. However, there are a wide range of variables to be taken into account; for example when the Renewable Electricity sector is targeted, there is a fairly rapid and significant increase in the share of electricity generated from renewable sources (see Figure A2.3), but this is assuming no constraints on the growth of this sector in response to the positive supply stimulus.

Figure A2.3-Impact of a 5% increase in energy efficiency in the Renewbale Electricity Sector on Key Indicators

Figure A2.4 Impact of a 5% increase in energy efficiency in the Renewable Electricity Sector on Environmental Indicators

The Sea Fishing sector is an interesting case. This is the least electricity-intensive sector in the Scottish economy. Table A2.3 shows that here we observe the smallest electricity rebound effect in the short-run, but the biggest long-run backfire effect (bigger even than in the electricity sectors). Again, the changes in energy consumption underlying this dramatic result are very small in the case examined here. Electricity consumption in the Sea Fishing sector itself falls in response to the increase in efficiency, but there is a small increase in aggregate electricity consumption of 0.0008% (over the long-run). This is mainly driven by increases in imported and domestic electricity used by the 'Transport' and 'Textiles and Clothing' sectors, both of which are direct intermediate suppliers of inputs to the 'Sea Fishing' sector. The increase in aggregate energy consumption is small but the efficiency shock is applied to a small share of total energy use. This means that there is in fact a sizeable backfire effect in terms of electricity consumption (323.8% over the long run) even though the shock is limited to the least (directly) electricity intensive production sector in the economy. This demonstrates why a general equilibrium framework is essential in assessing the nature and scope of rebound effects, even when improvements in energy efficiency are focussed in a single sector/activity.

Use of environmental IO analyses in understanding and anticipating CGE results

The electricity rebound result for the Sea Fishing sector may be surprising to readers who associate large rebound effects mainly with direct energy intensities. However, if we begin our analysis with a basic environmental IO accounting analysis, the results are not so surprising. Environmental IO analysis allows us to examine different types of multipliers for energy use and pollution generation, which take into account the target sector's backward linkages in the economy and the pollution and energy use embodied therein. The most striking IO results for the Sea Fishing sector are its Type 1 and Type II 'electricity to electricity' multipliers (see Figure A2.5). These show us how much electricity use in the economy arises per unit of direct electricity use for the sector in question. In the case of Sea Fishing both of these multipliers are the largest of the 25 Scottish production sectors identified in our framework, with 7.2 gigawatt hours arising per 1 gigawatt directly consumed in the Type I case and a huge 43.3 gigawatt hours per 1 gigawatt in the Type II case. These multipliers suggest that there will be large multiplier effects in terms of electricity use at the economy-wide level for any unitary direct change at the sectoral level.

Figure A2.5 Type 1 and Type II 'electricity to electricity' multipliers

The role of CGE analysis in modelling the impacts of shocks that affect prices and supply-side behaviour

While IO analyses are undoubtedly useful in understanding the importance of sectoral linkages, where we expect price effects to be important - for example, where rebound and disinvestment effects in response to a change in energy efficiency. ¹⁵ The value-added from CGE in identifying and analysing such effects can be shown if we continue with the example of improved energy efficiency in the Sea Fishing sector. The IO results in Figure A2.6 (direct, Type I and Type II non-electricity intensities for the non-energy supply sectors - i.e. non-electricity inputs, measured in tonnes of oil equivalents per unit of output - show that the Sea Fishing sector has the highest direct intensity among the non-energy supply sectors. Therefore we may expect the explanation of the non-electricity rebound effect to be more straightforward (as in the case of the Non-renewable Electricity sector), although the Type I and II multipliers in Figure A2.6 show that indirect and induced effects will again be important.

Figure A2.6 Type 1 and Type II 'non-electricity to non electricity' multipliers

However, Table A2.3 shows much smaller short and long run rebound effects for Sea Fishing relative to those for Non-renewable Electricity. Part of the explanation will be that the latter is much more intensive in terms of non-electricity energy inputs. However, the disinvestment effect discussed above is important in the case of Sea Fishing, particularly in the case of oil (diesel) as an input to production, the local supply of which comes from the Oil (Refining and Distribution and Nuclear) sector, hereafter simply referred to as the Oil sector. When the output price of the Oil sector falls in response to the initial contraction in demand from the pure efficiency effect in the Sea Fishing sector, this is sufficient to lower profitability to such an extent that the capital rental rate decreases sharply, as shown in Figure A2.7, triggering disinvestment.

Figure A2.7 Percentage Change in Capital Rental due to a 5% improvement in the sea fishing sector

The reason why the disinvestment effect his does not occur in the case of Non-renewable Electricity is that (in our model configuration), demand for output is sufficiently elastic to increase revenues as prices fall and prevent profitability from decreasing to the extent that the capital rental rate collapses ( i.e. as explained above, it is the net impact of different effects that is key to the outcome in any one case). In the case of what happens to the Oil sector when the Sea Fishing sector is targeted the energy efficiency shock, while demand throughout the system for Scottish Oil outputs does respond to the initial drop in prices (for example, with an increase in export demand), this is not sufficient to prevent profitability from decreasing and disinvestment occurs. In order for the return on capital and profitability to recover, the price of locally produced oil has to begin rising again - 12 years after the shock it is back to its initial level - which will dampen the rebound effect. This process occurs in all of the cases where disinvestment is reported in Tables A2.3 and A2.4. However, note that in some cases, the rebound effect is constrained to such an extent that it is smaller in the long run than in the short run.

Other sectors of interest: public and other services

Figure A2.8 Electricity Intensities

Part of the motivation of reporting the energy efficiency simulations is to demonstrate how CGE results can be interpreted, with input from a more straightforward IO analysis. To take another example, given the focus on reducing electricity consumption through improved energy efficiency in the recent AEA report, combined with the summary results in Tables A2.1 and A2.3 and our IO analyses, the Public and Other Services sector (hereafter referred to as POS) is of interest. Figure A2.8 shows that POS has the largest Type II electricity-output intensity (electricity required per unit of final demand for POS output) among the non-energy supply sectors and also a relatively high direct intensity. Table A2.1 shows that (of the sectors we identify) the 5% increase in energy efficiency leads to the largest long run drop (-0.31%) in total CO2 emissions, accompanied by one of the largest long-run increases in GDP (0.02%). Figures A2.9 and A2.10 show that, in contrast to the results where the electricity sectors are targeted (Figures A2.1-A2.4), there is also a desirable impact on all the sustainability indicators identified ( i.e. energy consumptions and emissions fall, while the GDP-intensity of energy use and emissions generation rises). However, Figure A2.11 shows that the impact on capacity in the energy supply sectors (all of which suffer from disinvestment effects) should also be taken into account. Indeed, the strength of the disinvestment effect is such that this is one of the cases where rebound effects actually decline over time.

Figure A 2.9 Impact of a 5% increase in energy efficiency in the Public and Other Services Sector on Key Energy Indicators

Figure A 2.10 Impact of a 5% increase in energy efficiency in the Public and Other Services on Environmental Indicator Variables

Figure A2.11 Percentage Change in Capital Stocks in the Energy Sectors due to a 5% improvement in the Public and Other Services

Other sectors of interest: the aggregate manufacturing sector

In the AMOSENVI model 6 manufacturing sub-sectors are identified. When these are shocked individually all show some extent of rebound effect, dampened by disinvestment effects, but with decreases in CO2 generation. However, the identification of these sub-sectors is ours and there may be interest in what happens if we introduce the energy efficiency improvement to Scottish manufacturing as a whole. Therefore, we have run another simulation where all 6 manufacturing sectors are shocked together. Summary results are presented in Tables A2.3 and A2.4 (along with some other grouped sectors that may be of interest). Note that the positive effects of improved energy efficiency in manufacturing are amplified when the whole sector is affected, with a long run increase in GDP of 0.07% accompanied by a 0.14% decrease in total CO2 emissions Figures A2.12 and A2.13 show period-by-period (year-by-year) results for the energy and environmental indicators identified in this study. Again, these show that the effects of improved energy efficiency in manufacturing are generally positive, with absolute decreases in all types of energy consumption and CO2 generation, and fall in the energy and CO2 intensities of economic activity.

Figure A2.12-Impact of a 5% increase in energy efficiency in the Aggregate Manufacturing Sector on Key Energy Indicator Variables

Figure A2.13- Impact of a 5% increase in energy efficiency in the aggregate Manufacturing sector on environmental indicator variables.

The only negative effects are that the share of electricity generated from renewable sources falls (this is a result of the great stimulus to the competitiveness of the more energy intensive Non-renewable electricity sector) and there is a contraction in capacity in all five Scottish energy supply sectors due to the disinvesment effect (see Table A2.4 and Figure A2.14).

Figure A2.14- Percentage Change in Capital Stocks in the Energy Sectors due to a 5% Improvement in the Aggregate Manufacturing Sector

A note of caution

The results presented in this appendix serve the dual purpose of indicating the likely nature of effects on key economic and environmental variables in response to an improvement in energy efficiency in different sectors of the Scottish economy and to help developing understanding of how to interpret CGE results using other information, such as results of IO accounting exercises. However, aside from the qualification noted at the outset regarding the age of the dataset used for both the IO and CGE analysis, it is also important to note that the configuration of the model ( i.e. assumptions regarding behavioural relationships, labour and macroeconomic closures etc) will impact on results of energy efficiency simulations. This is demonstrated in the sensitivity analyses reported by Hanley et al (2008) and Turner (2008a). Of particular importance in examining rebound effects are factors determining the general equilibrium elasticity of demand for energy as prices change. As noted above, we have made some broadbrush assumptions in the absence of econometric estimates of key energy demands etc, a problem we aim to address to some extent through the current programme of research into rebound effects under the ESRC First Grants Initiative. However, sensitivity analyses conducted so far have begun to indicate what are likely to be key parameters on which to focus our efforts in this respect. For example, we have recently carried out (as yet unpublished) work extending the simulation work for the commercial Transport sector (the base simulation for which is reported in Tables A2.1 and A2.3). ¹⁶ This focuses on rebound effects for the key energy input of oil, shows that if one parameter, the elasticity of substitution between energy and non-energy intermediate inputs to production in this sector is raised from the current value of 0.3 to 1 ( i.e. unitary elasticity of demand), we get rebound effects of around 100% (and the disinvestment effect disappears) and if we raise it any further we get backfire. Similar changes would be expected if we were to increase the responsiveness of different elements of direct and derived energy demands to changes in prices in any one of the sectors for which results are reported here. This conclusion emphasises the need to improve the modelling infrastructure for Scotland, with attention to, and availability of appropriate data for the econometric estimation of key energy demand relationships.

A2.5 Extended analysis of the impacts of improved technological progress: the potential impacts of the existing policy emphasis on improving labour productivity

Our initial work in this component of the project has focussed on the impacts of improved energy efficiency, as this is often taken to be the natural target of attempts to improve technological progress where our concern lies with reducing reliance on energy and the generation of greenhouse gas emissions. However, in another work stream currently being carried out in our project under the ESRC 1 ^st Grants Initiative (in collaboration with Professor Nick Hanley at the University of Stirling), we have begun to look at the impacts of increasing labour productivity as another form of technological progress that may be expected to shift the economy onto a different path of development with respect to the CO2 intensity of GDP ( i.e. its position on what is known as the Environmental Kuznets Curve) .This stream of work is as yet incomplete and unpublished. However, our initial findings suggest that considering the environmental impacts of current policy actions to improve labour productivity may provide a very valuable contribution to the policy debate on meeting the challenges of climate change. In short, while, as the results and discussion above suggest, the impacts of improved energy efficiency on the CO2 intensity of GDP are ambiguous (due to the likelihood of rebound, and even backfire effects), our initial results suggest that the impacts of increasing labour productivity may be more predictable and the direction of effects less sensitive to parameters governing price responsiveness in the system. Broadly our results suggest that, while boosting all activity, including CO2 generation, increased labour efficiency is likely to shift the input mix in production away from energy towards labour, and the composition of activity at the aggregate activity in favour of activities that are more labour than energy intensive, so that the CO2 intensity of Scottish production is likely to fall overall.

Using the AMOSENVI energy-environment Scottish CGE model we simulated a 5% improvement in labour efficiency, which is also termed labour productivity. As with the 5% improvement in energy efficiency this was introduced to the model as an exogenous and costless increase in labour augmenting technical progress to all production sectors and to groupings of sectors. Results for introducing the shock to each of the aggregate groupings of sectors introduced in Tables A2.2 and A2.4 above in turn are reported in Table A2.5 below, and for the full 25 sector breakdown in Table A2.6.

The results in Table A2.5 and Tables A2.6 (comparable with Tables and A2.1 and A2.2 respectively for the energy efficiency simulations) suggest that increasing the productivity of the labour force would have an adverse effect on the policies in place to reduce the level of CO2. CO2 emissions increase along with the boost to activity. However, when we look at the CO2 intensity of production, which uses CO2 as the numerator and GDP as the denominator, we can see more positive results for the economy inline with both objectives of the Scottish administration. This is because, as activity increases, due to the falling effective price of labour (mirroring the process with increased energy and effective energy prices), producers shift their input mix in favour of labour.

With a 5% improvement in labour productivity we observe a positive change in GDP across all the 25 production sectors, except for the Sea fishing sector. As expected the level of CO2 emitted rises as a result of this efficiency improvement over the same time period. As labour productivity increases more output is produced which leads to higher emissions of CO2 at new levels of production for each sector or groups of sectors. While this is not a desirable outcome as the policy objective is to reduce the level of CO2 emitted over the long run period, there is a more positive result if we look at the CO2 intensity of Scottish production. With CO2 as the numerator and GDP as the denominator for this indicator we are looking at CO2 levels over the level of GDP. When there a positive number is reported, the level of CO2 is increasing faster to that of GDP, which shows that the 5% improvement in labour efficiency is having an adverse effect on the environment while productivity is increased. If a negative number is reported, this shows that while output across the economy is growing the levels of CO2 have not risen as fast as GDP.

An important point to note is that the GDP effects are significantly bigger in Tables A2.5 and A2.6 relative to their energy efficiency counterparts in Tables A2.1 and A2.3, with the exception of the cases where efficiency improvements are introduced to the Electricity sectors. This is largely explained by the fact that labour is a more important input to production than energy in most sectors. As noted above, in most cases, the absolute level of CO2 emissions increases. However, in a number of cases the greater growth in GDP under the labour efficiency shocks brings with it a bigger long run decrease, or smaller increase, in the CO2 intensity of Scottish production. For example, if the efficiency improvement is directed at the Communications, Business and Finance sector (which contains a number of the key sectors identified in the Scottish Government Economic Strategy), the long run decline in the CO2 intensity of Scottish production is 0.24% with the labour efficiency improvement, compared with 0.13% in Table A2.1 (energy efficiency). When efficiency improvements are directed at the Non Renewable Electricity sector, the increase in the CO2 intensity of Scottish production is 0.68% when this takes the form of an increase in labour productivity compared with 1.29% for energy efficiency.

However, it is important to bear in mind that improved labour productivity does increase CO2 emissions in most cases. The exceptions are where the efficiency improvement is aimed at Sea Fishing and Public and Other Services sectors (at least over the long run). Generally, over the long run, if all sectors experience a 5% improvement in either labour or energy efficiency, our initial results suggest that improved labour productivity gives better aggregate results in terms of GDP and the CO2 intensity of Scottish production, but not levels of CO2 production. However, if we focus the shock only on energy use sectors ( i.e. omit the five energy supply sectors), the results are mixed in terms of the CO2 intensity of production and the larger increases in GDP from improving labour productivity need to be set against larger increases in Scottish CO2 production. However, two points should be noted. First, Table A2.6 shows that at the aggregate level, if we shock all 20 non-energy supply sectors, the CO2 intensity of Scottish production falls. Second, and perhaps more importantly, some initial sensitivity analyses suggest that if we make it easer to substitute between different types of input in production (including labour and energy), the results in terms of the CO2 intensity of production become more favourable for labour productivity and less so for energy efficiency. Therefore, further research is required. Nonetheless, the initial results presented here will hopefully stimulate discussion and consideration of potential positive and negative spillover effects of existing labour productivity policies and objectives to addressing the problem of climate change.

Table A2.5 Short and Long Run Impacts on GDP and CO2 from a 5% Increase in Labour Efficiency in Each Sector of the Scottish Economy

Table A2.6 Short Run and Long Run Impacts on GDP and CO2 from a 5% Increase in Labour Productivity in Selected Groups of the Scottish Economy

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