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7 Co-benefits Analysis

This section examines the co-benefit initiatives in more detail, providing specific data on the potential changes in both GHGs and air pollution, along with analyses to determine the potential health and economic implications of the initiatives. In order to do this, certain scenarios have been developed. These are based on changes within the sector activities, rather than arbitrary percentage emissions changes. They range from modest through to ambitious. The results are all shown in the tables by prefixes, a plus sign indicating there is a cost, and the minus sign indication a negative cost, ie, a benefit.

7.1 Assumptions

In conducting this analysis, a number of simplifying assumptions have been made on the details on how emissions (and effects) would change from the various sources analysed. The test has been made on a range of options, all of which are reasonably significant in order to show the scale of the effects possible. For instance ± 10% changes in fuel use, or ± 25% changes in wood burner use. These percentages should not be interpreted too literally and are simply designed to show the effects associated with changes of this order. In the real world there are several more detailed factors to consider. For instance, changes of this magnitude would only occur over several years, or in the face of very dramatic policy or economic drivers. Furthermore there will be strong regional differences, particularly in the case of wood burner effects. Finally, there is a lack of detailed information on some important factors, such as just how might any change in wood burner or industrial boiler use occur in relation to efficiency, model, fuel type etc. Thus the results presented in this section should be taken as preliminary and indicative, designed to show the order of magnitude of the effects and their interrelationship, rather than be used as any definitive measure of a specific co-benefit.

7.2 Domestic heating

Three scenarios were examined, based on a range of changes to peoples’ fuel usage, mainly in switching to wood burners, to alternative fuels, or to electricity (shown in Table 7.1). In this analysis wood is assumed to be carbon neutral with respect to CO2 – that is the CO2 gas emissions attract zero cost, even though the changes within the scenarios are significant. However wood use also leads to an increased emission of methane, which is included in the costs, although this is not a major factor.

The scenarios are somewhat simplistic as they assume simple displacement features – that is switching fuels. For instance scenario 1 in Table 7.1 assumes an increase in wood burner use will result in reduced thermal electricity generation at the average rate described previously (6 kt/MW-year). The opposite displacement occurs in scenario 3. The assumption is that the fraction of the electricity supply sourced from thermal generation is 30%. It has been lower in the past, but is increasing.

7.2.1 Wood burner emission factors

Determining the appropriate wood burner PM10 emissions factor for use in this analysis is a difficult task. There are a very wide range of wood burner types and ages throughout New Zealand, with PM10 emissions factors that can range from less than 1 g/kg to over 30 g/kg. The variation is due to appliance type, appliance age, fuel type and operation method. The Ministry for the Environment and numerous Regional Councils have conducted extensive tests for various purposes, such as compiling emissions inventories and testing mitigation policies. Ideally, a detailed analysis could be conducted to use these tests and build a very detailed scenario of the effects of the various options involving change of wood burner usage patterns. However this would be inordinately complex and still subject to numerous assumptions about replacement rates, types etc.

For the purposes of this analysis the following assumptions have been made:

  1. The average national current PM10 emission factor for wood burners is taken as 12 g/kg. This was derived from the weighted average values in the two main emissions inventories from Auckland and Christchurch. The figure is reasonably consistent with that used by the Ministry for the Environment – based on a wider selection of burners – of 14–16 g/kg.
  2. The average national PM10 emission factor for newly installed wood burners (2008 and beyond) is taken as 4.6 g/kg. This is based on recommendations made by the Ministry for the Environment in a 2007 report on the Warm Homes project (MfE, 2007a). A recommendation is also made that this should be the emissions figure used in any airshed modelling (unless more accurate data are available).
  3. The average PM10 emissions factor for a ‘low-emissions’ burner is taken as 1.0 g/kg. This is an extreme case constructed for the purpose of the scenario. It is unrealistic in the sense that it is probably unachievable in practice, but it is included as an example of the theoretical optimum emissions levels that could be achieved with 100% compliant new low-emissions burners.

Table 7.1: Cost effects of various scenarios for domestic heating

View cost effects of various scenarios for domestic heating (large table).

The calculations in Table 7.1 assume that:

  1. the ‘existing models’ of wood burners have an average PM10 emission rate of 12 g/kg
  2. the ‘current models’ of wood burners have an average PM10 emission rate of 4.6 g/kg
  3. the ‘low emissions’ wood burners have an average of 1.0 g/k or 1/12th the PM10 emission rate as the existing models
  4. 50% of the total electricity consumption goes into space heating.

The GHG emissions analysis is also somewhat simplistic and contains the following simplifying assumptions. It is assumed that a 25% increase in wood burner use results in a simple 25% increase in the current rate of GHG emissions (scenario 1). This may not be realistic, since newer burners are likely to be more efficient than the existing models, consequently burning less wood and emitting less CO2 than current models. The data on this is not clear, and so this simple assumption is used. Conversely, it is assumed that if 25% of wood burners are decommissioned (scenario 2), the reduced CO2 emissions will be 25% of the total. In practice this might not occur since older, less efficient, wood burners are likely to be preferentially replaced. Thus the ‘25%’ should not be interpreted too literally. The ‘real’ effect on emissions might be somewhat higher or lower, but probably not be more than a few percent. A good deal more real-world data would need to be analysed to improve this estimate. However the point of the scenario is to show in crudely quantitative terms what can happen to the various emissions if wood burner usage is altered by a reasonably sized factor – in this case a nominal 25%.

A similar situation exists with the consequent decrease or increase in electricity usage. This factor is smaller, but again the point of the analysis is to show the order of magnitude shifts that are possible, and their scale compared to shifts in wood burner usage. These assumptions are simplistic, but need to be made in order to make the comparison with the shifts in health effects associated with each scenario.

The results show that a 25% increase in use of wood burners has a health effect cost ranging up to $70M depending on the type of wood burner used. There is only a greenhouse co-benefit, of up to $12M if low-emissions wood burner users have switched from electricity and gas.

If 25% of wood burner users switch to electricity (heat pumps, say), there is a benefit in air pollution costs of $183M, and an increase in greenhouse costs of $3M. This air pollution benefit is probably underestimated, since any practical measures taken by government to encourage users to switch to cleaner fuels would almost certainly be targeted at older, high-emissions burners. That is, rather than simply reducing the number of ‘average’ emission burners (12 g/kg), the measures would likely reduce emission further (more from burners > 12 g/kg). Without analysing specific policies it is not possible to calculate this explicitly, but such a replacement measure would have greater air pollution health benefits than are calculated here.

7.3 Transport

Three simple transport scenarios are examined. These can be very complex in detail, because subtle adjustments in the fleet profile, in regional differences, in fuel types, and in evolving technology can make significant differences. The scenarios given here can thus only be used as broad-scale indicators.

Table 7.2: Cost effects of various scenarios for transport

Scenario option

Details

Annual impact GHG (CO2 only)

Annual impact air pollution (PM 10 only)1

Net

1 Generally reduce travel

All vehicles VKT reduced by 10%

–1,260kt

–$32M

–0.21kt

–$49M

–$81M
(Benefit)

2 Displacement

Car VKT reduced by 10% and bus VKT increased by 5%

–908kt

–$22M

+0.03kt

+$6M2

–$16M
(Benefit)

3 Heavy trucks to rail

HCV VKT reduced by 20% and rail emissions increase by 20%

–440kt

–$11M

–0.23kt

–$43M3

–$54M
(Benefit)

  1. This impact is due to PM10 alone, which dominates the health effect. A more indepth analysis would include changes in emissions of other pollutants, but these are either essentially neutral (eg, CO), or not enough is known about the effects (eg, acetaldehyde and NOx – see text).
  2. Although buses are a small fraction of the fleet (1%), they emit more pollution per VKT (Vehicle Kilometres Travelled – about 8 times the fleet average). Such impacts can be avoided through retrofitting catalysts.
  3. This figure is an estimate only, since rail emissions were not explicitly evaluated in the HAPiNZ study. The increase in rail is assumed to be either (a) electric, or (b) new low-emissions diesel rolling stock.

The results in Table 7.2 show that almost any measure to reduce vehicle trips will have benefits for both greenhouse gas emissions and local air quality effects. The only increase (a modest $6M) occurs if bus use increases significantly (5%), using buses with current emissions rates. In practice this is an overestimate, since many bus fleet operators are meeting new lower emission standards. For instance if the bus fleet were to substantially meet the latest Euro 4 standard (a policy being enacted in Auckland), then the cost figure would go down, and perhaps even turn into a benefit.

7.3.1 Biofuels

A separate calculation has been made for various scenarios involving switching portions of the transport fleet to biofuels. This is a trend in many parts of the world, with considerable interest from the public, since biofuels are seen as a sustainable and carbon neutral resource. However it is not as simple as it may seem, and this report shows the results from a number of studies that can be used to analyse these scenarios (see Appendices B and C). Table 7.3 shows some summary results.

Table 7.3: Cost effects of various scenarios for switching to biofuels

Scenario option

Details

Annual impact GHG (CO2 only)

Annual impact air pollution (PM10 only)1

Net

Government target 3.4%

3.4% of all petrol and diesel is replaced with ethanol and biodiesel, respectively

–323kt

–$8M

–0.07kt

–$17M

–$25M
(Benefit)

E10

All petrol has 10% blended ethanol

–950kt

–$24M

+0.01kt2

+$2M2

–$22M
(Benefit)

B20

All diesel sold in New Zealand has 20% blended biodiesel

–720kt

–$18M

–0.42kt

–$99M

–$117M
(Benefit)

  1. This impact is due to PM10 alone, which dominates the health effect. A more in-depth analysis would include changes in emissions of other pollutants, but these are either essentially neutral (eg, CO), or not enough is known about the effects (eg, acetaldehyde and NOx – see text).
  2. This figure is low because petrol is responsible for only a small fraction of total PM10 emissions from transport (compared to diesel). According to the Ministry of Transport standard emissions model – NZTER – this factor is approximately 25. That is for a given sized vehicle, the diesel version emits 25 times more PM10 than the petrol version. On this basis all of the PM10 health effects are assumed to be due to diesel emissions. The use of E10 is estimated to have a slight negative effect due to an increase in NOx emissions because of its higher combustion temperature.

The results in Table 7.3 show that any switches to biofuels have a strong gain in reducing CO2 emissions, roughly in proportion to the amount of imported petrol and diesel replaced. Biodiesel blends also have a significant air quality benefit (although as discussed above, this is not well validated by current research). Ethanol in petrol blends probably does not have strong air quality benefits since (a) petrol vehicles are responsible for a small fraction of PM10 (the main health effects), and (b) probably result in increased NOx emissions which can exacerbate the health effects. This latter factor has not yet been fully researched in New Zealand.

7.4 Industry

The analysis for industry (categorised basically as all other combustion processes that are not (a) domestic heating, (b) transport, or (c) thermal electricity generation, is difficult. This is because there are so many different processes that have a great variability on both greenhouse gas emissions and local air pollution. The greenhouse gas inventory is calculated mainly on fuel use, but with some additional emissions information in certain industries because of the process involved. The air pollution emissions are estimated from resource consents and area/ population-based estimates.

Table 7.4: Cost effects of various scenarios for industry

Scenario option

Details

Annual impact GHG (CO2 only)

Annual impact air pollution (PM10 only)1

Net

1 Thermal electricity generation

Reduced by 10% (say through use of renewable sources)

–610kt

–$12M

–0.32kt

–$2M1

–$14M
(benefit)

2 Industrial efficiency

Energy efficiency in industry, including combined heat and power, giving 20% less fuel demand

–1,020kt

–$26M

–0.64kt

–$28M

–$54M
(benefit)

3 Fuel switching

Switching from gas/coal/electricity to wood as fuel – 5% of usage3

–255kt

–$6M

+0.16kt

+$7M2

+$1M
(cost)

  1. This is a very modest gain since although air pollution emissions are reduced, they mostly occur in areas where few people live, and so the public health exposure is low.
  2. This increases since most of these industries will be in urban areas where PM10 will increase public exposure.
  3. This assumes a simple 5% CO2 emission reduction which obviously represents different amounts of coal or gas.

The analysis for industrial sources is based only on CO2 for GHG, and only on PM10 for air pollution. It is accepted that there may be effects due to other GHGs and other air pollutants, but these are not included here since (a) the effects are relatively minor relative to the main CO2/PM10 emissions, and (b) there is not enough data available on the quantum of emissions, and in many cases their effects.

The results in Table 7.4 indicate that, as would be expected, any measures to reduce fossil fuel energy use in the industrial sector will have benefits for CO2 emissions reductions. These also have modest air quality health effects gains, but only if the process does not involve industry switching to using wood, or wood waste. (Some of this wood combustion emissions effect could be reduced if processes were fitted with modern emissions control technology – but this would only be applied to the larger emitters, and comes with a substantial financial cost.)