Key observations as to data inputs, methods, assumptions and interpretation

It is important to underline that the estimates of the COPI for biodiversity loss presented in Section 6.3.1 are “rough” estimates. The results are presented here as the final answer, but rather as intermediate answers to the questions posed, resulting from an approach and set of methods, clarifying areas that are considered important to focus on, and creating a solid basis for future research.

• There is a wide range of gaps in available data. There are more data available for certain regions, biomes and ecosystem services than for others. This therefore creates a cautionary note in too detailed an interpretation of the results – the limitations need to be borne in mind. As regards data availability:

o For biomes: more data was available for forestry biomes than for others, then scrubland, grassland and steppe. Least good was savannah, Mediterranean scrubland and tundra. This was partly addressed in the “fuller estimation” scenario by benefit transfer from one biome to another for ecosystem services where a relation could be established – though not for tundra as these were left an unfilled gap as difficult to argue for a particular transfer approach.

o For ecosystem services more data was available on provisioning services of food, fibre and fuel, climate regulation (carbon storage), water purification and regulation. Data for some biomes were available for air pollution regulation and water provision, but less systematically available. Cultural services and tourism was more difficult to get values in per hectare values (reflecting an intrinsic challenge), Several services has little information available – ornamental services, regulation of human diseases, temperature regulation, living comfort and technological development

o For value types (relating to the total economic value (TEV) classification. More information was available on “direct use value”, a bit less for “indirect use value” and less for non-use values (option, existence, bequest).

o For type of units for values (eg per hectare values or others): a large share of the literature presents values in other units – eg for tourism and recreation and existence values they are often in £, $ or EUR per person. Some are convertible, with care, to values per hectare, others less amenable. While certain values make sense on a per hectare level (eg provisioning of food, fibre, fuel, water provision and purification), values per unit area are less directly representative for tourism.

o The gaps help clarify an agenda for action for a phase 2 of a wider evaluation of economics of ecosystems and biodiversity.

• Different mechanisms are possible to fill the gaps – each have the strengths and

weaknesses. There is a trade-off between local explicit theoretical correctness (which would argue for not filling the gaps as no method to do so is arguably good enough) and the pragmatic need to come to an overall understanding and grasp of the size of the losses in economic terms, and the fact that gaps lead to the final picture being skewed due to what is there and what is not. To address this tension, two scenarios were used. More work is needed in the future on gap filling, benefit transfer and aggregation/scaling up.

• The choice of mechanism to fill the gaps is critical, as inevitably there will be

more gaps than literature based data points. For example, the multipliers from 2000 to 2050 are critical, as are multipliers based on expectations of ecosystems services for different land-uses. This area could be improved in the future by an analysis as to what drives the value over time (eg production of the service itself, to use (eg population), to “appreciation” of the use (eg rising by income, affected by scarcity etc).

• Also of great interest is the relationship between ecosystem quality (as measured

services provided. The analysis above assumes that the two vary proportionately or with a maximum function (see Chapter 5), and this helps to explain a large proportion of the overall COPI estimates. Empirical evidence of the relationships is plentiful, but quantitative causal substantiation is scarce yet.

• Similarly it will be important to do a careful analysis of where relationships between the loss of habitat/landuse area and ecosystem service provision are linear (eg provision of wood) – the core operational assumption within the COPI study - and where they are less sensitive to loss (eg in initial phases of forest loss of a large forest for tourism), where they are very sensitive (eg ecologically poor or fragile areas) and where there are non linearities or critical thresholds (eg level at which species populations cannot be maintained) is important (see Figure 6.7 for simplified schematic). The possibility and limitations of substitution and how this should be integrated into analysis are also important issues to explore further.

Figure 6.7 Relationship between changes in habitat area and change in ecosystem services

• There is also a range of different ways of arriving at the cost estimate, which can also influence the result. For example in some cases non-market estimates are very low (e.g. for recreation) compared to understanding of the scale of the market. A theoretically correct way may not lead to good answers in all cases unfortunately. It is important to remember that all numbers have their strengths and weaknesses, and it is the overall understanding of the magnitude of the processes that is of particular importance rather than a specific number from a particular case study. Note that part of the complication on the recreation, tourism and indeed other values based on willingness to pay (WTP) approaches (eg existence and bequest values) , is that they are less naturally amenable to translation into “per hectare” values. For a phase 2, it will be useful to clarify which values are most amenable to per hectare treatment, and which less, and how to address the latter issues.

• Some numbers can dominate the results – market values for provisioning services and carbon prices are more readily available than for non market prices. It is important to draw conclusions in light of this.

• Other numbers could potentially dominate the result, but careful treatment can avoid any undue influence – in the case of coral reefs it is clear that there are extremely high per hectare values for one area (where there is not just a quality diving area, but where there is infrastructure and also brand value), but this can hardly be applied to all areas. It is therefore vital to chose the right selection of case values upon which to base the analysis, identifying and stripping out “outliers” (though these are valuable kept for use as case studies) and developing a suitable representative “average” (see Annex 1). The aim has to be to have a representative average that could usefully err (marginally) on the conservative to avoid potential criticism.

• Spatial considerations are important to interpret the results. For example, when

looking at losses to a region in billion EUR or in % GDP impacts, it is useful to remember that some of the losses are due to lost carbon storage, and hence a global loss (given climate impact) rather than a local loss, felt locally (e.g. loss of non market forest goods, or water provision or purification).

• The analysis compares the future state with that of a reference point. This is a

useful mechanism to arrive at an order of magnitude test-estimate and develop insights on where losses occur and on mechanisms for estimation. A wider scenario based approach could complement the current approach.

• The analysis has been a marginal analysis, and even over the period to 2050, the

losses are relatively small in terms of land use changes. Part of this is due to the model used. What is missed by this approach is therefore the potential losses that become more exponential, when critical thresholds are passed. The COPI study has not looked at critical thresholds in the monetary evaluation (some insights presented in the physical impacts chapter 5). It will be valuable to look more at this in the future, and arguably combining with risks assessment could prove one useful way forward.

• Furthermore, the stepwise analysis (scenario drivers-> pressures-changes ) does

not allow a feedback of the economic impact results back into the OECD economic model, and hence losses to the economy related to ecosystem service losses from biodiversity losses do not link back to the OECD economic projections. Ultimately a feedback mechanism would be required (see figure 6.8). Figure 6.5 shows the different paths of (a) GDP growth and (b) population growth and (c) ecosystems and biodiversity losses (with associated ecosystem service losses) . Clearly as the natural capital is drawn down, and the level of services falls, society and the economy also benefit less. Under current GDP statistics some of the losses will be translated into GDP values directly (eg loss of output of fisheries will be seen, when substitution possibilities run out), other will impacts indirectly (as more expenditure on water purification is needed to compensate for loss of natural purification, taking money away from other foci) and a range will have no GDP impact (eg loss of cultural values, option values, existence or bequest values). A fuller analysis would allow a feedback to take into account changes in inputs to the economy from loss of ecosystem service outputs, and change on manmade inputs to compensate for the loss (Eg growth in water