understanding the performance of biodiversity offset markets: … · 2020. 6. 24. · 1 1...
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Understanding the Performance of Biodiversity Offset Markets: Evidence 1
from An Integrated Ecological – Economic Model 2
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Katherine Needham (University of Glasgow) 4
Martin Dallimer (University of Leeds) 5
Frans de Vries (University of Stirling) 6
Paul Armsworth (University of Tennessee) 7
Nick Hanley (University of Glasgow) 8
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Abstract 11
Biodiversity offset markets can incentivize landowners to take actions that benefit biodiversity. A 12
spatially explicit integrated ecological-economic model is developed and employed for a catchment in 13
the UK where offset buyers (house developers) and sellers (farmers) interact through trading offset 14
credits. We simulate how changes in the ecological metric and geographic scale affects the performance 15
of the offset market. Results show that the choice of the metric has a significant effect on market 16
liquidity and the spatial distribution of gains and losses in the “target” species. The results also 17
consistently reveal relatively higher potential welfare gains for developers than for farmers. 18
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We thank The Leverhulme Trust for funding this work under project RPG-2017-148, and members of 20
our advisory group for numerous helpful comments on the research. 21
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1 Introduction 27
Biodiversity offsetting is a widespread practice that aims to reconcile the mounting pressure for urban 28
development and agricultural expansion with the need to significantly reduce the impacts of these on 29
biodiversity (Ten, Treweek, and Ekstrom 2010). Biodiversity offsets provide measurable conservation 30
gains to compensate for significant, residual impacts on biodiversity as a result of new development 31
activities (BBOP 2009). The term “offsetting” often encompasses a wide range of mechanisms 32
including compensatory mitigation, in-kind compensation, mitigation banking, habitat banking, species 33
banking, and wetland banking (Lapeyre, Froger, and Hrabanski 2015). As of 2018, 13,000 offset 34
projects have been recorded worldwide covering at least 153,670 km2 (Bull and Strange, 2018). 35
However, the approach is often criticised, since a large number of studies that have shown that offset 36
schemes have failed to deliver no net loss of biodiversity (Robertson 2004; Burgin 2008; Quétier, 37
Regnery, and Levrel 2014). Of particular ecological concern are the implications of offsetting for 38
changes to the functionality of restored systems, the longevity (security) of offset benefits, and time 39
lags between destruction from development and creation of the offset sites (Gibbons and Lindenmayer 40
2007; Maron et al. 2012). Restoration studies have only taken place on a limited range of habitats 41
(grassland and wetlands), which is a key reason why the implications of offsetting for other habitats 42
remain poorly understood (Evans et al. 2014). Perhaps the most contentious area of biodiversity 43
offsetting is what constitutes as an offset, and how to determine ecological equivalence between sites. 44
Across the disciplines of economics and ecology, these issues are seen as critical factors determining 45
the success of offsetting as a policy instrument (Heal 2005; Bull et al. 2013). 46
In this paper we are specifically interested in markets for biodiversity offsets. These are created when 47
multiple buyers and sellers of offset credits interact with others through a trading process. This creates 48
a setting in which landowners can choose to manage land for conservation, generating offset credits 49
which can then be sold to a developer who is required to mitigate development impacts. By establishing 50
an appropriate rate of exchange between sellers and buyers, markets can, in theory, achieve no net loss 51
of biodiversity within some defined area at least cost. By creating economic incentives for conservation, 52
market mechanisms encourage private landowners and firms to take costly actions that benefit 53
biodiversity (Kangas and Ollikainen 2019). Market forces within a biodiversity offset mechanism 54
should steer land allocation choices in the direction of greater efficiency, given some constraint on 55
overall biodiversity change. Buyers will not offer more for a credit than the value to them of land for 56
development, and sellers will require no less than the opportunity costs of creating offsets (through 57
foregoing some other form of land use such as crop production). Such offset markets could therefore 58
allow biodiversity conservation targets to be met at the lowest cost since only those suppliers whose 59
supply prices are competitive will be rewarded with payments for supplying offsets. 60
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The objective of this paper is to assess the social welfare implications and ecological impacts of what 61
we consider to be two key design parameters when developing markets for biodiversity offsets: the 62
ecological target which constitutes the biodiversity offset, and the size of the market for trading i.e. the 63
number of players. This is based on the identification of key offset market design parameters in 64
Needham et al. (2019). 65
We first develop a general model of a biodiversity offset market in the context of a no-net-loss 66
regulation. We then construct an integrated ecological-economic model of a biodiversity offset market 67
that builds spatially explicit biodiversity offset supply and demand curves. These curves capture the 68
spatial variations in both the costs of supplying biodiversity offsets and the demand for offsets related 69
to the value of new housing developments across the landscape. Through the identification of the 70
market-clearing price for the biodiversity offset, our model can then be used to assess the economic 71
welfare implications of such a market, and determine who benefits from the design of such schemes. 72
We can also consider the ecological implications of such a market across the landscape to identify where 73
the gains and losses in biodiversity take place. The model is parameterized for a north-eastern region of 74
England: The North Pennines, Tees Valley and North Yorkshire Moors in the context of UK 75
Government’s plan to implement biodiversity offsetting as part of the recently published 25 Year 76
Environment Plan (HM Government 2018). Our model captures (i) how the choice of the ecological 77
metric for trading has significant implications on the scale and distribution of new development, (ii) 78
how the ecological metric has significant impacts on ecological gains and losses of species not covered 79
under the no net loss regulation, (iii) how changing market boundaries changes spatial heterogeneity in 80
the supply and demand curves, and (iv) subsequently shows that a larger geographic scale benefits 81
developers, however, smaller-scale markets are ecologically more beneficial. 82
The remainder of the paper is organised as follows. In the next section, we discuss the related literature 83
and summarise some relevant policy experience with offsetting. In Section 3 we develop our general 84
model of biodiversity offset trading. Section 4 details the empirical approach, including an overview of 85
the integrated economic - ecological model, our case study, data sources and data development. Results 86
are presented in Section 5 and the discussion and conclusion in Sections 6 and 7. 87
2 Biodiversity offsetting: what do we know already? 88
Biodiversity offsetting is the final step in the mitigation hierarchy: this hierarchy is crucial for all 89
development projects aiming to achieve no overall negative impact on biodiversity or a net gain (also 90
referred to as a No Net Loss and the Net Positive Approach). First developers must first actively avoid, 91
minimize and reduce their impacts through restoration actions at the project site (McKenney and 92
Kiesecker 2010). In all cases, biodiversity offsets should meet the criterion of additionality: only those 93
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actions that would have not have otherwise occurred should be counted as a biodiversity offset (Laitila, 94
Moilanen, and Pouzols 2014). From a developer’s perspective, it is argued that this market approach is 95
more cost-efficient, quicker and more certain than traditional planning regulations (Levrel, Scemama, 96
and Vaissière 2017, Hannis and Sullivan 2012). The market element allows developers to purchase “off 97
the shelf” offsets where ecological gains have already been established and verified, rather than 98
developing their own restoration sites (Bonds and Pompe 2003). The use of offset banks in the supply 99
of offsets helps deal with one widely-recognised problem with offsets, namely that the ecological 100
consequences or restoration actions are uncertain (Maron et al. 2012). 101
Two key offsets schemes are the US Wetland Mitigation Banking (WMB) market and BioBanking in 102
New South Wales, Australia. The US (WMB) market developed as a response to Section 404 of the 103
Clean Water Act (1977) which required ‘no net loss of wetland acreage and function'. Developments 104
which impacted on wetlands were required to hold credits from investment in completed wetland 105
restoration (Hough and Robertson 2009). The first wetland mitigation banks were established in the 106
1980s. Conservation Banks were approved in the 1990s and sought to protect individual species rather 107
than wetlands functionality. As of 2020, there are currently 1482 mitigation banks and 207 conservation 108
banks in the USA (US Army Corps 2020). WMB credits were initially based on habitat acreage but 109
more recent approaches have sought to take wetland functioning into consideration, accounting for the 110
effects of offsetting actions on water quality, flood storage, and flow reductions and habitat quality. 111
Conservation Bank credits take into account acres of habitat needed to support a particular species or 112
breeding pair. 113
A significant change in the WMB market was the introduction of the 2008 Mitigation Rule which 114
prioritised mitigation banking as the first priority for compensation, ahead of permittee responsible 115
mitigation and out of kind mitigation. This resulted in a significant increase in the purchase of credits 116
and demonstrates how a regulatory change placing emphasis on mitigation banking above other 117
compensatory measures can have a significant positive benefit on market activity. Additionally, a 118
watershed approach to banking was introduced with an emphasis on using existing watershed plans to 119
inform compensatory mitigation decisions. New South Wales, Australia developed BioBanking 120
following the Environment Protection and Biodiversity Conservation Act (1999). The main aim of the 121
scheme is to integrate biodiversity into land-use planning for high growth areas, particularly within the 122
coastal zone (Scanlon 2007). The offset metric is based on the habitat hectares approach, with 123
adjustments to take into account state and national priorities, regional value, landscape value and the 124
threatened species response to proposed management actions (Office of Heritage New South Wales 125
2014). Whilst 53 BioBanking agreements have been secured (as of May 2016) many believe the scheme 126
has not been as successful as expected: the current market has struggled to secure offers from potential 127
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suppliers, and thus is failing to produce the cost-savings initially anticipated. In response there are calls 128
to make the scheme mandatory rather than voluntary to encourage offset creation (Dupont 2017). 129
Biodiversity offset markets are in the developmental stage within the UK. Biodiversity is predominantly 130
protected through the European Habitats and Birds Directives (92/43/EEC and 2009/147/EC). However, 131
compensation for damage to sites that are not covered by these Directives is relatively weak (Tucker et 132
al 2013) leading to a decline in biodiversity (Hayhow et al 2019). The UK Government launched six 133
pilot biodiversity offset schemes between 2012-2014. Similar to Biobanking in Australia, the offset 134
metric was based on the habitat hectares approach with trading ratios used to adjust for the risks 135
associated with restoring or expanding certain habitats and to take into account locational differences 136
between the offset site and development site (Defra 2012). Within the UK pilot areas, developers were 137
required to provide compensation for biodiversity loss and could choose to do so through offsetting. 138
Follow-up reviews highlighted that the potential benefits of offsetting would only be realised if driven 139
through mandatory offsetting (Baker et al 2016; Lockhart 2014; Sullivan and Hannis 2015). The 140
approach is currently being revisited as part of the UK Government’s approach to revising land 141
management through the 25 Year Environment Plan (HM Government 2018). This includes embedding 142
the “net gain” principle for development and creating or restoring 500,000 hectares of habitat. 143
Specifically, it identifies that this net gain could be delivered by “habitat banks, land-owners or brokers 144
as part of a flexible market”. 145
Despite the increasing policy interest in biodiversity offsets, and a growing ecological literature, 146
relatively little attention has been paid to the idea by environmental and resource economists when 147
compared to other incentive-based mechanisms, such as payments for ecosystem services and tradable 148
pollution permit markets for air and water pollution (Coralie, Guillaume, and Claude 2015; de Vries 149
and Hanley 2016). From a resource economist's perspective, markets for biodiversity offsets face some 150
of the same design issues as the tradable pollution permit markets including credit definition, market 151
participation, trading ratios and administration (Bonds & Pompe, 2003; Wissel & Wätzold, 2010). 152
Needham et al. (2019) review experience from pollution permit markets and identify some fundamental 153
parameters that seem most relevant for the design of offset markets. The most relevant of these from 154
the perspective of the current paper are: 155
(a) Policy targets and metrics: 156
When establishing a pollution permit-trading scheme, the first stage is for the government or regulatory 157
agency to determine the cap (or limit) on overall emissions. While voluntary trading can occur (for 158
example, in the context of carbon offsets and forest planting), imposing a regulatory cap greatly 159
increases the volume of trading. Efficient environmental markets require goods to be grouped into 160
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simple, measurable, standardised units in order so that they are fully exchangeable, i.e., tradable. For 161
biodiversity offsetting, a vague cap dictating “no net loss of biodiversity” leaves room for ambiguity 162
and is difficult to measure and quantify. This undermines effective monitoring and enforcement, with 163
the prospect of poor market functioning in terms of lack of both cost-effectiveness and environmental 164
effectiveness. A clear metric for how offsets are measured is thus required. What exactly this metric 165
should be is much less obvious. 166
(b) The trading ratio: 167
This governs the rate of exchange between offsets at different points in space (between the supply site 168
and the demand site for any prospective trade). Once the metric for an offset market has been established, 169
trading ratios are then required to determine the ecological rate of exchange between sellers and buyers 170
at different points in space. Ideally, such trading ratios need to reflect the many potential sources of 171
ecological heterogeneity implicit in the spatial definition of the credit, and thus the ecological 172
consequences of moving pressures on the biodiversity metric (positive, for offset supply and negative, 173
for offset demand) across different locations within the policy area. 174
(c) Market scale and trading volume: 175
For biodiversity-offset markets, erratic price signals will increase the uncertainty on investor returns 176
from development and, in turn, increase development costs. Higher offset price volatility may also deter 177
potential suppliers from incurring the (opportunity) costs needed to create credits. The implication is 178
that larger offset markets would be preferred to smaller markets on grounds of economic efficiency. 179
However, creating a large market geographically implies a greater heterogeneity in the biodiversity 180
value of conservation actions at different sites, making it harder to measure and ensure equivalence in 181
ecological gains and losses across space. This implies that there will likely be a case-specific optimal 182
trade-off between having a larger spatial scale which increases participation, and a smaller spatial scale 183
which makes the determination of ecological equivalence easier to model and control for via trading 184
ratios. Moreover, ensuring transactions costs of trading are kept as low as possible seems likely to be 185
important. 186
Fernandez and Karp (1998) provided one of the first economic analyses of wetland banking, focussing 187
on the optimal level of investment in wetland mitigation banking in the USA. Using an ecological-188
economic model they considered how restoration costs, restoration potential and the market value of 189
offset credits determine how much a landowner should invest in a wetlands bank. Following ecological 190
expectations, the authors found that as restoration costs reduce, it is more likely a landowner will restore 191
his whole land parcel. Increases in ecological uncertainty increase the cost of a credit and restoration is 192
harder to justify under higher interest rates. Other economic analyses have focussed on the implications 193
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of using different ecological metrics to determine what constitutes an offsets. Heal (2005) compared 194
the habitat hectares approach with species banking to protect the red-cockaded woodpecker and found 195
that more restrictive definitions of the ecological metric increase the price of a credit. Furthermore, 196
choosing the incorrect ecological metric nullifies the ecological benefits of the trading system. These 197
early economic analyses highlight the significant challenges associated with determining the unit of 198
currency when developing an offset market, and how the choice will be likely to significantly affect 199
ecological outcomes. 200
Other modelling approaches have focused on the impact of time lags related to restoration potential of 201
habitats in relation to NNL of species and also their effect on the market (Bendor 2009; Drechsler and 202
Hartig 2011). Time lags have been shown to increase the restoration costs for those wishing to develop 203
biodiversity offsets and greater fluctuations in credit supply prices (Drechsler and Hartig 2011). 204
Solutions to this have been offered regarding the release schedule of credits, with a balance needing to 205
be met between releasing enough “early release credits” (credits which have not yet reached the full 206
restoration potential) to foster investment into offset sites and not over releasing credits which leads to 207
a future net-loss in habitats and species (BenDor, Guo, and Yates 2014). 208
Implications of the trading ratio on market functioning have also been a key theme within the economics 209
literature. Bonds and Pompe (2003) provide one of the first economic analyses for calculating the 210
trading ratio to adjust for the functionality and location of wetland credits. Further work has been 211
undertaken in the ecological literature by BenDor et al (2009) Bruggeman et al (2005) Bull et al (2014) 212
Laitila et al (2014) and Moilanen et al (2009). However, there is still no single accepted method of 213
calculating the trading ratio in biodiversity offset markets. 214
Further research has concentrated on linking economic and ecological models to examine whether offset 215
markets offer opportunities for biodiversity conservation. Doyle and Yates (2010) provide an economic-216
ecological model of ecosystem service markets following a no net loss principle. A focal point in their 217
analysis is the scale of the market by assessing how (free) entry of mitigators (i.e., suppliers of offset 218
credits) affects ecosystem restoration. Their results indicate that as additional suppliers enter the market, 219
the size of an individual restoration project decreases but the total amount of mitigation increases, thus 220
increasing credit supply. Their results also indicate that using a single metric to capture ecosystem 221
functioning will result in loss of some ecosystem functions, stressing the need to further understand the 222
relationship between restoration and multiple ecosystem service functions. Most recently Kangas and 223
Ollikainen (2019) develop a model to analyse a market for biodiversity offsets with a focus on 224
restoration potential of three habitats: wetlands, forests and agricultural habitats in Finland. Credits are 225
developed as a result of restoration measures which would not have otherwise occurred, and restoration 226
potential is assessed based on expert assessments and the literature. Demand is based on predictions for 227
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future land use change. The model considers how market equilibrium adjusts in response to trading 228
ratios, the focus habitat for restoration, and uncertainties associated with restoration outcomes. Results 229
showed that all three of these aspects impacted the market equilibrium price in theoretically anticipated 230
ways: offset prices were higher when restoration actions were more costly and uncertainty high. 231
Building from the above literature, our research uses an integrated ecological-economic modelling 232
approach to explore first theoretically, and then empirically (for a specified case study), the impact of 233
two design parameters on equilibrium for a biodiversity offset market. Based on the identification of 234
key design parameters in Needham et al. (2019), we contrast economic and ecological market outcomes 235
under varying species-specific ecological policy targets and varying market size. Specifically, our 236
modelling approach allows us to develop spatially explicit supply and demand curves for biodiversity 237
offsets. This allows us to test how the choice of the ecological metric alters the market-clearing price 238
of an offset, and subsequently the gains to buyers and sellers from trading. Secondly, we compare the 239
market-clearing price and subsequent welfare implications when the market size is reduced from one 240
large planning region to three smaller regions. This builds on the work of Doyle and Yates (2010) by 241
adding spatial and cost heterogeneity to the model with offset suppliers and developers requiring offsets 242
having varying marginal costs (the empirical modelling by Doyle and Yates assumed homogeneity in 243
costs). In addition, our modelling framework allows us to assess how land-use and specific species 244
distribution and abundance shift within the case study region as a result of offset trading. 245
3 A General Model of Offset Trading 246
Consider a region where land can be divided into three possible uses which are mutually exclusive at 247
any point in time: agriculture, development for new housing, and conservation. Within such a setting, 248
it is assumed that there are a 𝑛 farmers who own the land and 𝑚 developers who wish to acquire land 249
for housing development. We further assume that there are a sufficient number of farmers and 250
developers so that no individual in either group has market power, i.e., all are price-takers in their 251
respective output markets as well as the market for offsets. 252
Farmers’ default land use is assumed to be for agricultural purposes in the form of crop or livestock 253
production. However, farmers can offer up some of their land for conservation, where conservation land 254
(newly created wildlife habitat, for instance) earns offset credits depending on the biodiversity metric 255
used. The farmer’s optimization problem entails maximizing the profits derived from agricultural 256
production plus the value of any offset sales. Every hectare given up to create new habitat means one 257
less hectare for agricultural production. We assume that this conversion cost to the farmer consists 258
solely of the opportunity costs of foregone agricultural output. From an agricultural perspective land is 259
of variable quality, measured in terms of agricultural output per hectare. Ranking all land owned by any 260
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one farmer along this gradient yields a continuous, upward sloping supply (marginal cost) curve of 261
creating new offset credits. The farmer maximises profits by choosing to supply the quantity of offsets 262
which equates the marginal cost of creating new habitat (i.e., lost agricultural profit at the margin) with 263
the offset price. If agricultural prices rise, then the opportunity cost of creating new habitat rises, and 264
so the supply curve for each individual farmer (hence aggregate supply) would shift up. 265
House developers face land values for housing that varies spatially across the region. Each developer 266
knows that, depending on the conservation value of land, each hectare acquired for new housing 267
development requires a number of offset credits to be purchased from an offset provider or offset bank. 268
Ranking development options according to their expected housing value to this developer from highest 269
to lowest yields a downward sloping demand curve for offset credits. The developer chooses to buy a 270
quantity of credits which equates their individual demand curve with the market price of offsets.1 If the 271
expected average house price in the catchment rises, this would increase the developer’s willingness to 272
pay for any potential housing site, implying a shift of the demand curve for offsets to the right. The 273
slope of the aggregate demand curve depends on the heterogeneity in the derived demand for offsets, 274
i.e., willingness to pay for land for housing development. If each hectare of habitat created can be 275
heterogeneous in ecological terms, then this means that each offset created or demanded has a different 276
ecological value. On the demand side, higher ecological value land requires credits of higher value to 277
be purchased to offset the impacts of development on biodiversity; on the supply side, higher value 278
ecological land generates more valuable credits when new habitat is created. 279
Assume for the moment that we have a single ecological indicator which is the focus for policy. Then 280
we can think of the value of any given credit supplied to the market to be ℎ𝑒𝑖, with ℎ referring to the 281
amount of hectares and 𝑒𝑖 indicating the ecological value at site 𝑖. On the demand side, a developer 282
must obtain a number of offsets equal to a value ℎ𝑒𝑗, where 𝑒𝑗 reflects the ecological value of site 𝑗 283
which is damaged by the development. Assuming a development of one hectare in size, the developer 284
must ensure it purchases appropriate offsets such that ℎ𝑒𝑖 = ℎ𝑒𝑗. From this we can define the offset 285
trading ratio between any two sites 𝑖 (supply side) and 𝑗 (demand side) as:2 286
(1) 𝑒 ≡𝑒𝑖
𝑒𝑗. 287
1 This could mean that some developers choose to buy no credits and not develop any housing. 2 If there is more than one ecological indicator, then there are multiple trading ratios between sites, one for each
target.
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We are now in a position to develop a simple general market equilibrium framework to highlight the 288
core functioning of the market for biodiversity offsets. This not only allows us to draw some clear 289
comparative statics results, but also provides a solid basis for the actual case study implementation later. 290
We will do so by building on Doyle and Yates (2010) but extend their model in two important ways. 291
First, our model captures heterogeneous opportunity cost of land use. Second, given this heterogeneity, 292
we will derive explicit demand and supply functions for three wading bird species. 293
Given a number of 𝑛 farmers, the quantity of hectares (offsets) “produced” by an individual seller can 294
be represented by the function ℎ(𝑛, 𝑒, 𝑝, 𝑐𝑘), where 𝑝 is the offset price, 𝑐𝑘 is the opportunity cost of 295
land use of type 𝑘. Aggregating across all farmers, the total amount of land supplied for conservation, 296
𝐻𝑆, is then: 297
(2) 𝐻𝑆(𝑛, 𝑒, 𝑝, 𝑐𝑘) = 𝑛ℎ(𝑛, 𝑒, 𝑝, 𝑐𝑘). 298
On the demand side there are a total of 𝑚 developers. These developers must buy a number of hectares 299
(offsets) to offset the degradation of each hectare ℎ due to development. More specifically, for each 300
hectare degraded it must buy an amount 𝑒ℎ, where ℎ(𝑚, 𝑒, 𝑝, 𝑟), where 𝑟 is the value of land for housing 301
in the development area and 𝑒 the trading ratio as specified in Eq. (1). The total amount of hectares 302
demanded, 𝐻𝐷, for development is then: 303
(3) 𝐻𝐷(𝑚, 𝑒, 𝑝, 𝑟) = 𝑚𝑒ℎ(𝑚, 𝑒, 𝑝, 𝑟). 304
The aggregate demand curve for offset credits, representing a particular set of spatial values for the 305
value of land for housing development, and the aggregate supply curve for offsets, showing the value 306
of foregone profits from farming, the offset market clears at an equilibrium offset price 307
𝑃∗(𝐻∗(𝑛, 𝑚, 𝑒, 𝑐𝑘 , 𝑟)). This generates an equilibrium amount of hectares 𝐻∗ for biodiversity offsets 308
such that 𝐻𝑆(𝑛, 𝑒, 𝑝, 𝑐𝑘) = 𝐻𝐷(𝑚, 𝑒, 𝑝, 𝑟). 309
Knowing the expression of the equilibrium price of offset credits, 𝑃∗, we can do some comparative 310
statics exercises to determine how the equilibrium price responds to changes in the identified 311
determinants. First, a change in the market size governs the offset equilibrium price in a straightforward 312
manner. An increase in the number of sellers, 𝑛, would increase the amount of hectares supplied to the 313
market for offsetting, which would have a downward pressure on the equilibrium price. In contrast, the 314
equilibrium price would increase if demand would be enhanced through an increase in the number of 315
buyers, 𝑚. These two effects are summarized by Eqs. (4a) and (4b), respectively: 316
(4𝑎) 𝜕𝑝∗
𝜕𝑛=
𝜕𝑝∗
𝜕𝐻∗
𝜕𝐻∗
𝜕𝑛< 0 317
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(4𝑏) 𝜕𝑝∗
𝜕𝑚=
𝜕𝑝∗
𝜕𝐻∗
𝜕𝐻∗
𝜕𝑚> 0 318
Second, a rise in the opportunity cost of land use of type 𝑘 , implying more profit foregone from 319
agricultural production, has a suppressing impact on the total amount of land supplied at any price. This, 320
in turn, would push up the equilibrium price of offsets for a given demand for offsets: 321
322
(4𝑐) 𝜕𝑝∗
𝜕𝑐𝑘=
𝜕𝑝∗
𝜕𝐻∗
𝜕𝐻∗
𝜕𝑐𝑘> 0 323
Third, higher expected house prices would attract more land being demanded for development. For a 324
given supply of land, this would increase the equilibrium offset price: 325
(4𝑑) 𝜕𝑝∗
𝜕𝑟=
𝜕𝑝∗
𝜕𝐻∗
𝜕𝐻∗
𝜕𝑟> 0 326
Finally, a change in the trading ratio between site 𝑖 and 𝑗 can originate from a change in the ecological 327
value of either site. More specifically, following the definition of the trading ratio in Eq. (1), an 328
increasing trading ratio can be the result from a higher (lower) ecological value of site 𝑖 (𝑗). If the 329
ecological value of site 𝑖 increases, this reduces the number of offset credits a developer needs to buy, 330
since each credit now delivers more biodiversity.. The same effect applies if the ecological value of site 331
𝑗 decreases. In this case too the developer needs to buy less credits to offset each hectare of land on 332
which development occurs, since the land converted to development is ecologically less valuable. This 333
reduction in offset credits demanded implies that the equilibrium offset price decreases with an 334
increasing trading ratio:3 335
(4𝑒) 𝜕𝑝∗
𝜕𝑒=
𝜕𝑝∗
𝜕𝐻∗
𝜕𝐻∗
𝜕𝑒< 0 336
4 An Integrated Ecological – Economic Model of a Biodiversity Offset Market 337
4.1 Model Overview 338
In the previous section, we developed a general model for a biodiversity offset market. We now adapt 339
this model and apply it to our case study, using integrated ecological-economic modelling. Our 340
3 In a similar vein, the opposite effect occurs when the trading ratio as defined in Eq. (1) decreases through either
a decrease (increase) in the ecological value of site 𝑖 (𝑗). In both these situations the developer’s demand for offset
credits increases, pushing up the equilibrium offset price.
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integrated model seeks to maximise the joint agricultural and development profits of landowners across 341
a case study region, subject to a regulatory limit of no net loss of the ecological target. Each landowner 342
manages a single 1 by 1 km land parcel and has three options: develop land for housing, provide 343
biodiversity offsets, or maintain the current land use. We assume that landowners maximise profits from 344
land use for each parcel. This profit maximization is subject to a number of constraints: land that is 345
already developed for housing can only remain in a developed state; agricultural land can either remain 346
as agricultural land or the farmer can undertake actions which benefit the ecological target and thus 347
generate biodiversity offset credits. If a landowner chooses to develop, he must hold the relevant number 348
of offset credits to ensure no net loss of the ecological target in the land parcel. 349
Our empirical approach consists of three stages. Firstly, an ecological model predicts the current level 350
of our ecological target variable across the case study region, based on current land use. This provides 351
us with a no net loss baseline for the ecological target. Secondly, the ecological model is used to estimate 352
potential changes in the ecological target as a result of landowners undertaking actions which benefit 353
the ecological target, thus generating the potential number of biodiversity offset credits a land parcel 354
could supply. We then determine the profitability of each land parcel under each land-use options, i.e., 355
development, offset provision or current land use. By integrating this profitability with the offset 356
requirements, supply and demand for offset credits for each land parcel is determined. Finally, we model 357
optimal, sequential trades (through a Walrasian auctioneer) based on derived spatially derived demand 358
and supply curves and the no net loss policy goal. This establishes a market-clearing price for the 359
biodiversity offset. A schematic of the modelling process is provided in Figure A in Appendix 1. 360
4.2 The Case Study 361
We apply our model of biodiversity offsetting to a UK case study region broadly known as the Tees 362
Valley, Pennine Uplands and North York Moors (Figure 1) The case study region covers an area of 363
approximately 5400 km2 and encompasses a range of habitats and land use types, from upland moors 364
in the west of the region, low lying agricultural land throughout the central region and increasing 365
urbanization in the east at the coastal margin. The case study region includes three Special Protection 366
Areas (SPAs) designated under the EU Birds Directive and three Special Areas for Conservation (SACs) 367
designated under the EU Habitats Directive. The North Pennine Moors SPA and SAC covers 1030 km2 368
and is designated for its upland dry heaths (heather and blanket bogs) and calcareous grasslands. The 369
North Yorkshire SPA covers 441 km2 and is designated for its populations of Merlin (Falco 370
columbarius) and Golden Plover (Pluvialis apricaria). In addition, it contains the largest tract of heather 371
moorland in England. The North Pennine Moors SPA encompasses extensive tracts of semi-natural 372
moorland habitats including upland heath and blanket bog. It is designated for its populations of Hen 373
Harrier (Circus cyaneus), Merlin (Falco columbarius), Peregrine (Falco peregrinus) and Golden Plover 374
13
(Pluvialis apricaria). The Teesmouth and Cleaveland Coast SPA is classified for its breeding Little tern, 375
passage Sandwich tern, wintering Knot and Redshank and an assemblage of over 20,000 wintering 376
waders (JNCC 2020). 377
The case study was chosen based on the development pressures within the Tees Estuary portion of the 378
case study which includes the Teesmouth and Cleveland Coast SPA (Planning area 1 in Figure 1). The 379
Stockton-On-Tees Economic Strategy (2017 – 2032) identifies the need to maximise the river and port 380
as key economic assets (Stockton-On-Tees Borough Council 2016) with further expansion of the port 381
infrastructure and the chemical and process industries which it supports, placing further pressures on 382
these coastal habitats (Simpson 2011). Within the Tees Valley, an increase in 27,000 households is 383
projected by 2039 requiring an increase in the affordable housing stock of 1,832 to 2,106 dwellings per 384
annum (Ferrari and Dalgleish 2019). Amongst this intensive development pressure is the SPA made up 385
of a complex of discrete sites, with additional non-designated areas also used for foraging and roosting 386
by wading birds. The area has been highly modified by human activities, with over 90% of intertidal 387
habitats lost to land claim (Smith 2011). The competing pressures for development and conservation of 388
biodiversity make this an ideal study region in which to test our general model of biodiversity offsets. 389
390
391 Figure 1: Tees Valley, Pennine Uplands and North York Moors case study region. Inset map depicts the 392 location of the case study on the North East coast of England. A landowner in our study correlates with a 393 single 1 by 1 km grid or land parcel. 13 Local authorities are labelled on the map which forms the three 394 planning regions: Planning area 1 (Hartlepool, Darlington, Stockton on Tees, Middlesbrough and Redcar 395
14
and Cleveland), Planning area 2 (Northumberland, Sunderland, Country Durham and Eden) and Planning 396 area 3 (Richmondshire, Hambleton, Ryedale and Scarborough). Contains Natural England data on English 397 SPAs issued under Open Government licence v3.0 unless otherwise stated © Crown Copyright 2019. 398 Contains OS data 1:250 000 Scale Colour Raster © Crown Copyright 2019. 399
400
4.3 Market Design Parameters 401
As highlighted in the introduction, the first stage of developing a biodiversity offset market should be 402
the identification of the policy target in order to determine the ecological metric which denominates an 403
offset credit. Based on the international importance of the case study region for supporting breeding, 404
roosting and overwintering bird populations, the policy target for our modelling approach is NNL of 405
each of three wading bird species: the Eurasian oystercatcher (Haematopus ostralegus), Eurasian 406
curlew (Numenius arquata) and the northern lapwing (Vanellus vanellus). These species were chosen 407
based on national priorities for wader conservation: all three species are recorded as ‘Near Threatened’ 408
on the international IUCN Red List4 of Threatened Species (Bird Life International 2019) Of particular 409
concern is the decline in curlew populations, with UK populations declining at the steepest rate recorded 410
throughout the curlew’s range in Europe. Population declines are linked to a loss and degradation of 411
breeding habitat as a result of changing agricultural practices, such as the conversion of traditional 412
meadows to intensive grassland monocultures (Franks et al. 2017). One model of biodiversity offsetting 413
will be constructed for each species separately, that is each model focusses on no net loss of a single 414
target species. This follows the recommendations of Needham et al (2019) which require that the 415
biodiversity offset market to be based on a simple, measurable ecological metric which can be 416
denominated to standardised units. This approach allows us to ensure no net loss of the target species, 417
but to also consider what we call the “unintended consequences of trading”: what are the ecological 418
impacts on the two species not considered under the no net loss policy target? 419
In addition to our policy target, the regulator also sets a “preferred land management practice” for 420
generating offset credits. The land management practice is one which is expected to benefit the target 421
species. By replacing the current land use with this new, ecologically-preferred land management 422
practice, the expected abundance of the target species will increase, hence generating biodiversity offset 423
credits. The number of credits created in each parcel of land depends on the “ecological productivity” 424
of that parcel, defined as the estimated increase in abundance of the target species if a parcel at a specific 425
location is converted to this preferential land use type. For our model, we adopt a preferred land 426
management practice of “improved grassland” without livestock. This change in land management 427
practice will only take place on agricultural land patches currently farmed for crops or livestock, as such 428
improved grassland is more beneficial to the three target species than the agricultural practices of crop 429
and/or livestock production. 430
15
We compare the effects of market size on the ecological and economic efficiency of the offset market 431
by conducting our analysis at two spatial scales for the oystercatcher policy target. The analysis is first 432
undertaken across the full case study region so that only one market exists. Three further models are 433
then run using “planning areas” as sub-markets. 434
We do not incorporate temporal dynamics within our model, instead, we compare the case study region 435
in a state prior to offsetting and a state where full offsetting has taken place up to the market equilibrium. 436
We capture spatial heterogeneity in the definition of the credit by integrating a predictive ecological 437
model with our economic model of the offset market. A detailed discussion on the ecological modelling 438
is provided in the next Section. 439
4.4 Data Development 440
The spatial resolution used in the analysis are land parcels of 1 x 1 km aligned to the Ordnance Survey 441
British National Grid, with each 1 x 1km parcel representing a single landowner. The case study region 442
contains 5280 parcels and each land parcel contains data from four spatially referenced datasets 443
covering land classification, crop distribution, housing values, and wading bird abundance and 444
distribution. Where possible datasets from 2017 are utilised. Detailed information on data development 445
can be found in Appendix 1 of the paper. A summary is provided in the following paragraph. 446
For each sampled 1 x 1 km parcel, ESRI ArcGIS v10.4 (ESRI, Redlands, CA) was used to derive 447
information on the UK Biodiversity Action Plan Broad Habitats classes and crop cover. Land is 448
classified using the Centre of Ecology and Hydrology (CEH) Land Cover Map 2015 (LCM2015) and 449
CEH Land Cover plus 2017 (Rowland et al 2015). This produces 21 land classifications following the 450
UK Biodiversity Action Plan Broad Habitats classes including urban, improved grassland, arable and 451
horticulture. Arable and horticulture is further sub-divided into 11 crop types (beet, field beans, 452
grassland, maize, oilseed rape, potatoes, spring barley, winter barley, spring wheat, winter wheat 453
(including oats) and other). The value of agricultural land was calculated using gross margin data for 454
crops and livestock from the SRUC Farm Management Handbook (Beattie 2019). For crop data, the 455
crop coverage derived from the Land Cover plus Crops dataset was combined with the crop gross 456
margin data from the SRUC Farm Management Handbook. Gross margin values are given for each crop 457
type per ha on a productivity range from low, medium to high yield. To account for differences in yield, 458
data on soil quality at the 1 by 1 km resolution was derived and linked to the crop distribution. For 459
livestock, data was sourced from the England Agricultural Census which details the number of livestock 460
by type at the 5 by 5 km grid resolution. Using the livestock gross margin data from the SRUC Farm 461
Management Handbook, the total gross margin for all livestock at the 5 by 5 km resolution was 462
calculated. This was then disaggregated to the 1 by 1 km resolution. 463
16
Data on actual housing land values in the UK is limited due to the highly competitive nature of land 464
sales. Instead, we specify the value of land for housing development using the UK Government’s House 465
Price Paid dataset as a proxy. HM Land Registry publishes transaction data for all residential properties 466
sold from 1995 onwards and this is made available at the most detailed postcode level for England. 467
Property prices were first converted in 2017 prices using the Bank of England’s RPI figures (this is the 468
preferred inflation measure for house prices within the UK as used in the House Price Index4). The 469
average house sold price for each postcode sector was then calculated. This data was overlaid with the 470
1 x 1 km land parcels in ArcGIS. This allowed the average sold price for each land parcel to be derived. 471
From government and industry reports, it has been estimated that land value represents between 30% 472
and 50% of the price paid for residential property (HM Government 2018). Consequently, in our 473
modelling, we take the land value at 50% of the mean house price in each gird square. 474
Species data is obtained from the British Trust of Ornithology (BTO) Breeding Bird Survey (BBS). 475
Data was received for the Eastern region of the UK for the sampling years 2016 and 2017. There is no 476
dataset available which details abundance of the target species for every 1 by 1 km land parcel within 477
the case study region. Consequently, as part of the ecological modelling process, we developed a 478
Species Abundance Model (SAM) for the eastern region of the UK which could then be used to predict 479
the current distribution and abundance of the target species for every grid square in the case study region 480
based on current land use. 481
SAMs generate predictions of abundance for unsampled locations in a study area using existing data 482
from other sample areas, extrapolating this to new areas based on environmental characteristics (Barker 483
et al. 2014). The development of these ecological models allowed us to obtain predictions for the 484
abundance of the three target species based on current land use and any land-use change as a result of 485
biodiversity offsetting. The first stage of the modelling process was to produce a multiple regression 486
model to identify which parameters (environmental characteristics) predict current distributions for 487
each of the target species (curlew, oystercatcher, lapwing) across the eastern region of the UK. This 488
allowed us to identify which land management practices are most beneficial to the target species (related 489
to the parameters used in the modelling process) and thus serve as our “preferred” land management 490
practice within the biodiversity offset market. The SAM could then be used to predict how the 491
4 The index applies a statistical method, called a hedonic regression model, to the various sources of data on
property price and attributes to produce estimates of the change in house prices each period. The index is published
monthly. More detail available from: https://landregistry.data.gov.uk/app/ukhpi.
17
distribution and abundance of the target species could alter as a result of land-use change at a 1km grid 492
square level, allowing us to generate the predicted biodiversity offset credits. 493
The choice of a specific functional form for SAMS is governed by the knowledge of the species and 494
characteristics of the available data. The majority of SAMS sit within the generalised linear model 495
framework (GLM). Such models are typically described in terms of their systematic component in 496
which the response is linked to the environmental data, and their stochastic structure that describes the 497
error distribution (Venables and Springer 2002). For our analysis, we employed the negative binomial 498
model. This is a count data model, derived from the Poisson model but accounts for over dispersion 499
within the data (Potts and Elith 2006). Let us denote as a random variable the single species count, Yij, 500
from the ith site and the jth count. We assume: 501
(5) 𝐸(𝑌𝑖𝑗) = 𝜇𝑖𝑗. 502
We allow the mean to be a positive function of covariates 503
(6) 𝜇𝑖𝑗 = exp(𝒙𝑖𝑗′ 𝜷), 504
where 𝒙𝑖𝑗 is a vector of measured covariates for the jth count of the ith site, and 𝜷 a vector of parameters. 505
We fit the following model for each regression method: 506
(7) 𝑥𝑖𝑗′ 𝛽 = 𝛽0 + 𝛽1𝑥𝑖1 + 𝛽2𝑥𝑖2 + ⋯ + 𝛽𝑛𝑥𝑖𝑛 + 𝜀𝑡. 507
The models were fit by minimizing the negative log likelihood using the minimization procedure (glm2) 508
in the software package R (Version 3.6.2). The best model for each species was selected by comparing 509
the AIC values (Burnham and Anderson 1998). Models with the smallest AIC value are considered the 510
best model. The model was also evaluated for a sub-sample of the 2016 data set which was independent 511
of the dataset used the primary modelling. Explanatory variables for the ecological modelling were 512
derived using a series of spatially referenced data products: habitats were described using the land 513
classification data derived from the LCM2015 and the Land Cover plus Crops map. A further set of 514
environmental variables were derived using OS Open Map products. These variables were chosen based 515
on the existing literature for predicting bird habitat suitability (Brotons et al. 2004; Tattoni, Rizzolli, 516
and Pedrini 2012). 517
The results highlight that none of the crop types are positively associated with increased abundance of 518
any of the focus species, however switching from the crop types most negatively associated with each 519
species to improved grassland could benefit each species, dependent on other environmental factors 520
within the 1 by 1 km land parcel (the ecological modelling results can be found in Table A in Appendix 521
2). 522
18
Once the preferred predictive model was established for the eastern region data set, predictions could 523
then be made for the target species abundance for all land parcels across the case study region based on 524
current land use and changes in land use (Table 1). This allows us to identify our offset losses and gains. 525
For each target species, the predictive model calculates the current species abundance and determines 526
how this abundance would change based on all landowners in a region adopting the preferred land 527
management practice for the target species. The chosen land management practice is switching any land 528
patch containing crops to improved grassland. 529
Table 1: Predicted species abundance within each 1 x 1km land parcel across the case study region based 530 on current land use and under a full offset scenario (the full offset scenario is based on all crop hectares 531 being converted to improved grassland) 532
Predicted species abundance within each 1 x 1km land parcel based on current land use
Bird species
Total predicted abundance
across case study region (95%
confidence intervals)
Total number of
parcels with presence
Median count of birds
in a single land parcel
with presence (min –
max)
Lapwing 9267 (8962 - 9572) 4764 1.6 (0.1 - 62.0)
Oystercatcher 4301 (3865 - 4737) 4860 0.4 (0.1- 45.0)
Curlew 8986 (8697 - 9276) 4252 1.5 (0.1 - 40.3)
Predicted species abundance under full offset scenario (based on all crop ha being converted to improved
grassland)
Bird species
Total predicted abundance
across case study region (95%
confidence intervals)
Total number of
parcels with presence
Median count of birds
in a single land parcel
with presence (min –
max)
Lapwing 12485 (12165 - 12803) 4806 2.3 (0.1 - 65.4)
Oystercatcher 5834 (5324 - 6344) 5138 0.8 (0.1 - 74.3)
Curlew 11149 (10883 - 11416) 4762 2.0 (0.1 - 40.4)
4.5 The Empirical Modelling Framework for the Offset Market 533
As a baseline, we take the current land use structure in the case study area of size 𝐿. This is divided into 534
a number of 𝑛 land parcels equalling a size of 100 ha per parcel, i.e., a parcel is measured at a resolution 535
of 1 × 1 km. Each land parcel 𝑖 = 1, … , 𝑛 can comprise of any combination of 30 distinguished land-536
use types and crop classifications, denoted 𝐴1, … , 𝐴30. A policy target is chosen based on no net loss in 537
abundance of a single wading bird species known as our ecological metric (the baseline being set by 538
the current population of this species). Each land parcel has an associated ecological index 𝑒𝑖, which is 539
the abundance of the single species found within the corresponding land parcel. Across the whole case 540
study area, total species abundance is then simply the aggregate 𝐸 = ∑ 𝑒𝑖𝑛𝑖=1 , which represents the no 541
net loss conservation objective. 542
The regulator sets the preferred land management practice that benefits the target species and this allows 543
us to identify the land parcels that offer biodiversity offsets. Offsets are generated based on a farmer 544
19
switching the entire land parcel to the new land management practice. This provides us with a measure 545
of single species abundance for each land parcel before and after the change in land management 546
practice. This subsequently allows us to calculate the gains and losses for the species within the land 547
parcel. Recall that we assume that the default crop and livestock distribution on agricultural land is 548
currently the most profitable to a farmer, and that switching to an alternative land management practice 549
will (potentially) result in a loss of profit. Consequently, for a farmer to choose to supply offsets, the 550
offset price must at a minimum compensate for this loss in profit. We can then calculate the farmer’s 551
minimum willingness to accept for a change in land management practice, which gives us the minimum 552
unit price farmer 𝑖 = 1, … , 𝑛 would be willing to supply one offset: 553
(8) 𝑊𝑇𝐴𝑖 = 𝑃𝑖𝑚𝑖𝑛 =
𝑃
𝐵𝑖. 554
With 𝐵𝑖 denoting the increase in the target species abundance gained from offsetting, 𝑃𝑖𝑚𝑖𝑛 thus reflects 555
the unit price of a single wading bird. 556
This allows us to generate a series of supply prices for a single offset for each land parcel. To do so, we 557
first calculate the farmers’ profit by using crop and livestock gross margins (revenues minus variable 558
costs) data within each land parcel 𝑖 = 1, … , 𝑛: 559
(9) Π𝑖𝑡 = ∑ 𝐴𝑘
𝑡 𝜋𝑘𝑡
𝐾
𝑘=1
560
where 𝜋𝑘𝑡 is the gross margin for crop and livestock type 𝑘 = 1, … , 𝐾 in period 𝑡 = {0,1}, with 𝑡 = 0 561
and 𝑡 = 1 referring to the corresponding values before and after changing the agricultural land 562
management practice, respectively. The difference in the gross margin reflects the opportunity cost and 563
is the compensation required by the farmer in order to enter the market as an offset supplier:5 564
(10) 𝑃 = ΔΠ𝑖 ≡ Π𝑖0 − Π𝑖
1. 565
Knowing the opportunity cost that follows from (10) and taking the predicted increase in the abundance 566
of the target species 𝐵𝑖 from the ecological model, land parcels can now be ranked based on which 567
deliver the “best” value biodiversity offsets. Substituting these values into (8) gives a straightforward 568
5 Note that whilst the farmer will lose the gross margin for the original crop (the opportunity cost), still a profit
on the replacement crop will be made after switching.
20
basis on which to rank of parcels, where the inverse of (8) is essentially a benefit/cost ratio of gains in 569
the target species. 570
On the demand side of the market, developers’ demand for offset credits will be determined by the 571
expected revenue from converting their land into housing, taking into account the need to purchase 572
credits to offset any losses in the target species: 573
(11) 𝑃𝑗𝑚𝑎𝑥 =
𝑟
𝑒ℎ 574
with 𝑃𝑗𝑚𝑎𝑥 referring to the maximum willingness to pay for an offset by developer 𝑗 = 1, … , 𝑚. 575
For each land parcel we have information on the average house price sold and the proportion of the 576
parcel currently converted to housing. For consistency with the agricultural gross margin data, we 577
transform the house price sold data into a rental value of the land for the developers. To derive this land 578
rental value we first determine the remaining hectares of land within a parcel which can be converted 579
to housing development. Taking the 100 ha as the base of the parcel level and adjusting for the total 580
amount of land devoted to urban (𝐻𝑢𝑟𝑏) and suburban (𝐻𝑠𝑢𝑏) land use, the remaining land available for 581
development is: 582
(12) 𝐻𝑑𝑒𝑣 = 100 − 𝐻𝑢𝑟𝑏 − 𝐻𝑠𝑢𝑏. 583
Dividing the average house price sold on a land parcel by 100 gives us the average house price sold per 584
hectare, �̅�ℎ𝑎 . Using this value and (12) allows us to determine expected house price for the yet 585
undeveloped hectares within a parcel, i.e., the expected value of land sold for potential housing 586
development: 587
(13) 𝔼[𝑟] = 𝛼�̅�ℎ𝑎𝐻𝑑𝑒𝑣, 588
where 𝛼 ∈ [0,1] is the fraction of a house price that accounts for the value of land. 589
As a final step, we derive the expected net development value of land for housing, 𝑉, by adjusting the 590
land rental value obtained in (13) for the gross margin from the agricultural production within the land 591
parcel: 592
(14) 𝔼[𝑉] = 𝔼[𝑟] − ∑ 𝜋𝑘1
𝐾
𝑘=1
593
We assume that a developer will convert all remaining hectares of a land parcel to housing, subsequently 594
losing any income from agriculture and requiring the purchase of biodiversity offsets. For development 595
21
to take place, the developer must hold biodiversity offsets equal to the predicted abundance the target 596
species which are lost as a result of converting his land parcel to housing. As a result, the maximum 597
willingness to pay for a single offset for each land parcel is equal to the net value of land for housing 598
(14) relative to the amount of offsets required: 599
(15) 𝑊𝑇𝑃𝑗 = 𝑃𝑗𝑚𝑎𝑥 =
𝔼[𝑉]
𝑒ℎ. 600
We now have a range of offset prices based on farmers’ minimum WTA (8) to change the land 601
management practice and the developers’ maximum WTP (15) for a single offset, from which we can 602
generate the supply and demand curves of offset credits. For each price point, farmer 𝑖 = 1, … 𝑛 will 603
choose to supply offsets ℎ𝑖 if the supply price is at least equal to the unit cost of providing an offset, 604
i.e., its 𝑊𝑇𝐴𝑖. This is summarized in the following general decision rule guiding a farmer’s decision: 605
(16) ℎ𝑖 = {𝐵𝑖 if 𝑃 ≥ 𝑊𝑇𝐴𝑖 = 𝑃𝑖
𝑚𝑖𝑛
0 otherwise
. 606
607
Similarly, for each price point developers will choose whether or not to purchase offsets, and if so, how 608
many. In this respect, note that a developer must develop a whole parcel and must acquire a sufficient 609
quantity of offset credits ℎ𝑗 to replace all lost species within the parcel. More specifically, the demand 610
for offsets by developer 𝑗 = 1, … 𝑚 is governed by the following general decision rule: 611
(17) ℎ𝑗 = {𝑒0 if 𝑃 ≤ 𝑊𝑇𝑃𝑗 = 𝑃𝑗
𝑚𝑎𝑥
0 otherwise
. 612
We can now determine the quantity of offsets that are demanded and supplied at each price point across 613
all land parcels, yielding the offset supply and demand curves and allowing the identification of the 614
offset equilibrium price. Once the market equilibrium is established, straightforward welfare 615
assessments can be done by computing standard producer (farmer) surplus and consumer (developer) 616
surplus for the various scenarios tested. 617
4.6 Model Assumptions 618
Our empirical framework makes a series of assumptions. Firstly, our model is run over two time periods 619
t={0,1}, with t=0 and t=1 referring to the corresponding values before and after changing the 620
agricultural land management practice. Consequently, neither the ecological or economic models take 621
into account temporal dynamics. This implies that there are no time lags between the time period where 622
we lose abundance as a result of development and the time period where we gain abundance as a result 623
22
of the land management practice change. In practice, we would expect the losses to take place relatively 624
quickly once the development of a site begins, but the increases in abundance to happen gradually over 625
multiple months and years. We also recognise that there could be time lags between a species moving 626
between the site impacted by development and the newly created offset site. Secondly, we assume that 627
the current land management practice for farmers (i.e., current crop rotation or livestock) is privately 628
optimal and that switching to a new land management practice will result in a net loss in agricultural 629
profits. We also assume that once land has been secured for biodiversity offsetting it cannot be changed 630
into different land uses and it is protected in perpetuity. Finally, we assume not all land use types can 631
be developed new housing. The non-developable land types are coastal habitats (saltmarsh, littoral rock 632
and sediment, supralittoral rock and sediment), inland rock and woodland (coniferous and broadleaf). 633
We address the implications of these assumptions in the discussion. 634
5 Results 635
5.1 Outcomes of Offset Trading 636
The first design parameter we are interested in is how the choice of the biodiversity policy target, and 637
thus the unit of exchange, effects the market-clearing price, economic surplus and the ecological 638
landscape. The market-clearing equilibrium price of a credit varies between the three different policy 639
targets (Figure 1). The costliest offset credit is for a single oystercatcher credit under the NNL of 640
oystercatcher policy target. An oystercatcher offset credit costs £21,860 per bird, followed by lapwing 641
(£12,600 per bird under NNL of lapwings as the policy target) and curlew (£9971 per bird under NNL 642
of curlew as the policy target). We find that the greatest amount of housing development took place 643
under the oystercatcher policy target with 694 land parcels developed. Of these, 661 land parcels 644
required the developer to purchase offset credits. In contrast, under the lapwing target, 161 land parcels 645
were developed, and 81 required lapwing offset credits to be purchased. Under the curlew target, the 646
smallest amount of development took place, with 108 land parcels developed, of which 61 required 647
curlew offset credits to be purchased. From the supply perspective, there was the highest number of 648
suppliers for the oystercatcher target with 27 land parcels changing their land management practice to 649
become offset suppliers. 24 land parcels changed their land management practice to supply lapwing 650
offsets (under the lapwing policy target) and six land parcels changed their management practice to 651
supply curlew offsets (under the curlew policy target). 652
23
653
Figure 2: A comparison of the market-clearing price, the number of land parcels developed requiring 654 offsets and the number of land parcels choosing to supply offsets for each policy target 655
656
657
24
We can also consider view these results spatially (Figure 3). Figure 3 highlights the locations of the 658
newly created offset supply sites and where we see declines in the policy target as a result of 659
development taking place within the land parcel. At the market equilibrium, there was NNL in 660
abundance of the policy target for the three different species. For the curlew policy target, six land 661
parcels changed land management practice to become offset supply sites. These land parcels are located 662
at the coastal margin and each parcel provides between one and four offsets (which can be thought of 663
as increase between one and four curlews as a result of the change in land management practice). In 664
contrast, new development takes places further inland away from the coastal margin, with the greatest 665
number of curlews lost to development in a land parcel being two. For the lapwing policy target, the 666
maximum number of lapwing offsets supplied by a single land parcel is six. The offset supply sites for 667
lapwings are geographically closer to where the development takes place compared to the offset supply 668
sites for curlew (under the curlew policy target): the majority of the lapwing offset supply sites are 669
within 5 km of where lapwings are lost as a result of land parcels becoming developed. In contrast, 670
curlew supply sites are found at least 15 km from where curlews are lost as a result of new development. 671
Oystercatcher supply sites hold the greatest concentration of offsets, with some of the offset supply sites 672
providing more than 10 offsets per land parcel (which can be thought of as an increase of more than 10 673
oystercatchers due to the change in land management practice). The oystercatcher supply sites are 674
geographically distant from where oystercatchers are lost as a result of new development, with the 675
greatest distance between offset supply site and development impact over 100 km. 676
25
677
Figure 3: A comparison of biodiversity offset supply sites and development impacts under the three policy 678 targets: curlew (top left panel), lapwing (top right panel) and oystercatcher (bottom left panel). 679
Across all policy targets, there are gains from trade realised as a result of trading biodiversity offsets 680
compared to a no-offset-trading scenario. The greatest gains from trade are realised under the 681
oystercatcher policy target. Across all policy targets, consumer surplus accruing to developers is 682
consistently greater than producer surplus earnt by offset suppliers, suggesting the offset trading offers 683
more benefits to the housing developers than agricultural landowners. Despite these potential gains 684
from trade under the offset market scenario, a small proportion of potential development land was 685
utilised for new developments. The highest number of new developments took place under the 686
oystercatcher metric, with 15% of all possible development area having new housing built, compared 687
to 2% of the land area under the lapwing metric and less than 1% of land area under the curlew metric. 688
We can compare the impacts of trading on the non-target species. By this we mean that if the policy 689
target is NNL of oystercatchers, do we see a positive or negative change in abundance of lapwing and 690
/ or curlew (the non-target species in this scenario) as a result of offset trading? The results show, that 691
whilst the biodiversity offset market secures NNL of the target species under each of the single species 692
targets, there is a net loss in abundance of the other two (non-target) species (Figure 4). The smallest 693
26
losses take place under the curlew metric, with NNL of curlew, a net gain of 21 oystercatchers and a 694
net loss of 14 lapwings. The greatest loss in abundance is under the oystercatcher metric, with a net-695
loss of 3800 lapwings and curlews combined. This result highlights the differences in habitat 696
preferences of the three species and showcases the complexity and difficulty in defining an offset credit 697
to maintain no net loss of multiple species. 698
699
Figure 4: A comparison of the net losses of the non-target species across the three ecological metrics 700
701
5.2 Market Size Effects 702
The second design parameter we are interested in is how changes to the size of the market, in terms of 703
the number of landowners within a region, affects the market equilibrium and subsequent economic 704
surplus. We test this by dividing the original case study area into the three sub-markets, known as 705
Planning Areas (PA) 1, 2 and 3, which broadly align to the Local Authority planning areas found within 706
the case study region. For this analysis, we focus solely on NNL of oystercatcher as the policy target. 707
The model using the oystercatcher policy target was thus re-run for each PAs individually as 3 distinct 708
and independent sub-markets. 709
The first result of the sub-market models showed that market equilibrium was reached in each market 710
and there was no net loss of the oystercatcher policy target in all the sub-markets. Under the full market 711
region, an oystercatcher offset cost £21,978 per bird at market equilibrium. In contrast, under the sub-712
market analysis, PA 1 had the lowest cost offset at £771 per oystercatcher, distinctly cheaper than the 713
full market-clearing price. Within PA 1 there were two land parcels that provide a high number of 714
0
500
1000
1500
2000
2500
3000
3500
4000
4500
Curlew as the NNL policy target Lapwing as the NNL policy
target
Oystercatcher as the NNL policy
target
Net
lo
sses
in s
pec
ies
abund
ance
as
resu
lt o
f
off
set
trad
ing
27
“cheap” oystercatcher offset credits (35 in total) which are sold to 74 developers. In addition, a high 715
proportion of development takes place on land parcels which do not require offset credits (33 parcels). 716
The economic surplus for the offset suppliers was approximately 10% of the economic surplus of the 717
developers (£300,000). Comparing development in the full market with the development in PA 1, we 718
can see more development takes place in PA 1, compared with the same area in the full market (Figure 719
5). 720
721
Figure 5: A comparison of offset supply sites for oystercatchers and development impacts on oystercatcher 722 abundance under the full market scenario (left panel) and sub-market scenario (right panel). Planning 723 boundaries are shown on both maps for ease of comparison. 724
PA 2 had the highest market-clearing price for the oystercatcher offsets at £39,632 per bird. 32 land 725
parcels changed land management practice to become oystercatcher supply sites, which in total supplied 726
41 oystercatcher credits (on average 1.3 oystercatchers supplied per parcel). This is significantly smaller 727
than the number of offsets supplied per land parcel compared to Planning Areas 1 and 3 (on average 728
17.5 per parcel and 8.1 per parcel respectively). Compared to the full market model, there is 729
significantly less development taking place in PA 2, as a result of the increase in the price of an 730
oystercatcher credit. 731
PA 3 had the greatest number of land parcels requiring an offset (302), with a market-clearing price of 732
£26,699 per oystercatcher credit. The distribution of housing developments in PA3 is broadly similar 733
to the full market. This similarity is a result of PA 3 containing the majority of the land parcels which 734
supply offsets in the full market. 735
28
Comparing economic surplus for the developers and offset suppliers, the sub-market scenario offers a 736
small increase in the total surplus for offset suppliers of approximately £8000. This is due to the increase 737
in the additional land parcels supplying offsets in PA 3 and the increase in market clearing price for Pas 738
2 and 3 compared to the full market scenario. Surplus for housing developers reduces by £5,000 (Error! R739
eference source not found.). 740
741
Figure 6: A comparison of the number of oystercatcher credits supplied, the number of land parcels 742 becoming offset suppliers and the number of developers requiring offsets, under the full market and three 743 sub-markets models. The market equilibrium credit price (Market E) is provided for the full market and 744 three sub-markets. 745
From an ecological viewpoint, the sub-market scenario is marginally more beneficial to the non-target 746
species (curlews and lapwings) compared to the full market scenario (Figure 7). Net losses of both these 747
species are lower under the sub-market scenario, mainly as a result of the geographical re-distribution 748
of offset trades. 749
750
751
302
117
74
661
14
32
2
28
114
41
35
260
0 200 400 600 800
Plan area 3: Market E = £26,699
Plan area 2: Market E = £39,632
Plan area 1: Market E = £771
Full Market: Market E = £21, 979
Oystercatcher offsets supplied No. of suppliers No. of developers requiring offsets
Economic surplus for developers = £364,344
Economic surplus for farmers = £380
Economic surplus for developers = £1,08,710
Economic surplus for farmers = £222,519
Economic surplus for developers = £6,150,068
Economic surplus for farmers = £266,077
Economic surplus for developers = £12,400,000
Economic surplus for farmers = £480,240
29
752
Figure 7: A comparison of the losses (top chart) and gains (bottom chart) in the abundance of the NNL 753 target species (oystercatcher), as well as curlews and lapwings (the unintended consequences of trading) 754 across the full market and regional sub-market scenario. 755
756
757
30
6 Discussion 758
Tradeable offset credits offer a promising approach to tackling the conflicts between land development 759
and biodiversity conservation. Whilst there has been a growing policy interest in the concept, and a 760
large ecological literature studying the problem, there have been relatively few contributions from 761
economists to thinking about how best to design such offset markets. Our paper is designed to make a 762
contribution to the gap in the literature. One key contribution of our paper is the development of 763
spatially explicit supply and demand curves for biodiversity offsets, which allows us to test how two 764
changes to two fundamental design parameters – the ecological target and the geographic size of the 765
market – affect the functioning of a biodiversity offset market. To do this, we develop and integrate 766
ecological and economic models which encapsulate the spatial heterogeneity in the biodiversity 767
potential of individual sites as well as spatial heterogeneity in supply and demand prices. 768
Results show that the choice of the ecological metric has a significant effect on the number of trades 769
taking place, the amount of allowable development, unintended ecological consequences, and economic 770
welfare. Each of the three species considered as NNL targets favour different land parcels, as predicted 771
by the ecological model. From a supply perspective this means that each ecological target has a varying 772
opportunity cost in terms of agricultural land foregone. From a demand perspective, land parcels with 773
development potential have varying permit requirements depending on which species is chosen as the 774
NNL target. Using the spatially derived supply and demand curves reveals that the market does not 775
always react according to theory. Here our NNL curlew target with the lowest equilibrium offset price 776
at £9,972 per curlew resulted in the fewest number of trades taking place (10 trades and 108 land parcels 777
developed). In contrast, the NNL target which resulted in the costliest equilibrium offset price (£21,979 778
per oystercatcher) had the highest number of trades (260) with 694 land parcels developed. Based on 779
our comparative statics’ analysis, we might have presumed that the species which offered the cheapest 780
offsets would have led to the most development taking place as more developers would have been 781
willing to purchase a cheaper offset. This follows from an assumption that cheaper credit prices would 782
reduce opportunity costs for developers. However, based on our integrated modelling this result is more 783
nuanced: the greatest abundance of curlews and lapwings are found in the areas with the highest 784
development values, and subsequently a greater number of credits is required by the developer per land 785
parcel developed. As such, developers are willing to pay less per offset credit. Moreover, the most 786
favourable areas for lapwing and curlew supply are in areas where the value of agriculture is higher 787
(i.e., the suppliers' opportunity cost is higher), increasing the individual farmers' unit cost of offset 788
provision. There are very few farmers who offer offsets to the market below the maximum willingness 789
to pay of the housing developer. The market is therefore “constrained,” imposing a downward pressure 790
on the offset price to a level below what many suppliers would be willing to accept. 791
31
In contrast, oystercatchers are recorded at lower abundances in the areas of high housing value, meaning 792
developers are required to spend less on offsetting per parcel, increasing their maximum willingness to 793
pay for a single offset and thus encouraging more farmers to supply oystercatcher offsets. In addition, 794
under the full market size scenario, all oystercatcher offsets are created at the coastal margin where the 795
value of agricultural land is low but the potential for the number of new offsets to be generated is high. 796
This influences the farmers’ willingness to accept switching from agriculture to offset generation. 797
Related to this first result is the impact of the choice of offset metric on the other two bird (non-target) 798
species considered. Whilst for each model we focus on no net loss of the target species and this condition 799
is always met, we can analyse the impact on the other species because of spatial changes in land use. 800
Across all three metrics we see a net loss in the other two bird species, with the highest losses for 801
lapwings and curlews under the oystercatcher metric. This highlights that oystercatchers have very 802
different habitat requirements compared to curlews and lapwings. Developing a market that would 803
ensure no net loss of these three species simultaneously would, therefore, be more complex and likely 804
depress the level of new housing development, although this would be ecologically more beneficial. It 805
adds further evidence to the idea that designing credit currencies is inherently complex. Simple single 806
species metrics lend themselves to simple trading rules but can result in negative impacts on other 807
measures of biodiversity. 808
In terms of economic welfare, there is a systematically large difference between consumer and producer 809
surplus changes identified in the model outcomes. Consumer surplus is consistently greater across the 810
three ecological metrics, indicating that the market benefits housing developers more than farmers. This 811
has an interesting relevance given the current state of biodiversity offset discussions within the UK 812
policy context. The current plans are backed heavily by housing developers, with many even keen to 813
adopt a “net gain agenda”. However, there is a significant lack of farmers/landowners willing to take 814
part in the planning process or engage in the biodiversity offset pilot schemes (Sullivan and Hannis 815
2015). At present, the current incentives offered by biodiversity offsetting are not strong enough for 816
these entities to begin to engage with the policy process. 817
A second design issue relates to the size of the market. In general, more efficiently functioning markets 818
are those featuring a high number of participants that stimulate trading and enhance market liquidity. 819
Such markets would likely feature less volatile prices than thin markets, reducing price uncertainty in a 820
manner conducive to more investment in conservation (Wissel and Wätzold, 2010). Our theoretical 821
model predicts that an increase in the number of sellers would increase the amount of hectares that 822
generate offsets, resulting in a downward pressure on the equilibrium credit price. In contrast, the 823
equilibrium price would increase if demand would be enhanced through an increase in the number of 824
buyers. In our empirical model we can only adjust the number of potential sellers by changing the 825
32
market boundaries. However, by doing this we change the spatial heterogeneity embedded in both the 826
supply and demand curves. This leads to a counter-intuitive empirical result that, when we run our 827
analysis over smaller sub-markets. These sub-markets are based broadly on Local Authority planning 828
regions within the case study. The smallest sub-market (Planning area 1) had the lowest equilibrium 829
price per unit of one offset credit compared to when trading is allowed across the full region. This 830
specific sub-market only contains two offset supply sites, which, however, offer a high number of 831
credits due to the high ecological productivity of the two land parcels (that is, changing land 832
management to grassland produces a large predicted rise in target species abundance). Since credits are 833
supplied relatively cheaply due to the low opportunity costs of the land, the total amount of development 834
increases within this area (for new development not requiring offsets) due to spatial heterogeneity in 835
land value for development. That is, land value in the sub-region is low, thus depressing the developers’ 836
maximum willingness to pay for an offset. As such, in the full market scenario where offsets are more 837
costly, development would not take place in the same locations, since developers would instead choose 838
more profitable land parcels. In contrast, for Planning areas 2 and 3, the credit price increases compared 839
to the single catchment-wide market equilibrium price of £21,000 (£26,000 and £29,000 per offset 840
respectively. In these two sub-markets where the credit price increases, demand for new development 841
falls, consistent with the theoretical prediction. Spatially this is due to these areas no longer having 842
access to the cheapest offset supply sites. This is particularly evident in Planning area 3 where new 843
offset suppliers enter the market as a result of an increase in the offset supply price. 844
Ecologically it appears that configuring smaller sub-markets is beneficial as the losses in numbers of 845
the non-target wading birds are smaller than the full market scenario. This is a result of the decline in 846
new development due to the increase in credit price in two of the sub-markets. A concern ecologically 847
under the full market scenario when oystercatchers are the NNL target species is the difference in the 848
location of habitat being lost due to development compared to habitat being created for offsets. 849
Development takes place in upland regions where oystercatchers breed in the spring and summer 850
months, but offset sites are being created along the coastal margin, a favourable overwintering habitat. 851
A benefit of the sub-market scenario then is that it forces the creation of more expensive offset sites 852
inland. If a full market scenario was preferred, a trading rule where only spring /summer habitat could 853
be traded for newly created spring /summer habitat could be specified. This likely reduces the total 854
amount of new development due to higher credit prices. 855
We would also like to note some limitations in the model and its application. Firstly, our model does 856
not consider intertemporal dynamics regarding the ecological and economic aspects of the market. In 857
our modelling approach we chose to model two states: current land use where no offsetting took place 858
and a second state where all offsetting takes place up to where the market reaches an equilibrium. From 859
an ecological viewpoint this does not consider dynamics and time lags in the generation of the offset 860
33
credits related to the target species, which is a key concern of ecologists regarding offsetting (Gibbons 861
and Lindenmayer 2007; Maron et al. 2012). As recommended in Needham et al (2019), if offset markets 862
are to be developed then we would recommend the use of “banked credits” where trading can only take 863
place once the credits have been certified as providing an increase in the target species, which seeks to 864
overcome some of the problems associated with uncertainty in restoration. From an economics 865
perspective, we do not consider dynamics associated with land value changes associated with new 866
housing development. The rental value of land may decrease once a threshold of development on 867
neighbouring land parcels has been reached, thus reducing potential profits for developers and reducing 868
the requirements for credits. We also assume that once land has been secured for through an offset it 869
cannot be changed into a different land use, i.e., it is protected in perpetuity. Evidence from the UK 870
biodiversity offset pilots show that landowners are strongly opposed to this policy, preferring a 10 year 871
time horizon (Hannis and Sullivan 2015). This would likely lead to NNL of the ecological target over 872
time and undermines the potential of offset markets to deliver biodiversity protection. Stronger 873
incentives would need to be offered to landowners to encourage them to set aside their land for offsetting 874
permanently. 875
The supply prices of offsets were determined solely by agricultural gross margin data. We acknowledge 876
that this approach does not consider the social implications of offset developments, for example, loss 877
of locally important offset areas and access to green spaces. This is an aspect of offsetting that is being 878
discussed in greater detail in the literature, particularly with respect to offsetting in developing countries 879
(Griffiths et al. 2019). We also assumed that trading was mandatory across the whole case study region, 880
with any development impact on the ecological metric requiring an offset. This is in contrast to current 881
offset schemes globally where the purchasing of offsets is often undertaken voluntarily by developers, 882
who can also choose to undertake offsetting in the form of compensatory mitigation directly rather than 883
purchasing offsets through a market. This has been shown to reduce the demand for third-party 884
generated offsets. This not only suppresses the development of an offset trading scheme, but in many 885
cases also leads to lower quality restoration actions than those which would have been undertaken 886
through a regulated offset supplier such as an offset bank (Bonnie 1999; Bekessy et al. 2010). 887
The analytical framework here could be used to further study various design parameters within 888
biodiversity offset markets. For example, different ecological metrics could be used, such as no net loss 889
of habitats or an overall measure of species richness; temporal relationships could be explored through 890
the use of a dynamic model that allows for feedbacks in the system to account for impacts on 891
surrounding land parcels when adjacent parcels are either developed or certain land management 892
practices are changed. Finally, rather than considering land market values alone, other associated land 893
values could be considered too, such as accounting for carbon storage potential in upland habitats or 894
recreational values associated with green spaces near developed areas. 895
34
7. Conclusions 896
This study provides a novel analysis of biodiversity offset markets, integrating ecological species 897
abundance modelling with economic modelling of market outcomes. Our paper has provided evidence 898
on the importance of choice of the ecological metric for trading, as determined by the policy target, and 899
of the size of the market for trading, in terms of the development of a biodiversity offset market. 900
Empirical modelling of such offset markets has shown that altering these two design parameters does 901
not necessarily affect the market in expected ways. The choice of metric affects both the scale and 902
distribution of new development, as well as the ecological gains and losses, which differed significantly 903
between our three NNL target species (we focus on wading birds). This adds further evidence to the 904
argument that defining a credit for biodiversity offset trading is inherently complex. In addition, the 905
cheapest offset credit lead to the lowest amount of new development, and conversely the most expensive 906
credits were associated with the greatest amount of new development. 907
Regarding market size, we show that a larger geographic region for trading benefits the developers. 908
This scale allows each developer to access the cheapest offset supply sites. Dividing the region into 909
three smaller sub-markets constrains developers to access to the cheapest offsets within their sub-market 910
and places downward pressure on new development in two of the three sub-markets. However, dividing 911
a market into sub-markets also has ecological implications for both target and non-target species, and a 912
policy designer would need to balance these against the economic impacts of creating sub-markets in 913
deciding whether and how to segment an offset market, 914
In conclusion, our work has provided evidence to help understand how the choice of the ecological 915
target and the size of the market affect the development and functioning of a biodiversity offset market. 916
Implementing such markets could assist in reducing development/conservation conflicts. However, 917
offsetting alone is not sufficient to guarantee no declines in wider measures of biodiversity, and existing 918
national and international biodiversity protection must remain in place (such as Special Protection Areas, 919
for example). Instead, offsetting has the potential to reduce the loss of biodiversity for ecologically 920
valuable habitats that are not within the internationally protected regions, with the aim of moving 921
towards a net gain in biodiversity nation-wide. 922
923
35
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Market Analysis With an Empirical Application to the Finnish Economy.” Ecological Economics 1011
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Laitila, Jussi, Atte Moilanen, and Federico M. Pouzols. 2014. “A Method for Calculating Minimum 1013
Biodiversity Offset Multipliers Accounting for Time Discounting, Additionality and Permanence.” 1014
Methods in Ecology and Evolution 5 (11): 1247–54. https://doi.org/10.1111/2041-210X.12287. 1015
Lapeyre, Renaud, Géraldine Froger, and Marie Hrabanski. 2015. “Biodiversity Offsets as Market-Based 1016
Instruments for Ecosystem Services? From Discourses to Practices.” 1017
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Lockhart, Andy. 2014. “Developing an Offsetting Programme: Tensions, Dilemmas and Difficulties in 1019
Biodiversity Market-Making in England.” Environmental Conservation 42 (4): 335–44. 1020
https://doi.org/10.1017/S0376892915000193. 1021
Maron, Martine, Richard J. Hobbs, Atte Moilanen, Jeffrey W. Matthews, Kimberly Christie, Toby A. 1022
Gardner, David A. Keith, David B. Lindenmayer, and Clive A. McAlpine. 2012. “Faustian 1023
Bargains? Restoration Realities in the Context of Biodiversity Offset Policies.” Biological 1024
Conservation 155 (October): 141–48. https://doi.org/10.1016/j.biocon.2012.06.003. 1025
McKenney, Bruce A., and Joseph M. Kiesecker. 2010. “Policy Development for Biodiversity Offsets: 1026
A Review of Offset Frameworks.” Environmental Management. https://doi.org/10.1007/s00267-1027
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Needham, Katherine, Frans P de Vries, Paul R Armsworth, and Nick Hanley. 2019. “Designing Markets 1029
for Biodiversity Offsets: Lessons from Tradable Pollution Permits.” Journal of Applied Ecology 1030
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Office of Heritage, NSW. 2014. “Biodiversity Legislation Review OEH Paper 4: Conservation in Land-1032
Use Planning.” In . www.environment.nsw.gov.au. 1033
Potts, Joanne M., and Jane Elith. 2006. “Comparing Species Abundance Models.” Ecological Modelling 1034
199 (2): 153–63. https://doi.org/10.1016/j.ecolmodel.2006.05.025. 1035
Quétier, Fabien, Baptiste Regnery, and Harold Levrel. 2014. “No Net Loss of Biodiversity or Paper 1036
Offsets? A Critical Review of the French No Net Loss Policy.” Environmental Science and Policy 1037
38 (April): 120–31. https://doi.org/10.1016/j.envsci.2013.11.009. 1038
Robertson, Morgan M. 2004. “The Neoliberalization of Ecosystem Services: Wetland Mitigation 1039
Banking and Problems in Environmental Governance.” Geoforum 35 (3): 361–73. 1040
https://doi.org/10.1016/j.geoforum.2003.06.002. 1041
Scanlon, Jane. 2007. “An Appraisal of the NSW Biobanking Scheme to Promote the Goal of Sustainable 1042
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Development in NSW.” Journal of International and Comparative Environmental Law. 2007. 1043
http://classic.austlii.edu.au/au/journals/MqJlICEnvLaw/2007/4.html. 1044
Simpson, Katherine. 2011. “Implementing an Ecosystem Approach:The Case of the Teesmouth and 1045
Cleveland Coast European Marine Site.” 1046
Smith, Ken. 2011. “The State Of The Natural Environment Of The Tees Estuary A Review Of The Bird 1047
Chapter.” http://www.inca.uk.com/wp-content/uploads/2012/01/BIRDS-SONET-UPDATE-1048
2011.pdf. 1049
Sullivan, S., and M. Hannis. 2015. “Nets and Frames, Losses and Gains: Value Struggles in 1050
Engagements with Biodiversity Offsetting Policy in England.” Ecosystem Services 15 (October): 1051
162–73. https://doi.org/10.1016/j.ecoser.2015.01.009. 1052
Tattoni, Clara, Franco Rizzolli, and Paolo Pedrini. 2012. “Can LiDAR Data Improve Bird Habitat 1053
Suitability Models?” Ecological Modelling 245 (October): 103–10. 1054
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Biodiversity Protection-The Case Of Habitat Banking European Commission DG Environment.” 1057
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Catharina, van Teeffelen, Astrid. 2013. “Policy Options for an EU No Net Loss Initiative.” 1060
www.ieep.eu. 1061
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Online. 2020. https://ribits.usace.army.mil/ribits_apex/f?p=107:2. 1063
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Biodiversity Conservation: A Review.” Environmental and Resource Economics. Springer 1067
Netherlands. https://doi.org/10.1007/s10640-015-9996-8. 1068
William Bull, Joseph, and Niels Strange. n.d. “The Global Extent of Biodiversity Offset Implementation 1069
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https://doi.org/10.1038/s41893-018-0176-z. 1071
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Permits to Biodiversity Conservation” Conservation Biology 24 (2): 404–11. 1073
https://doi.org/10.1111/j.1523-1739.2009.01444.x. 1074
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1077
1078
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7 Supplementary material 1079
1080
1081
1082
Figure A: Flow chart of the integrated ecological – economic model to determine the market-clearing
price for biodiversity offsets under three different policy targets
42
7.1 Data sources 1083
Land classification 1084
For each 1 by 1km land parcel, ESRI ArcGIS v10.4 (ESRI, Redlands, CA) was used to derive 1085
information on the UK Biodiversity Action Plan Broad Habitats classes and crop cover. Land is 1086
classified using the Centre of Ecology and Hydrology (CEH) Land Cover Map 2015 (LCM2015) and 1087
CEH Land Cover plus Crops 2017 (Rowland et al 2015). LCM2015 is created by classifying two-date 1088
composite images and is based mainly on data from Landsat-8 (30m resolution) supplemented with 1089
AWIFS data (60 m resolution) as required (NERC CEH). This produces 21 land classifications 1090
following the UK Biodiversity Action Plan Broad Habitats classes including urban, improved grassland, 1091
arable and horticulture. A limitation for our work is that the data is periodically updated (the previous 1092
iteration being LCM2007) and as such there may be differences between the actual habitats identified 1093
in 2015 and our model year (2017). To derive crop coverage, we used the 2017 CEH Land Cover plus 1094
Crops map. This is the first high-resolution map of UK crops and classifies two million land parcels 1095
based on the LCM2015. Crop data is derived from a combination of Copernicus Sentinel-1 C-band SAR 1096
(Synthetic Aperture Radar) and Sentinel-2 optical data. Accuracy of the data is assessed using crop type 1097
data collated by the Rural Payments Agency, Rural Payments Wales and Scottish Government Rural 1098
Payments and Services which detail all land registered for agricultural use and are submitted annually 1099
by farmers. There are 11 crop types (beet, field beans, grassland, maize, oilseed rape, potatoes, spring 1100
barley, winter barley, spring wheat, winter wheat (including oats) and other. The LCM2015 and Land 1101
Cover plus Crops 2017 datasets were combined using the Tabulate Intersection spatial statistics tool in 1102
ArcGIS. This derived the total area for each habitat and crop type within the 1 by 1 km land parcel. In 1103
over 95% of land parcels the Land Cover plus Crops data aligned with the LCM2015 arable and 1104
horticulture classification. Where differences were identified, for example, winter wheat in the Land 1105
Cover plus Crops dataset overlaying a layer containing grassland from the LCM 2015 data, we assumed 1106
land use from the Land Cover plus Crops dataset. There may be disagreement between the two datasets 1107
based on the changes to field boundaries between the collection years (limited impact) but is more likely 1108
due to changes in validation methods for the data between the years 2015, 2016 and 2017 as 1109
acknowledged by CEH. 1110
1111
1112
1113
1114
43
Agricultural profits and land use data 1115
The value of agricultural land was calculated using gross margin data for crops and livestock from the 1116
SRUC Farm Management Handbook. Gross margin is a widely-used measure of net benefit from crop 1117
or livestock choice in farm accounting, being constructed from total revenues from sale of 1118
crop/livestock product minus the variable costs of production. For crop data, the crop coverage derived 1119
from the Land Cover plus Crops dataset was combined with the crop gross margin data from the SRUC 1120
Farm Management Handbook. Gross margin values are given for each crop type per ha on a productivity 1121
range from low, medium to high yield. To account for differences in yield, data on soil quality at the 1122
1km2 resolution was derived and linked to the crop distribution. 1123
The calculation of livestock gross margins were more complex. Data on livestock is sourced from the 1124
England Agricultural Census conducted in June each year. This surveys farmers via a postal 1125
questionnaire with the farmer declaring agricultural activity on their land. Data for non-surveyed farms 1126
is extrapolated from previous years and trends on comparable farms. This data is made available at the 1127
5 by 5 km grid square resolution and lists the number of livestock by type within the 5 km grid square. 1128
The most recent release is the 2010 data. Using the livestock gross margin data from the SRUC Farm 1129
Management Handbook, the total gross margin for all livestock within the 5 km2 grid square was 1130
calculated. Using the Tabulate Intersection Tool within ArcGIS, livestock gross margin data on the 5 1131
km resolution was then disaggregated to the 1 by 1 km land parcel resolution by attributing these values 1132
to only those habitats which would likely contain livestock: improved grassland, semi natural grasslands 1133
and heather grassland. We recognise that there are some limitations with this disaggregation approach, 1134
for example, it does not fully take into account differences in productivity between the various grassland 1135
types, however, given the scale of the case study region we believe this still creates a reliable indication 1136
for livestock gross margins. 1137
Land values for housing development 1138
To establish demand for offset credits data is needed on the potential value of land for development. 1139
Ideally, we would secure value for residential land within each parcel. However, due to data availability 1140
restrictions, we used a proxy measurement for the potential value of land for residential housing using 1141
the UK Government’s price paid dataset. HM Land Registry publishes transaction data for all residential 1142
properties sold from 1995 onwards and this is made available at the most detailed postcode level for 1143
England. To derive a land value proxy, property prices were first converted in 2017 prices using the 1144
Bank of England’s RPI figures (this is the preferred inflation measure for house prices within the UK 1145
44
as used in the House Price Index6). For price paid data of below £30,000 and above £400,000 additional 1146
screening was undertaken to ensure the values were representative of the postcode region. Duplicate 1147
sales of the same property were identified and only the most recent transaction used in calculations. 1148
The average house sold price for each postcode sector was then calculated. This data was overlaid with 1149
the 1 x 1 km land parcels in ArcGIS. This allowed the average sold price for each land parcel to be 1150
derived based on how many postcode sectors were present within a 1km2, the area covered by these 1151
postcodes within the grid and the price paid value associated with each postcode. This calculated a 1152
weighted average value for house prices sold at the 1km2 resolution. Where there was limited 1153
transaction data for a postcode sector, a smoothing function was applied in ArcGIS to calculate a 1154
predicted value for a 1km2 parcel based on the values of the surrounding land parcels. From government 1155
and industry reports, it has been estimated that land value represents between 30% and 50% of the price 1156
paid for a residential property. 1157
Bird Data 1158
Species data is obtained from the British Trust of Ornithology (BTO) Breeding Bird Survey (BBS). 1159
BBS survey squares are randomly selected from all 1 by 1km grid squares in the UK National Grid and 1160
the aim is that the same surveys are surveyed each year. Data was received for the Eastern region of the 1161
UK for the sampling years 2016 and 2017. BBS data is collected by volunteers who conduct three field 1162
visits to each survey square: a reconnaissance visit and two bird recording visits. Surveys take place 1163
during the early breeding season (April – mid-May) and a second visit at least for weeks later (mid-1164
May to the end of June). Counts begin early in the early morning to maximise bird activity and last 1165
approximately 90 minutes. Volunteers record all the birds they see or hear as they walk methodically 1166
along their transect routes (BTO, 2019). To analyse species abundance the maximum count of the two 1167
visits was taken in line with BBS methodology. Species that were not recorded are assigned a count 1168
value of zero. Between 18% and 23% of the grids sampled contained at least one bird. 1169
Explanatory variables were derived using a series of spatially referenced data products: habitats were 1170
described using the land classification data derived from the LCM2015 and the Land Cover plus Crops 1171
map (21 classifications). A further set of environmental variables were derived using OS Open Map 1172
products. The average altitude within the grid square was derived using OS Terrain 5 digital terrain 1173
model (DTM). OS Open Map local (vector) was used to derive the distance from the nearest railway 1174
6 The index applies a statistical method, called a hedonic regression model, to the various sources of data on
property price and attributes to produce estimates of the change in house prices each period. The index is published
monthly. More detail available from: https://landregistry.data.gov.uk/app/ukhpi
45
line, overhead pylon, road (divided into Motorway, A road and B road) watercourse and tidal boundary. 1175
Distance from the centroid of each grid square to the polygon/polyline feature was calculated using the 1176
Arc GIS analysis tool ‘Near’ within the ‘Proximity’ tool box. In addition, road density, pylon density 1177
and railway density within the grid square were derived using the ‘Line Density Tool’ within the Spatial 1178
Analyst toolbox. For road density, two measures were calculated: unweighted where all three road types 1179
were given a value of 1; and a second weighted density with Motorways weighted highest (5), A roads 1180
(3) and all other roads (1). These variables were chosen based on the existing literature for predicting 1181
bird habitat suitability. 1182
7.2 Ecological Modelling Results 1183
Table A: Predictive ecological model results for curlew, lapwing and oystercatcher species using a pooled 1184 dataset for the years 2016 and 2017. 1185
VARIABLES
Curlew Pooled Sample:
GLM with Binomial Log
Link
Lapwing Pooled
Sample: GLM with
Binomial Log Link
Oystercatcher Pooled
Sample: GLM with
Binomial Log Link
Habitat: Coefficient Standard
Error Coefficient
Standard
Error Coefficient
Standard
Error
Acid Grassland (area ha) 0.003 -0.011 -0.036*** -0.009 -0.023*** -0.009
Bog (area ha) 0.001 -0.011 -0.043*** -0.01 -0.024*** -0.009
Broadleaf woodland (area ha) -0.053*** -0.012 -0.081*** -0.011 -0.041*** -0.01
Calcareous grassland (area ha) 0.023 -0.017 -0.036** -0.016 0.002 -0.017
Coniferous woodland (area ha) -0.021** -0.011 -0.074*** -0.01 -0.033*** -0.009
Fen, marsh and swamp (area ha) -0.037 -0.034 -0.005 -0.013 -0.022 -0.014
Freshwater (area ha) -0.025* -0.014 -0.048*** -0.013 0.011 -0.011
Heather (area ha) 0.004 -0.011 -0.038*** -0.01 -0.023** -0.009
Heather grassland (area ha) 0.008 -0.011 -0.035*** -0.01 -0.011 -0.009
Inland rock (area ha) -0.097*** -0.027 -0.130*** -0.038 -0.053*** -0.016
Littoral rock (area ha) 0.088 -0.149 -0.646*** -0.177 0.283*** -0.093
Littoral sediment (area ha) 0.023 -0.024 -0.168*** -0.05 0.076*** -0.022
Neutral grassland (area ha) 0.048 -0.043 0.036 -0.035 -0.037 -0.048
Saltmarsh (area ha) 0.063*** -0.023 0.029* -0.017 0.047*** -0.016
Saltwater (area ha) 0.013 -0.015 -0.086*** -0.019 0.004 -0.013
Suburban (area ha) -0.079*** -0.014 -0.100*** -0.012 -0.031*** -0.009
Supralittoral rock (area ha) -0.543* -0.321 0.082 -0.159 -0.118 -0.152
Supralittoral sediment (area ha) -0.044** -0.022 -0.048*** -0.015 0.003 -0.011
Urban (area ha) -0.068*** -0.019 -0.078*** -0.015 -0.064*** -0.012
Crop type:
Beets -0.072 -0.046 -0.019 -0.013 0.002 -0.014
Field Beans -0.040** -0.019 -0.039*** -0.012 -0.061*** -0.017
Improved Grassland 0.002 -0.011 0.028 -0.009 -0.01 -0.009
46
Maize -0.043 -0.028 -0.027* -0.015 -0.089*** -0.019
Oil Seed Rape -0.032*** -0.012 -0.048*** -0.01 -0.054*** -0.011
Potatoes 0.027 -0.026 -0.032** -0.014 0.014 -0.012
Spring Barley -0.003 -0.013 -0.049*** -0.012 0.004 -0.01
Spring Wheat -0.01 -0.013 -0.034*** -0.011 -0.006 -0.01
Winter Barley -0.024* -0.014 -0.040*** -0.012 -0.016 -0.012
Winter Wheat -0.045*** -0.013 -0.052*** -0.01 -0.042*** -0.009
other -0.033** -0.013 -0.030*** -0.01 -0.020** -0.01
Overhead Pylons Density (km2) -0.445*** -0.116 -0.336*** -0.117 -0.375** -0.155
Tidal (distance km) 0.015*** -0.003 0.013*** -0.004 -0.002 -0.003
Watercourse (distance km) -0.232** -0.091 -0.409*** -0.085 -0.401*** -0.087
Model year dummy: 0 = 2016, 1=
2017 -0.02 -0.091 0.051 -0.101 0.013 -0.103
Constant 0.771 -1.053 4.350*** -0.902 1.644* -0.853
Observations 3,626 3,620 3,610
chi2 835.6 3.776 525.4
*** p<0.01, ** p<0.05, * p<0.1
1186
7.3 Data Sources 1187
British Trust for Ornithology Breeding Bird Survey 1188
Many thanks to the British Trust of Ornithology for supplying 2016 and 2017 Breeding Bird Survey 1189
Data for oystercatcher, curlew and lapwing. The BTO/JNCC/RSPB Breeding Bird Survey is a 1190
partnership jointly funded by the British Trust for Ornithology (BTO), Royal Society for the Protection 1191
of Birds (RSPB) and the Joint Nature Conservation Committee (JNCC), with fieldwork conducted by 1192
volunteers. 1193
EDINA Agricultural Census 1194
England and Wales Agricultural census data supplied by EDINA at the University of Edinburgh and 1195
the Department of Environment, Food and Rural Affairs (DEFRA) for England, The Welsh Assembly 1196
Government, or The Scottish Government (formerly SEERAD). 1197
CEH Land Cover 1198
Rowland, C.S., Morton, R.D., Carrasco, L., McShane, G., O'Neil, A.W., Wood, C.M. (2017). Land 1199
Cover Map 2015 (vector, GB). NERC Environmental Information Data Centre 1200
https://doi.org/10.5285/6c6c9203-7333-4d96-88ab-78925e7a4e73 1201
47
CEH Land Cover® plus Crops 1202
Based upon CEH Land Cover® Plus: Crops © NERC (CEH). © RSAC. © Crown Copyright 2007, 1203
Licence number 100017572.’ 1204
Soil Quality - LandIS 1205
Reproduced from National Soil Resources Institute Soilscapes viewer. © Cranfield University 2019. 1206
No part of this publication may be reproduced without the express written permission of Cranfield 1207
University. 1208
HM Registry House price data 1209
Obtained from https://data.gov.uk/dataset/4c9b7641-cf73-4fd9-869a-4bfeed6d440e/hm-land-registry-1210
price-paid-data 1211
Contains HM Land Registry data (c) Crown copyright and database right 2018. 1212
This data is licensed under the Open Government Licence v3.0. 1213
Ordnance Survey Open Data 1214
Contains OS data © Crown Copyright OS Open Roads, OS Open Rivers, OS Terrain® 50, Boundary-1215
Line™, Code-Point® Open and 1:250 000 Scale Colour Raster, 2018 1216