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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

3

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

https://doi.org/10.1016/j.ecoser.2014.10.010. 1018

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

009-9396-3. 1028

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for Biodiversity Offsets: Lessons from Tradable Pollution Permits.” Journal of Applied Ecology 1030

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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

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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

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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

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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

https://doi.org/10.1016/j.ecolmodel.2012.03.020. 1055

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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

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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