Making Agri-Environmental Payments More Cost Effective

2.  Literature review and policy spectrum framework

Abstract

This chapter seeks to identify and discuss “best practice” design principles for agri-environmental payment schemes. On the basis of a literature review, a Policy Spectrum Framework is developed to classify payment schemes based on key design features that are conducive to achieving cost-effective outcomes. Based on this Framework, an assessment of payment design options is developed based on their advantages and disadvantages.

2.1. Literature review

The literature review has been split into two parts.

• General literature review to identify evidence on “best practice” design principles for agri-environmental payment mechanisms: this review is used to inform the Policy Spectrum Framework (Section 2.2). Three hundred and seventy relevant papers have been identified to date. Key papers (meta-analyses or literature syntheses, plus papers containing strong theoretical discussions about desirable properties of agri-environmental measure or discussing criteria for evaluating agri-environmental policies) were identified based on an abstract search and Secretariat expert knowledge.

• Detailed literature review of choice experiment (CE) studies (“CE literature review”) examining farmer or landholder preferences for agri-environmental policy design elements: a detailed review of 55 choice experiment studies has been completed and is informing the design of the choice experiment component (a summary of key features of reviewed choice experiment studies is provided in Annex F).

2.2. Policy Spectrum Framework

Policy spectrum

Agri-environmental payment mechanism design is a broad field covering many different policy elements. A significant body of past OECD work (OECD, 2010[1]; OECD, 2010[2]; OECD, 2012[3]; Lankoski and Ollikainen, 2003[4]; Lankoski, 2016[5]; Hardelin and Lankoski, 2018[6]) has provided guidance on various elements of policy design. This work builds on these past efforts, and acknowledges that a key ‘frontier’ for agri-environmental policy design is the question of how and when policies should provide incentives for farmers based on:

• The implementation of agri-environmental practices or actions: The basis for a payment is a defined farming practice or action including:

  • farm management actions (e.g. reduced fertiliser application, use of cover crops, conservation tillage, organic farming as per agreed requirements)

  • installation and maintenance of on-farm infrastructure for preventing and mitigating environmental damage or providing environmental goods (e.g. livestock fencing, buffer strips, constructed wetlands).

• Achieving a specified level of on-farm environmental results or landscape-level aggregate environmental results: The basis for a payment is defined in terms of environmental results (whether measured or modelled) achieved by the payment beneficiary (whether an individual in case of on-farm results or group of landholders in case of landscape-level results). The payment can be based on achieving specific environmental result thresholds (either measured or modelled), for example, nitrogen runoff below 10 kg/ha or GHG emissions reduction by 20% relative to the baseline. Payment can also be based on abatement of GHG emissions or reduction of nitrogen runoff, for example, EUR 5/kg of nitrogen runoff reduction or EUR 30/tonne of CO₂ equivalent emissions reduction.

• Achieving a specified level of agri-environmental performance: Payment is defined in terms of achieving environmental performance measured by indicators or proxies for the potential environmental results, for example nutrient (N and P) balance kg/ha or environmental benefit index value. The payment can be based on achieving a certain performance threshold (e.g. N-balance below 50 kg/ha) or be based on continuous performance improvements (e.g. EUR/kg of reduced N‑balance). Hence, the main difference between the results-based payment and the performance-based payment is that the former is based on the specific environmental result measured directly (reduction of nitrogen runoff or GHG emissions) whereas the latter is based on proxies for the potential environmental results (for example, nitrogen surplus indicating potential nitrogen runoff or nitrous oxide emissions). Moreover, in many cases these performance indicators or proxies reflect several environmental effects (for example, high N-balance has implications to water quality, air quality and GHG emissions).

• A combination of practices adoption and achieving a specified level of environmental performance or results (hybrid policy): Basis for a payment is defined based on a combination of practice adoption and performance or results achievement. For example, a mechanism which includes a ‘base payment’ based on practices and a ‘bonus payment’ based on results.

The question of whether, and how, policies should provide incentives for farms to adopt specific practices, or achieve a specified agri-environmental performance or result is important for at least two reasons. First, an increasingly large literature assessing the performance of existing agri-environmental payment mechanisms – the vast majority of which are practice-based – shows that there is significant ‘room for improvement’. Generally ex post evaluations have shown that such policies typically have limited environmental effectiveness and do not provide strong value-for-money (budgetary cost-effectiveness) (Batáry et al., 2015[7]; Coderoni and Esposti, 2018[8]; Dal Ferro et al., 2018[9]; Hardelin and Lankoski, 2018[6]; Lankoski, 2016[5]; Shortle et al., 2012[10]; Engel, 2016[11]). These evaluations often conclude that incentivising practice implementation, which may have tenuous connection with actual environmental performance or results, is an important reason for the limited environmental- and cost-effectiveness. In light of these findings, policy makers have in recent years become more receptive to considering alternatives to practice-based policies.

Second, advances in digital tools relevant for implementing alternatives (including targeted practice-based policies, as well as performance- and results-based policies) are reducing the costs and practical difficulties of implementing them (OECD, 2019[12]). Thus, policy options which even a few years ago may have been considered ‘infeasible’ due to high data needs or transaction costs may be more feasible now or in the future. Thus, policy makers have the opportunity to reconsider the feasible policy set. Table 2.1 sets out a spectrum describing the key options policy makers can choose from in this respect.

Table 2.1. Policy spectrum: From actions to outcomes

Uniform action or practice-based	Targeted action- or practice-based	Performance- based	Field- or farm-level results-based	Aggregate or landscape-level results-based
Hybrid policies incorporating practice-based elements with performance- or results-based elements
Payment is conditional on implementing specified practice(s) Payment is not differentiated across individuals, or spatially Payment generally made to an individual, but could use features such as agglomeration bonusesa or zonal-based eligibility rules to incentivise co-ordination	Payment is conditional on implementing specified practice(s) Payment is differentiated (various options for differentiation based on different types of targetingb) Payment generally made to an individual, but could use features such as agglomeration bonuses or zonal-based eligibility rules to incentivise co-ordination	Payment is conditional on estimated or measured improvements in farm-level environmental performance (may take into account a limited number of on-farm exogenous factors) Can be differentiated based on different levels of performance, or uniform payment conditional on achieving a specified performance threshold Payment generally made to an individual Could use features such as agglomeration bonusesa or zonal-based eligibility rules to incentivise co-ordination	Payment is conditional on estimated or measured field- or farm-level environmental results (if estimated, taking into account both endogenous and exogenous factors) Can be differentiated based on different levels of results, or uniform payment conditional on achieving a specified results threshold Payment generally made to an individual Could use features such as agglomeration bonusesa or zonal-based eligibility rules to incentivise co-ordination (if not differentiating payment based on level of results)	Payment is conditional on estimated or measured aggregate or landscape -level environmental results (if estimated, taking into account both endogenous and exogenous factors) Can be differentiated based on different levels of results, or could be uniform payment conditional on achieving a specified results threshold Payment could be made to a group or individual

Notes:

a. An “agglomeration bonus” is an incentive paid if a desired spatial configuration of practice adoption in a given region is achieved (e.g. buffer strips adopted in all field parcels adjacent of given water course). Other types of spatial incentives are also possible: for example, an agglomeration malus instead rewards landscape diversity (i.e. penalises agri-environmental actions taken in adjacent units).

b The concept of “targeting” can be implemented via several different policy dimensions, e.g. one policy may target by restricting eligibility to producers in a certain area or who meet certain key criteria, but pay uniformly to all eligible participants; another may have open eligibility but may encourage targeting via self-selection by differentiating payments by paying more in areas where environmental benefits (or cost-benefit ratios) are higher

Framework for providing “best-practice guidance” on where to situate along the policy spectrum

The policy-spectrum shows that even within this particular ‘frontier’ of policy design, there are many different options available for policy makers. Therefore, the question naturally arises as to where policy makers should choose to situate along the spectrum, given their specific context. This section provides the first steps towards developing a framework for providing guidance on this question.

This report uses cost-effectiveness as the key criterion for providing guidance on ‘best practice’ agri-environmental payment mechanism design. Cost-effectiveness is a holistic concept that takes into account environmental effectiveness, different kinds of costs (e.g. compliance costs and policy-related transactions costs), and can incorporate dynamic considerations (e.g. policy impacts on innovation). OECD (2010, p. 17[1]) characterises cost-effectiveness of agri-environmental policies as follows: “minimising the costs, prior to remuneration for profit losses if any, of achieving the environmental goal…the cost-efficient policy instrument is the one that minimises compliance costs while achieving the environmental target.”

It is acknowledged that additional criteria beyond cost-effectiveness are also relevant for policy evaluation and guidance. For example, equity and distributional impacts may warrant attention in their own right, not least because the distributional consequences of a policy may affect its cost-effectiveness in the long term (Engel, 2016[11]). More broadly, behavioural and social impacts of alternative policy options may also be of interest, again because of their long-term impact on policy cost-effectiveness, or in their own right (Dessart, Barreiro-Hurlé and van Bavel, 2019[13]; OECD, 2012[14]; Engel, 2016[11]). Another potential consideration is ancillary benefits and costs, which are impacts caused by agri-environmental policy mechanisms, but which are not encompassed by the explicit policy objective (OECD, 2010[1]). Where relevant, this paper considers how different policy design options perform against such other criteria; it should however be noted that, compared to providing quantitative measures of cost-effectiveness, in general it is relatively difficult to quantify performance against these criteria or to provide qualitative assessments that are generalizable or comparable across contexts.

To date, seven key dimensions for assisting policy makers to decide on where to locate along the draft Policy Spectrum have been identified. Each dimension constitutes a desirable property of agri-environmental policies that is considered important for achieving cost-effectiveness, based on Secretariat expert judgement and the literature review, and also taking into account recently developed taxonomy to characterise agri-environmental schemes with special focus on those policy design features that are conducive to their cost-effectiveness (Guerrero, 2021[15]). An overview of the dimensions is provided in Table 2.2, and the seven dimensions are discussed below.

Table 2.2. Dimensions of cost-effective agri-environmental payment mechanisms

Policy feature	Specification	Key design options
Setting clear policy objectives	How many objectives?	Single objective Multiple objectives
	Can the objectives be quantified?	Objectives are easy to monitor and quantify Objectives are difficult or costly to monitor and quantify
Targeting	Spatial targeting	Zonal targeting based on environmental sensitivity (e.g. proximity to watersheds, areas with soil degradation)
	Cost-targeting	Compliance cost thresholds in enrolment screens
	Benefit-cost targeting	Environmental benefit based Environmental Benefit Indices (EBIs) Ratio of environmental benefits to compliance cost
Eligibility	Beneficiaries	Individual Groups of individuals or collectives
	Farm type	Intensive farming systems Extensive farming systems Farm size
	Other eligibility criteria	Income Age
Behavioural aspects	Dispositional factors	Resistance to change Flexibility Risk attitude Environmental concern
	Social factors	Group behaviour, influence of neighbours
	Cognitive factors	Perception of costs, benefits and risks
Additionality	Definition of baselines	Historic baselines Current environmental performance as baseline Analytical baselines Dynamic baselines Baseline practice or performance requirements
Tailoring	Calculation of payment rate	Based on compliance costs Based on value of environmental benefits Based on environmental performance or results Bid-based (conservation auctions)
	Uniform payments	Based on estimated average compliance costs
	Differentiated payments	Based on estimated differential compliance costs or environmental benefits Bid-based, auction mechanisms
	Advisory systems	Training and education Communication platforms and information sharing Consulting
	Contract design	Contract length Flexibility
	Hybrid schemes	Mix of action and performance- or result-based payments Fixed payment elements Bonus payments
Conditionality and Enforcement	Monitoring	In situ inspections Digital Technologies Self-monitoring Group monitoring
	Sanctions	Expiration of future payments Reimbursement of past payments

Notes: a. Note that design options may not be mutually exclusive: hybrid policies containing elements of more than one option are possible. b. Note that information-oriented policies (e.g. provision of extension and technical assistance, government-developed digital tools to assist farmers’ participation in policy mechanisms etc.) are considered as part of the overall policy mix, either as an intrinsic part of the agri-environmental payment mechanism or as a complementary policy.

Setting clear policy objectives

The exact policy objectives have an influence on where to situate the policy measure along the above spectrum, as the possibility of linking specific farm actions or practices to broader environmental policy objectives differs across objectives.

An important consideration is whether policies should focus on a single objective or on several. Noting that there are many linkages between on-farm management actions and various environmental outcomes, setting multiple objectives may allow a policy to take advantage of synergies between objectives (Engel, 2016[11]). However, given that actions to achieve each single objective are unlikely to be perfectly correlated, there may be both synergies and trade-offs to consider. Also, setting multiple objectives increases policy complexity and may result in increased implementation challenges and transaction costs, which may hamper cost-effectiveness. Studies in favour of either approach are identifiable in the literature: for example, Meyer et al. (2015[16]) recommend agri-environmental policies focus on a single environmental objective, whereas others recommend setting objectives which take advantage of synergies between related environmental outcomes.

Whether single or multiple, for any cost-effective policy it is important that objectives are clearly defined and measurable. Often this is not the case in practice (Uthes and Matzdorf, 2013[17]; Wunder, Engel and Pagiola, 2008[18]). Objectives should be quantifiable to allow measuring whether policy goals have been achieved cost-effectively (Lankoski, 2016[5]). Biodiversity indicators or the definition of water quality levels could serve this purpose.

If suitable indicators can be identified, performance-based and result-based schemes tend to be advantageous and to deliver more cost-effective results (Börner et al., 2017[19]; Engel, 2016[11]; Allen et al., 2014[20]). Hitherto, result-based schemes have been used predominantly for biodiversity objectives and are claimed to be well suited, especially for the maintenance of existing environmental conditions (Herzon et al., 2018[21]; Schwarz et al., 2008[22]; Allen et al., 2014[20]; Bertke, Klimek and Wittig, 2008[23]). When targeting specific species, results-based schemes can also be advantageous (Matzdorf, Kaiser and Rohner, 2008[24]); paying Dutch farmers by clutch of meadow birds, for instance, proved to be more cost-effective than remunerating them for specific mowing restrictions (Verhulst, Kleijn and Berendse, 2007[25]; Musters et al., 2001[26]). Practice-based payments often do not effectively protect biodiversity, since correlation between the prescribed practices and the desired outcome is not guaranteed (Kleijn et al., 2001[27]; Kleijn et al., 2004[28]; Zechmeister et al., 2003[29]).

Due to its complexity, however, biodiversity is difficult to measure. While some authors therefore express caveats for result-based schemes, others point out that suitable indicator approaches, especially for grassland, have already been successfully identified and implemented (Peerlings and Polman, 2009[30]; Wittig, Kemmermann and Zacharias, 2006[31]; Bertke, Klimek and Wittig, 2008[23]; White and Sadler, 2012[32]; Moxey and White, 2014[33]; Diekmann, 2003[34]). Further research and development of digital tools may facilitate quantification, which should render result-based schemes more cost-effective in the future. Yet, for objectives with outcomes impossible or too costly to monitor, or which largely depend on external factors such as weather conditions, practice-based approaches may remain the more appropriate option (Engel, 2016[11]; Börner et al., 2017[19]; Hanley and White, 2013[35]).

O’Rourke (2020[36]) summarises some desirable characteristics of indicators for results-based policies:

• They should represent the environmental issue that the scheme proposes to address and be directly linked to the environmental objective of the programme and the payment basis.

• They should be mostly achieved by management practices and, to the extent possible, not be influenced by exogenous factors such as weather conditions.

• They should be easy to measure, quantify, and observe by the farmer.

• There needs to be a clear understanding on how farmers’ decisions affect the indicator.

Table 2.3 shows examples of indicators used in results-based schemes in OECD countries.

Table 2.3. Examples of indicators used in results-based agri-environmental schemes

Country	Name of scheme	Indicators	Payment basis
Australia	Box gum grassy woodland project (https://www.lls.nsw.gov.au/regions/south-east/grants-and-funding/thinking-inside-the-box-gum-grassy-woodland)	Conservation value score of box gum grassy woodland	Bid-based
Australia	Reef Trust Tender—Burdekin (https://www.nqdrytropics.com.au/reef-trust-tender2/ )	Reduction of nitrogen application (kg)	Bid-based
Austria	Humus-Program of the Ökoregion Kaindorf	Humus content in soil	Ton of CO2 sequestered in humus
Germany	Harrier nest protection in arable fields (Weihenschutz) - Nordrhein-Westfalen	Number of nests of certain bird species	Forgone income from protecting nests/Per nest
Germany	Coordinated grassland bird protection (Gemeinschaftlicher Wiesenvogelschutz) - Schleswig-Holstein	Presence of specific grassland-breeding birds species	Per hectare in those areas where birds have bred. The payment rate increases with the number of nests
Germany	Species-rich grassland (Artenreiches Dauergrünland) - Baden-Württemberg	Presence of minimum 4 or 6 flower species	Per hectare in those areas where species are found. The payment rate increases with the number of species
Germany	Species-rich grassland (Artenreiches Grünland – Kennarten) - Rheinland-Pfalz	Presence of minimum 4 or 8 grassland plant species	Per hectare in those areas where species are found. The payment rate increases with the number of species
Ireland	Sustainable agricultural plan for the Macgillycuddy reeks	Peatland scorecard	Based on habitat management costs and the peatland score
Ireland	Managing the habitats of the Aran islands	Habitat condition based on presence and abundance of specific species and management practices	Based on management costs
Ireland	Protecting farmland pollinators	Score obtained from the abundance and diversity of plants and pollinators, farm features and physical structures	Based on the score and quality of habitat
Ireland	The Burren programme	Score obtained from management practices and landscape characteristics	Based on cost incurred and income forgone. The payment rate increases with the score
Spain	Biodiversity in grasslands and improved hedges	Number of grassland species and hedges (characteristics and location)	Based on willingness to accept methods for participating into the programme

Source: Result based payments network (https://www.rbpnetwork.eu/).

Targeting

Heterogeneity is a natural feature of agriculture and environment linkages. There is a large spatial variation across landscapes with respect to productivity (and thus profitability of production) and environmental sensitivity. Numerous authors recommend a targeted policy design, which considers spatial variation of compliance costs and environmental benefits, to enhance environmental effectiveness and cost-effectiveness of policy (Espinosa-Goded, Barreiro-Hurlé and Ruto, 2010[37]; Broch et al., 2013[38]; Berry et al., 2005[39]; Matzdorf, Kaiser and Rohner, 2008[24]; Bartkowski et al., 2018[40]; Uthes and Matzdorf, 2013[17]; Wünscher, Engel and Wunder, 2008[41]).

Effectively addressing spatial variation of costs and benefits in policy implementation requires good data on the farmers’ compliance costs and environmental sensitivity. In turn, this leads to greater administration efforts and increased implementation costs (policy-related transaction costs). (Balana, Vinten and Slee, 2011[42]; Engel, 2016[11]; Falconer, 2000[43]; Uthes and Matzdorf, 2013[17]). However, some studies have shown that the efficiency gains from targeting can outweigh the implementation costs (Armsworth et al., 2012[44]; Lankoski, 2016[5]). For example, Lankoski (2016[5]) employs the ‘targeting gains ratio” to identify the budgetary cost-effectiveness gains from environmental targeting relative to the increase in transaction costs when more targeted payments are implemented. Targeting gains ratio varies across different payment designs, but is found to be as high as 28 in the best case, meaning that EUR 1 spent on public transaction costs for improved environmental targeting pays back EUR 28 through budgetary cost-effectiveness gains. Plausibly the gains from targeting are larger the greater the heterogeneity in costs and benefits (Wünscher, Engel and Wunder, 2008[41]; Armsworth et al., 2012[44]; Engel, 2016[11]).

A relatively inexpensive form of targeting is area-based targeting using geographical criteria, such as location near protected areas or proximity to watersheds, compared to data-intensive targeting at an individual farm-level (FAO, 2007[45]; Engel, 2016[11]). However, if environmental characteristics vary significantly within the area, site-specific environmental scores are a more suitable option. If farmers differ in their compliance costs, it may be useful to target low-costs sites and hence achieve a higher environmental performance with a given budget. Cost-targeting is often accompanied by payment differentiation (e.g. using discriminatory price auctions), remunerating the farmers by their compliance costs (Engel, 2016[11]). Wünscher et al. (2008[41]) propose that cost-targeting with payment differentiation may contribute the largest part of the increase in cost-effectiveness from improved targeting.

Cost-targeting is beneficial only when environmental benefits within region or across farms and field parcels are relatively homogeneous (Claassen, Cattaneo and Johansson, 2008[46]). With heterogeneity in both environmental benefits and compliance costs between farms, the two approaches should be combined to benefit-cost targeting. Specific performance scoring systems, such as the environmental benefit indices, allows policy makers to target the farms that achieve the highest environmental gains relative to costs (i.e. the agri-environmental payment) and thus improves budgetary cost-effectiveness. Numerous studies have identified efficiency gains from benefit-cost targeting (Claassen, Cattaneo and Johansson, 2008[46]; Uthes and Matzdorf, 2013[17]; Arbuckle, 2013[47]; Barton et al., 2003[48]; Wünscher, Engel and Wunder, 2008[41]; Lankoski, 2016[5]; Hardelin and Lankoski, 2018[6]).

Eligibility

Agricultural and agri-environmental policies mostly target farmers or landholders. They can address either individual farmers or groups of farmers or collectives. Furthermore, they can be restricted to certain characteristics, such as farm type or size, farmers’ income, education level or age (OECD, 2007[49]).

Agri-environmental payments are most often directed to individual farmers for their conservation efforts, but could alternatively be allocated to groups of farmers to receive remuneration for collective actions or environmental results at the landscape level. The preferred approach depends, for example, on the environmental issue in question. When a specific spatial pattern of measures is needed to achieve an environmental objective, such as broader biodiversity conservation, or protection of species with large habitats, cooperation between land-managers can be beneficial (Franks, 2011[50]; Engel, 2016[11]; Mills et al., 2012[51]). Several authors claim that coordinated action or community commitment improves environmental performance or efficiency of a scheme (Brouwer, Tesfaye and Pauw, 2011[52]; Le Coent and Thoyer, 2014[53]; Prager, Reed and Scott, 2012[54]; Uthes and Matzdorf, 2013[17]; Burton and Schwarz, 2013[55]; Franks, 2011[50]). It can also reduce the risk of leakage by preventing relocation of harmful activities to adjacent sites (Engel, 2016[11]).

Another benefit of co-ordinated action is the possibility of mutual learning and the creation of social capital (Lastra-Bravo et al., 2015[56]; Mettepenningen et al., 2013[57]). By exchanging knowledge, farmers can not only share best practices and foster innovation, but can also decrease compliance costs, for instance by sharing machinery costs (Polman, 2002[58]; Franks, 2011[50]; Mettepenningen et al., 2013[57]). On the other hand, the requirement of large capital expenditures, conflicts related to timing of machinery usage, monitoring of depreciation and variable costs might make potential payment recipients reluctant to be involved in a group scheme. Effects of group payments on transaction costs are ambiguous. Farmers within a target area may differ in risk preferences, environmental attitudes, cost of capital, and discount rates in a way that may increase the transaction costs of group action. Some authors have stated that by grouping beneficiaries, transaction costs are relatively lower (Goldman, Thompson and Daily, 2007[59]; Jones et al., 2009[60]), while others predict an increase of transaction costs owing to higher coordination efforts (Franks, 2011[50]; Mills et al., 2012[51]).

Collective contracts can have positive impacts on compliance and enforcement, since it can activate normative behaviour and peer monitoring (Brouwer, Tesfaye and Pauw, 2011[52]; Mills et al., 2008[61]; Cranford, 2014[62]; Hanley and White, 2013[35]; Sommerville, Jones and Milner-Gulland, 2009[63]), which might lower administrative costs (Dietz, Ostrom and Stern, 2003[64]; Dobbs and Pretty, 2008[65]; Mills et al., 2012[51]). On the other hand, many authors stress the importance of advisory systems or intermediary agencies to assist coordinated action, raising transaction costs for the implementing agency (Burton and Paragahawewa, 2011[66]; Moxey and White, 2014[33]; Riley et al., 2018[67]; Mills et al., 2012[51]; Franks, 2011[50]).

Another eligibility criterion can be the farm type, which has ramifications for participation rates and efficient attainment of environmental outcomes. Because of higher conservation benefits, several authors advocate for explicitly targeting extensive agricultural landscapes (Dahms et al., 2010[68]; Aviron et al., 2005[69]). Additionally, intensive farms request higher compensation payments than less intensive ones, increasing the cost of the policy (Breustedt, Schulz and Latacz-Lohmann, 2013[70]; Danne and Musshoff, 2017[71]). However, engaging with larger farms would allow benefiting from economies of scale, which contributes positively to the cost-effectiveness of the policy (Adams, Pressey and Stoeckl, 2014[72]; Espinosa-Goded, Barreiro-Hurlé and Dupraz, 2013[73]).

Behavioural aspects

The voluntary character of agri-environmental payments necessitates that policy design and implementation are attractive for farmers to ensure their participation. However, it is insufficient if payments are solely economically beneficial for farmers and neglect the fact that other factors might influence farmers’ decision-making. Thus, insights from behavioural economics can improve agri-environmental policy design in this respect. Dessart, Barreiro-Hurlé and van Bavel (2019[13]) identify three different types of factors, which play into farmers’ considerations, additional to purely economic reasoning:

• Dispositional factors which encompass the farmers’ personality, their risk attitudes and environmental concerns;

• Social factors such as preferences for interactions with other individuals, social norms and expectations;

• Cognitive factors which describe the ability of farmers to understand the benefits and costs that they are facing, their belief on outcomes and their own abilities to reach certain goals.

Acknowledging behavioural factors and addressing them through adequate policy design can increase participation and render agri-environmental programmes more effective (OECD, 2012[14]; Dessart, Barreiro-Hurlé and van Bavel, 2019[13]).

The most important dispositional factors are personal preferences on flexibility and inherent resistance to change, risk preferences and environmental concerns. Several studies have shown that farmers are resistant to change (Burton, Kuczera and Schwarz, 2008[74]; Sheeder and Lynne, 2011[75]). In Hermann, Mußhoff and Agethen’s study (2016[76]), for example, resistance to change was the reason for deterring conventional hog farmers from converting to organic practices. Relatively easy entry conditions with incremental increases towards more sustainable practices could be an effective solution (Öhlmér, Olson and Brehmer, 1998[77]; Schroeder et al., 2013[78]). Furthermore, specifically targeting farmers with positive or less reluctant attitudes towards change and sustainable land management can be beneficial. Both Dessart, Barreiro-Hurlé and van Bavel (2019[13]) and Falconer (2000[43]) recommend to segment sub-populations of farmers with similar attitudes, which is especially relevant for group payments.

Besides change, rigid management prescriptions may discourage farmers from enrolling in agri-environmental schemes. More flexibility and less stringent restrictions are preferred by farmers and can increase participation (Engel, 2016[11]; Darnhofer et al., 2017[79]; Wittig, Kemmermann and Zacharias, 2006[31]; Klimek et al., 2008[80]; Ruto and Garrod, 2009[81]; Wilson and Hart, 2001[82]; Dessart, Barreiro-Hurlé and van Bavel, 2019[13]).

Result-based payments are an effective way to provide flexibility for selection of environmental practices (Matzdorf, 2004[83]; Bräuer, Müssner and Marsden, 2006[84]; Musters et al., 2001[26]; Gorddard, Whitten and Reeson, 2008[85]; Andeltová, 2018[86]; Burton and Schwarz, 2013[55]), since farmers are free to achieve environmental results with the measures they consider the most appropriate. This allows greater cost-effectiveness (Casey and Boody, 2007[87]; Ferraro and Simpson, 2002[88]; Wunder, 2005[89]; Wätzold and Drechsler, 2005[90]; Casey and Boody, 2007[87]) because farmers may have better knowledge on local conditions and environmental interrelations (Andeltová, 2018[86]; Zabel and Roe, 2009[91]).

Farmers’ attitude towards risk is another relevant dispositional factor. Many authors state that risk preferences play a role in adoption of conservation contracts (Claassen, Cattaneo and Johansson, 2008[46]; Chèze, David and Martinet, 2017[92]; Kuminoff and Wossink, 2010[93]) and higher risks could possibly decrease scheme uptake (Loisel and Elyakime, 2006[94]). Others have claimed that “risk does not have a clear negative impact on the willingness to participate” (Matzdorf and Lorenz, 2010, p. 542[95]), also in (Trujillo-Barrera, Pennings and Hofenk, 2016[96]; Brouwer, Tesfaye and Pauw, 2011[52]).

Results-based schemes expose farmers to a higher uncertainty of the amount of payment and therefore necessitate a risk premium (Schwarz et al., 2008[22]; Andeltová, 2018[86]; Zabel and Roe, 2009[91]). The required premium is higher in cases where environmental outcomes are strongly influenced by external factors such as weather events or pests and hence beyond the farmer’s control (Schwarz et al., 2008[22]). In some cases these risks may decline over time when farmers gather experience and knowledge on effective management practices (Baumgärtner and Hartmann, 2001[97]; Burton and Schwarz, 2013[55]). This makes easy entry conditions and higher risk premiums possible policy solutions which are particularly relevant at initial stages of the scheme (Schroeder et al., 2013[78]).

Environmental concern is often mentioned to positively affect enrolment in agri-environmental schemes (Toma and Mathijs, 2007[98]; Best, 2010[99]; Läpple and Van Rensburg, 2011[100]). This calls for social marketing programs, such as media campaigns or agricultural education services aiming at raising environmental awareness (Cullen et al., 2018[101]; Dessart, Barreiro-Hurlé and van Bavel, 2019[13]). Relative to practice-based payments, result-based payments tend to increase social networking, knowledge sharing and intrinsic motivation for environmental conservation (Matzdorf, 2004[83]; Matzdorf, Kaiser and Rohner, 2008[24]; Andeltová, 2018[86]; Burton and Schwarz, 2013[55]).

Besides dispositional factors, social considerations are a crucial element in farmers’ decision making. It has been shown that behaviour of neighbouring farmers influences participation decisions (Damianos and Giannakopoulos, 2002[102]; Defrancesco et al., 2008[103]). Scheme adoption of neighbouring farmers positively affects contract uptake (D’Emden, Llewellyn and Burton, 2008[104]; Gillich et al., 2019[105]). Furthermore, there is evidence that peer pressure and social norms have ramifications on farmers’ decision to participate (Burton and Paragahawewa, 2011[66]; Emery and Franks, 2012[106]; Chen et al., 2009[107]) and fear of judgment by peers may increase the probability of scheme uptake (Emery and Franks, 2012[106]). Although Sattler and Nagel (2010[108]) downplay the relevance of judgment of others in adoption decisions, numerous authors stress that social networks can catalyse farmers’ behaviour (Polman and Slangen, 2008[109]; Mathijs, 2003[110]; Capitanio, Adinolfi and Malorgio, 2011[111]; Beckmann, Eggers and Mettepenningen, 2009[112]; Peerlings and Polman, 2009[30]). Peer effects can also help proliferate new practices. If learning is a primary barrier to the adoption of a practice or set of practices that achieve the desired performance or results, non-program participants may learn or be encouraged by their participating neighbors, and in turn later adopt practices, even without payment. If spillovers do occur, this increases the benefit-cost ratio of individual payment schemes as opposed to group payment schemes.

Cognitive factors play an important role in farmers’ decision-making. Here, it is important that the farmer fully understands the costs, benefits and risks and perceives them realistically. It has been shown that farmers’ perception of costs, benefits and risks may be distorted and does not always reflect the real measures (Michel-Guillou and Moser, 2006[113]; Doyle, 2012[114]; Bocquého, Jacquet and Reynaud, 2014[115]; Hardaker and Lien, 2010[116]; Kahneman and Tversky, 1979[117]). Immediate benefits weigh disproportionally more in farmers’ calculations than those in the future, and risks of high impact and low-probability extreme events, such as hail, tend to be overestimated (Doyle, 2012[114]; Bocquého, Jacquet and Reynaud, 2014[115]).

These knowledge issues can be tackled through adequate policy design by raising farmers’ awareness, education and training (Trujillo-Barrera, Pennings and Hofenk, 2016[96]). Access to relevant and reliable information is crucial for a farmer’s decision to participate in a scheme (Llewellyn, 2007[118]; Kallas, Serra and Gil, 2010[119]; Balderas Torres et al., 2013[120]). Precise information channelling and the provision of advisory systems are valuable policy elements (D’Emden, Llewellyn and Burton, 2008[104]; Dessart, Barreiro-Hurlé and van Bavel, 2019[13]), and can contribute to a reduction of communication costs and hence improve the efficiency of the policy (Defrancesco et al., 2008[103]).1 In order to tackle farmers’ tendency to value immediate costs more than long-term benefits, higher payments should be made at initial stages of the contract when farmers face high fixed costs (Duquette, Higgins and Horowitz, 2012[121]; Grolleau, Mzoughi and Thoyer, 2015[122]; Colen et al., 2016[123]; Dessart, Barreiro-Hurlé and van Bavel, 2019[13]).

Additionality

OECD (2012, p. 11[3]) defines additionality as “the extent to which the policy was a necessary condition for obtaining the targeted result”. Chabé-Ferret and Subervie (2013[124]) then define “windfall effects” as payments (or “windfall” gains) made in respect of actions which are not additional. For example, (Claassen, Duquette and Smith, 2018[125])show that additionality vary across practices for practice-based schemes in US agricultural conservation programs. Weak additionality means that a significant portion of payments are received by participants for implementing practices or management actions that would have taken place even in the absence of the payment. Achieving strong additionality contributes towards cost-effectiveness in that it limits budgetary outlays that do not directly deliver environmental benefits.

Assuring additionality necessitates clearly defined baselines and reference levels, which in practice are often lacking (Engel, 2016[11]; Wunder, Engel and Pagiola, 2008[18]; Ferraro and Pattanayak, 2006[126]; Casey and Boody, 2007[87]; Porras et al., 2011[127]). A baseline should consider the environmental condition at the beginning of the contract and incorporate the expected changes in external factors and land use that would have taken place in absence of the program (Naeem et al., 2015[128]; Claassen, Cattaneo and Johansson, 2008[46]).

However, a common practice is to use historic baselines (Angelsen and Wertz-Kanounnikoff, 2008[129]), which do not reflect likely future changes. While historic baselines can be computed relatively easily, they might punish farmers who have taken pro-environmental actions before the programme (Alpizar et al., 2013[130]).

Reference levels based on current environmental performance can mitigate aforementioned problem with historic baselines. By computing a farm-specific baseline, such as an environmental benefit index,2 the actual improvement can be measured and remunerated. Weinberg and Claassen (2006[131]) show an increase of environmental effectiveness by a factor of five when shifting from payments for “good performance” to payments for “improved performance”. Similar results have been found by Casasola et al. (2009[132]).

Result-based schemes directly link payments to environmental results (Matzdorf and Lorenz, 2010[95]) and hence have the potential to achieve high additionality (Burton and Schwarz, 2013[55]). Furthermore, result-based schemes provide incentives to enroll the best-suited land for provision of environmental results and thus enhance environmental effectiveness and prevent adverse selection (Matzdorf, Kaiser and Rohner, 2008[24]; Quillérou and Fraser, 2010[133]; Börner et al., 2017[19]; Burton and Schwarz, 2013[55]; Engel, 2016[11]). On the other hand, they also attract farms where the required results are already achieved or close to achievement (Uthes and Matzdorf, 2013[17]), which impedes additionality.

An effective definition of baselines is critical for additionality and cost-effectiveness (Bosch et al., 2013[134]; Porras et al., 2011[127]; Berkhout, Doorn and Schrijver, 2018[135]), and particularly important for result-based schemes (Burton and Schwarz, 2013[55]; Schwarz et al., 2008[22]). Setting the baselines right is a difficult exercise, requiring the availability of data and models on the landscape or even farm level (Claassen, Cattaneo and Johansson, 2008[46]; Wunder, Engel and Pagiola, 2008[18]). This is time-consuming and entails high transaction costs (Wunder, Engel and Pagiola, 2008[18]; Naeem et al., 2015[128]; Bosch et al., 2013[134]). The feasibility of baseline-setting affects whether practice-based or result-based payments are more suited (Berkhout, Doorn and Schrijver, 2018[135]). If administrative capacities are high and data is available at reasonable cost, allowing for the definition of effective baselines, result-based payments can lead to more effective environmental protection (Bosch et al., 2013[134]; Burton and Schwarz, 2013[55]). If this not the case, practice-based payments are a better alternative, since they do not require reliable monitoring of environmental improvements (Herzon et al., 2018[21]; Colombo and Rocamora-Montiel, 2018[136]). Moreover, for practice-based payments, additionality can sometimes be roughly inferred by whether the participant had undertaken practice in the past.

Tailoring

The payment rate has important implications for budgetary cost-effectiveness of the scheme. Any rate above the minimum payment necessary to guarantee participation will overcompensate farmers for income forgone and extra costs incurred, thus reducing the number of participants possible under a fixed budget, and hence reducing both environmental effectiveness and budgetary cost-effectiveness (Brouwer, Tesfaye and Pauw, 2011[52]; Börner et al., 2017[19]; Ferraro, 2008[137]; Lankoski, 2016[5]) (Balderas Torres et al., 2013[120]; Bastian et al., 2017[138]; Dickinson et al., 2012[139]; Miller et al., 2011[140]; Farmer et al., 2011[141]; Farmer, Chancellor and Fischer, 2011[142]).

A minimum payment covers the farmers’ compliance costs with the scheme. These are comprised of the income forgone from practice adoption (opportunity costs) and the farmers’ private transaction costs (Wunder, Engel and Pagiola, 2008[18]; Falconer, 2000[43]; Berkhout, Doorn and Schrijver, 2018[135]; Engel, Pagiola and Wunder, 2008[143]; Mettepenningen et al., 2013[57]). Several authors point out that payments solely based on opportunity costs without transaction costs are insufficient (Wünscher and Engel, 2012[144]; Wünscher, Engel and Wunder, 2008[41]; Falconer, 2000[43]). Likewise, payments should account for possible shifts in opportunity costs or market prices over time (Herzon et al., 2018[21]; Niens and Marggraf, 2010[145]; Russi et al., 2016[146]).

The alternative remuneration method is to link payments to the social value of the environmental benefit that has been created (Berkhout, Doorn and Schrijver, 2018[135]; Hanley and White, 2013[35]; Engel, 2016[11]). Payment based on the value of environmental benefits is particularly relevant for the result-based payment schemes, where the achievement of environmental results provides the basis for payments. However, monetary valuation (social valuation) of environmental results is challenging and even for result-based schemes in which the payment is linked to the quantity or quality of the environmental result, the payment calculation may still be based on income foregone and extra costs incurred (Herzon et al., 2018[21]; Schwarz et al., 2008[22]; Lankoski, 2016[5]).

Many studies show that farmers request higher payments for accepting restrictive contracts and reduced flexibility. Ruto and Garrod (2009[81]) stated that farmers request greater financial incentives for lower flexibility. Espinosa-Goded, Barreiro-Hurlé and Ruto (2010[37]) showed that higher payments increased farmers’ willingness to participate in schemes that required a change in farm management practices. Multiple studies confirm that lower payments were needed for less rigid contracts in terms of management prescriptions, contract length and paperwork involved (Espinosa-Goded, Barreiro-Hurlé and Ruto, 2010[37]; Ruto and Garrod, 2009[81]; Christensen et al., 2011[147]; Lastra-Bravo et al., 2015[56]; Breustedt, Schulz and Latacz-Lohmann, 2013[70]).

Allowing for more flexibility in management practices lowers the remuneration needed as farmers can select the least-cost practices for themselves and therefore increased flexibility can decrease overall budgetary costs. Result-based payments provide more flexibility in this regard as farmers can more freely choose the practices to attain the desired environmental result (Schwarz et al., 2008[22]; Matzdorf and Lorenz, 2010[95]; Zabel and Roe, 2009[91]; Schilizzi, Breustedt and Latacz-Lohmann, 2011[148]; Matzdorf, 2004[83]; Schilizzi and Latacz-Lohmann, 2016[149]; Lankoski, 2016[5]). Furthermore, higher flexibility can reduce farmers’ compliance costs providing an incentive to identify the most efficient options (Matzdorf and Lorenz, 2010[95]; Weinberg and Claassen, 2006[131]; Wätzold and Schwerdtner, 2005[150]; Burton and Schwarz, 2013[55]).

On the other hand, result-based schemes expose farmers to higher uncertainty of payments and therefore may necessitate risk premiums.3 If these risk premiums are higher than the compensation needed for reduced flexibility, a practice-based scheme might reduce the needed payment level (Schilizzi, Breustedt and Latacz-Lohmann, 2011[148]; Schilizzi and Latacz-Lohmann, 2016[149]).

Short-term contracts are the preferred option by farmers giving them flexibility on their farm management in the future (Engel, 2016[11]; Balderas Torres et al., 2013[120]; Berkhout, Doorn and Schrijver, 2018[135]; Miller et al., 2011[140]). This is especially relevant for practice-based schemes, where farmers value the option to change management practices when the contracts expire (Ruto and Garrod, 2009[81]; Lütz and Bastian, 2002[151]; Pasquini et al., 2009[152]). Shorter commitments allow farmers to stay responsive to changes in future market conditions, which might affect their opportunity costs (Engel, 2016[11]; Niens and Marggraf, 2010[145]; Russi et al., 2016[146]). Furthermore, short contracts allow trying out the policy for both the farmer and the implementing agency, if the scheme is in its initial stages (Engel, 2016[11]; Christensen et al., 2011[147]).

Some environmental objectives might require long-term commitments, calling for longer contracts, such as biodiversity conservation (Ruto and Garrod, 2009[81]; Berkhout, Doorn and Schrijver, 2018[135]). Long-term contracts can assure long-term provision of the environmental service and can help to assure conditionality (Engel, 2016[11]). For result-based schemes long-term contracts are beneficial, since the rationale of result-based payments is that farmers build up knowledge and develop new skills and innovation over time (Burton and Schwarz, 2013[55]; Baumgärtner and Hartmann, 2001[97]; Wittig, Kemmermann and Zacharias, 2006[31]). The aforementioned inherent risk of result-based schemes may also decline with increasing experience (Burton and Schwarz, 2013[55]; Casey and Boody, 2007[87]; Wätzold and Drechsler, 2005[90]). Additionally, it might take time until environmental outcomes can be observed (Schwarz et al., 2008[22]; Herzon et al., 2018[21]; Hasund, 2013[153]). Therefore scheme success might be increased with long-term contracts (Mccracken et al., 2015[154]; Sattler and Matzdorf, 2013[155]).

Uniform fixed payment is currently the most commonly used option for agri-environmental schemes (Engel, 2016[11]; Latacz-Lohmann and Schilizzi, 2005[156]; Schwarz et al., 2008[22]; Schilizzi, Breustedt and Latacz-Lohmann, 2011[148]). Its advantage is easier implementation and thus smaller transaction costs due to lower information needs (OECD, 2007[49]). If environmental benefits and compliance costs are relatively homogeneous among farmers and can be reasonably estimated, uniform payments are advisable (Mills et al., 2012[51]; Lankoski, 2016[5]).

Conversely, if compliance costs or environmental benefits differ largely among participants, uniform payments fail to account for this heterogeneity (OECD, 2007[49]; Latacz-Lohmann and Breustedt, 2019[157]; Hasund, 2013[153]; OECD, 2010[1]; Lankoski, 2016[5]), leading to overcompensation of farmers with low compliance costs (Groth, 2005[158]; Berkhout, Doorn and Schrijver, 2018[135]; Armsworth et al., 2012[44]; Schwarz et al., 2008[22]; OECD, 2010[1]), while farmers with high compliance costs, who could potentially deliver large environmental benefits, will not enter into the scheme, since they would be undercompensated (Groth, 2005[158]; Berkhout, Doorn and Schrijver, 2018[135]; Schwarz et al., 2008[22]; Wünscher, Engel and Wunder, 2008[41]).

Under heterogeneous conditions, uniform payments therefore reduce the budgetary cost-effectiveness of the scheme due to high information rents (an overcompensation of a farmer’s income forgone and extra costs incurred) for low compliance cost farmers and adverse selection (Armsworth et al., 2012[44]; Wünscher, Engel and Wunder, 2008[41]; Holm-Mueller, Radke and Weis, 2002[159]; OECD, 2010[1]; Lankoski, 2016[5]). The choice between standardised and less costly fixed payments and more complex differentiated payments hence depends on the heterogeneity among farms regarding compliance costs and environmental benefits (OECD, 2007[49]; Börner et al., 2017[19]; Wätzold and Schwerdtner, 2005[150]).

In contexts with high variability among farmers, payments can be differentiated, either on the basis of compliance costs or environmental benefits (Hanley and White, 2013[35]). Numerous authors have confirmed increased environmental effectiveness and cost-effectiveness with differentiated payments (Engel, 2016[11]; Porras et al., 2011[127]; Ezzine-De-Blas et al., 2016[160]; Groth, 2005[158]; Wünscher, Engel and Wunder, 2008[41]; Lankoski, 2016[5]). However, differentiated payments require more information than uniform payments and thus have higher transaction costs that reduce the potential cost-effectiveness gains from payment differentiation. (Engel, 2016[11]; Börner et al., 2017[19]; Börner et al., 2017[19]; Wätzold and Schwerdtner, 2005[150]; OECD, 2010[1]; Lankoski, 2016[5]). Another concern is that differentiated payments might be perceived as unfair by policymakers and farmers (Latacz-Lohmann and Schilizzi, 2005[156]; Wunder, Engel and Pagiola, 2008[18]; Holm-Müller and Hilden, 2004[161]).

Differentiated payments based on compliance costs require information on farmers’ compliance costs and farmers might not have incentives to reveal their true costs. This information asymmetry reduces the cost-effectiveness of the agri-environmental schemes (Engel, 2016[11]; Börner et al., 2017[19]; OECD, 2007[49]; Wunder, Engel and Pagiola, 2008[18]; Latacz-Lohmann and Breustedt, 2019[157]; Cooper, Hart and Baldock, 2009[162]; Canton, De Cara and Jayet, 2009[163]). One mechanism to overcome the asymmetric information problem are self-selection contracts. The implementing agency offers different contract types, which farmers choose depending on their own characteristics and hence reveal their preferences and compliance costs (Wu and Babcock, 1996[164]; Ozanne and White, 2007[165]; OECD, 2010[1]). Another mechanism to overcome information asymmetry is bidding mechanisms such as auctions, which have already been used in practice, for example, in the Conservation Reserve Program in the United States (Claassen, Cattaneo and Johansson, 2008[46]) and in the Victorian Bush Tender Trial (Cocklin, Mautner and Dibden, 2007[166]; Stoneham et al., 2003[167]; Vukina, Levy and Marra, 2006[168]; OECD, 2010[1]).

Through competitive bidding for agri-environmental contracts, farmers reveal their compliance costs, which reduces information rents and overcompensation and hence increases budgetary cost-effectiveness (Berkhout, Doorn and Schrijver, 2018[135]; Costedoat et al., 2016[169]; Herzon et al., 2018[21]; Boxall, Perger and Weber, 2013[170]; Stoneham et al., 2003[167]; Schillizzi and Latacz-Lohmann, 2007[171]; Glebe, 2008[172]).

In various studies auctions outperform fixed uniform payments with cost-effectiveness gains between 16% – 315% (without transaction costs) (Latacz-Lohmann and Schilizzi, 2005[156]). The gains in budgetary cost-effectiveness of auctions, however, are highly dependent on the magnitude of additional transaction costs relative to more simple payment designs (Glebe, 2008[172]; OECD, 2010[1]; Lankoski, 2016[5]). Lankoski (2016[5]) incorporates transaction costs of different payments designs (uniform payment, differentiated payments and auctions) for budgetary cost-effectiveness analysis of payments promoting biodiversity enhancement in farmland and finds that auctions and differentiated payments perform better than uniform payment even when transaction costs are accounted for (cost-effectiveness gain is 16% for auction and 5% for differentiated payment).

Following Lankoski (2016[5]) the relative performance of different payment designs, from uniform payments to results-based payments, depends on the extent to which opportunity costs and environmental quality4 vary across participants as illustrated in Table 2.4. Uniform payment works well when both opportunity costs and environmental quality are homogenous. When opportunity costs vary but environmental quality is homogenous then differentiated payment on the basis of costs would perform better and be fairer than uniform payment. In this case, an auction system would also perform well, but differentiated payment would probably be an easier and more flexible system when opportunity costs are reasonably well known. When environmental quality varies, the added value of auction systems and other targeting mechanisms (results-based or differentiated payments) increases. In these cases, auctions work well when the number of potential participants (bidders) is large, and results-based payment would be best suited for situations where the number of participants is low. When environmental quality varies, efficiency requires that auction and other mechanisms employ an environmental scoring system to address environmental heterogeneity, e.g. use of environmental benefit index. Also, policy-related transaction costs affect the efficiency of alternative payment types and thus auctions may be preferred to results-based schemes when potential pool of participants is large.

Table 2.4. Suitability of different payment types under homogenous and heterogeneous spatial conditions

Environmental quality	Opportunity costs
	Homogenous	Heterogeneous
Homogenous	Uniform payment (N, n)	Differentiated payment-cc (N, n)
Heterogeneous	Differentiated payment-eb (N,n) Auction-eb (N) Results-based payment-eb (n)	Differentiated payment–cc and eb (N,n) Auction–cc and eb (N) Results-based payment–cc and eb (n)

Note: N = works well with large number of participants; n = suitable for small number of participants; cc = differentiated on the basis of compliance costs; eb = differentiated on the basis of environmental benefits.

Source: Lankoski (2016[5]).

Conditionality and enforcement

Achieving strong conditionality means that farmers participating in an agri-environmental schemes receive remuneration if and only if they actually deliver the agreed action, practice, performance, or result as specified in their contract (Hardelin and Lankoski, 2018[6]; Engel, Pagiola and Wunder, 2008[143]; OECD, 2007[49]; Rojas and Aylward, 2003[173]). This assures that the payments are spent for actual environmental improvements, or actions leading to those improvements, and that the policy is cost-effective. Monitoring and controls are key elements to guarantee conditionality and increase compliance (Engel, Pagiola and Wunder, 2008[143]; OECD, 2007[49]; Porras et al., 2011[127]; Naeem et al., 2015[128]; Grammatikopoulou, 2016[174]).

The feasibility of monitoring and control of the measures may be challenging and comes with considerable costs (Berkhout, Doorn and Schrijver, 2018[135]; Claassen, Cattaneo and Johansson, 2008[46]; Chaplin, 2018[175]; Schwarz et al., 2008[22]). Porras et al. (2011[127]) state that if a practice-based scheme is based on easily observable land-use measures then it probably has lower monitoring costs than results-based schemes.

Monitoring costs depend on the environmental objective and its measurability. Some environmental goals are difficult to quantify and to measure, and thus quantified measurements for environmental performance are lacking (Brouwer, Tesfaye and Pauw, 2011[52]; Moxey and White, 2014[33]; Kaiser et al., 2010[176]; Burton and Schwarz, 2013[55]; White and Sadler, 2012[32]; Matzdorf, Kaiser and Rohner, 2008[24]). When it is impossible to define clear indicators or when monitoring of results is more costly than monitoring of practices, practice-based payments are easier to enforce and can be more appropriate (Herzon et al., 2018[21]; Hanley and White, 2013[35]; Allen et al., 2014[20]; Börner et al., 2017[19]).

Moral hazard is a concern for agri-environmental payments. Farmers might have incentives to cheat, receiving the compensation without implementing the required practices and thus incurring the full compliance costs for their commitment. This is particularly the case for farmers with high compliance costs, since their pay-off for cheating is higher than for other farmers (Latacz-Lohmann and Schilizzi, 2005[156]; OECD, 2010[1]).

Controls and monitoring can only be effective if non-compliance is detected and penalised (OECD, 2007[49]; Wunder, Engel and Pagiola, 2008[18]). Sanctions usually include the cancellation of future payments or sometimes past payments have to be paid back (Brouwer, Tesfaye and Pauw, 2011[52]; Wunder, Engel and Pagiola, 2008[18]; Engel, 2016[11]). While the rationale for fines is to achieve high compliance levels, excessive penalties can actually reduce compliance or participation because farmers might perceive them as demotivating (Engel, 2016[11]; Börner et al., 2017[19]; Falk and Kosfeld, 2006[177]; Vollan, 2008[178]). Herodes (2008[179]) finds that unclear control criteria entail widespread reluctance among farmers for scheme participation. Thus, it is important to have clear control criteria that are linked to farmer-controlled variables to enhance scheme participation.

Successful enforcement requires setting the levels of the following elements appropriately: 1) intensity of monitoring, 2) level of sanctions, 3) stringency of compliance requirements, and 4) level of agri-environmental payments (Latacz-Lohmann, 1998[180]). Several authors have derived optimal monitoring and sanction strategies in the context of agricultural and agri-environmental policies (Choe and Fraser, 1999[181]; Ozanne, Hogan and Colman, 2001[182]; Kampas and White, 2003[183]; Fraser, 2002[184]).

In practice, monitoring rates for agri-environmental payments are relatively low in developed countries and lie between 3% and 5% (Wunder, Engel and Pagiola, 2008[18]; OECD, 2010[1]). Many programmes lack effective enforcement strategies (Hart and Latacz-Lohmann, 2005[185]), which could reduce environmental effectiveness (Ezzine-De-Blas et al., 2016[160]). Yet, enforcement is a crucial element for conditionality and hence an important driver for the cost-effectiveness of the policy.

2.3. Policy Spectrum Framework: Assessment of payment design options according to key dimensions of cost-effectiveness

Table 2.5 provides an assessment of policy design options covered by the Framework. This assessment is based on the literature review and Secretariat’s judgement. It does not aim to identify the single “best” policy design (no “one size fits all” approach), but rather discusses advantages and disadvantages of different options, presenting evidence from literature review, policy simulations and economic experiment on performance of different policy options according to the key criteria (primarily cost-effectiveness, but also other criteria as discussed above, where warranted). In Table 2.5, uniform and targeted practice-based payments are discussed in the same column, since both are practice-based options. Aggregate or landscape-level results-based payment is not separately presented it represents a special case of results-based payment.

Table 2.5. Qualitative assessment of payment design options from a cost-effectiveness viewpoint

Policy design element	Uniform or targeted practice-based payment	Performance-based payment	Result-based payment	Hybrid payment
Quantifiable policy objectives	May be preferred option only when environmental performance or results are very difficult or costly to measure. Works if practices are highly correlated with environmental performance or results and quantitative targets are set e.g. for acreage or number of participants.	Improves cost-effectiveness if suitable environmental performance proxies are available, such as environmental benefit indices and nutrient balances. Suitable option if direct environmental results cannot be measured.	Improves cost-effectiveness if environmental results can be reliably measured or suitable indicator approaches are available, for example, in the context of biodiversity.	Improves cost-effectiveness relative to pure practice-based approach.
Targeting	Uniform practice-based payment has poor cost-effectiveness when there is spatial heterogeneity in compliance costs and/or environmental benefits. Targeted (whether cost-targeting or benefit targeting or benefit-cost targeting) practice-based payment improves cost-effectiveness relative to uniform payment.	Improves cost-effectiveness by allowing spatial targeting based on environmental proxies (e.g. nutrient surplus or environmental benefit index value).	Improves cost-effectiveness by allowing spatial targeting based on environmental benefits or benefit-cost ratios.	Improves cost-effectiveness by allowing spatial targeting based on environmental benefits or benefit-cost ratios.
Tailoring	Uniform payment rate works only when compliance costs are homogeneous among farmers, which is rarely the case. Poor cost-effectiveness due to overcompensation of compliance costs to low-cost farmers (information rent). High-cost farmers with potentially high environmental benefits do not participate (adverse selection).	Payment rate can be tailored, for example, by providing differentiated payment rate on the basis of environmental performance. Combination of competitive bidding (auctions) and environmental benefit index would allow benefit-cost targeting that highly improves cost-effectiveness as auction mechanism reduces information rent and environmental benefit index targets high benefit sites.	Improves cost-effectiveness as payment rate can be tailored to reflect environmental results achieved. The uncertainties associated with the achievement of the results may require a risk premium for risk-averse farmers, which reduces budgetary cost-effectiveness.	Payment rate can be tailored according to compliance costs of adopting the practices and environmental results achieved. Reduces the financial risk for farmers as compliance costs are covered for practice adoption.
Additionality	Option only when environmental performance or results are very difficult or costly to measure. Can provide additionality, if practices are highly correlated with environmental performance or results, and practices would not have been adopted without payment.	Enables payment for the environmental performance improvement and thus increases environmental effectiveness, additionality and budgetary cost-effectiveness.	Result-based payment directly linked payment to environmental results and hence has the potential to achieve high additionality, environmental effectiveness and budgetary cost-effectiveness. However, if payment is linked to maintaining already achieved results then additionality is low.	Bonus payment (result-based payment) is directly linked to environmental results so there is high potential for additionality.
Enforcement	Monitoring and enforcement should be relatively easy for observable measures, such as land use and land cover based measures. But, is more difficult for unobservable measures, such as chemical fertiliser, pesticide and manure application intensity. May be preferred option If practices can be monitored and enforced more easily and with much lower transaction costs than performance-based or results-based payments.	If environmental performance improvements can be clearly defined and monitored then performance-based payments can be beneficial. However, this requires suitable environmental performance indicators that may be lacking for some environmental objectives. When it is impossible to define clear performance indicators or when monitoring of performance is more costly than monitoring of practices, practice-based payments may be easier to enforce and can be more appropriate	If results can be clearly defined and monitored then result-based payments can be beneficial. However, this requires suitable and reliable indicator approaches that may be lacking for some environmental objectives. When it is impossible to define clear indicators for results or when monitoring of results is more costly than monitoring of practices, practice-based payments may be easier to enforce and can be more appropriate	May be beneficial if practices are easily observed, monitored and enforced.
Transaction costs	Transaction costs (both public and private) should be relatively low for uniform practice-based payment and this is especially the case when practices are relatively easy to observe, monitor and enforce (e.g. land use based measures). Targeted uniform payments will increase transaction costs somewhat as information is required, for example, on spatial variation of potential environmental benefits of practice adoption.	Differentiated payments and bidding mechanisms have higher transaction costs than uniform payments due to higher information needs, including information related to spatial variation of environmental benefits and/or compliance costs. Also the development of suitable environmental performance indicators that can be tailored to local circumstances adds complexity and transaction costs.	Transaction costs can be reduced if reliable result indicators based on up-to-date data are readily available and if these are relatively easy to understand and measure by farmers, which allows self-monitoring by farmers.	Transaction costs may be high as both practices and results need to monitored and enforced.
Behavioural factors	Provides rigid management proscriptions without farm-specific flexibility that are not necessarily the least-cost ways to achieve environmental objectives. Does not provide incentives for innovation. Financial risk lower than with the performance-based and the results-based payments, especially when environmental performance and results are dependent on external factors (e.g. weather) outside of the farmers’ control.	Increases flexibility and fosters innovation, which promotes the least cost achievement of the environmental performance targets. If environmental performance scores are dependent on factors outside of farmers’ control then may increase financial risk relative to the practice-based payment.	Increases flexibility and foster innovation, which promotes the least cost achievement of the environmental results. Relative to the practice-based payments the result-based payments tend to increase social networking, knowledge sharing and intrinsic motivation for environmental conservation. However, relative to practice-based payments the results-based payments may increase financial risk for farmers and thus may increase a risk premium required by risk-averse farmers.	Relative to the pure results-based payment decreases flexibility and innovation and thus potentially cost-effectiveness. On the other hand is less risky option to risk-averse farmers which may increase acceptance and participation.

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Annex 2.A. Economic experiment component: Summary of findings from choice experiment literature review

Annex Table 2.A.1. Choice experiment design features: Selected results from the literature

Source	Country and region	Time period	Choice variables	Sample size	No. choice sets	Survey delivery method	Payments for	Estimation method
Adams et al (2014[72]), Adams et al (2012[186])	AUS	May to September 2009	Three labelled attributes [Conservation covenant; Conservation management agreement; Sell] - Payment (as a % of stewardship costs): (0%; 50%: 100%: 150% for 2 attributes, Market value for Sell attribute) - Configuration: (From no land set aside to one small patch; From no land set aside to several patches; From no land set aside to one continuous area; From one small patch set aside to several patches; From several patches set aside to one continuous area)	92	80: 8 blocks of 10 choice sets (no opt-out)	Face-to-face and mail	Stewardship Program	Conditional mixed-effects logit
Alló et al (2015[187])	SPA	summer 2012	- Payment (EUR, per ha): (30; 60; 90; 120) - Flexibility (how much of the total area enrolled in the contract can be excluded without penalty each year): (0%; 40%) - Fine in addition to the return of the payment (EUR, per ha): (0; 200) - Cover crop area: (0%; 20%) - Restriction on land use: (No restrictions; April 1–August 1)	359	8: 2 blocks of 4 choice sets	face-to-face	AES for bird protection	Ordered logit model
Bastian et al (2017[138])	US	?	- Easement length: (Perpetuity; 20-25 years) - Public access: (Y/N) - Inclusion of wildlife habitat: (Y/N) - Restricted managerial control: (Y/N) - Financial benefit (% of the average land market value): Income and estate tax benefit plus (0; 25; 50; 45; 100)% of average market value of land	Landowner 2101 / Land trust 291	24: (Landowners: 12 blocks of 2 choice sets), (Land trusts: 6 blocks of 4 choice sets)	Mail	conservation easements	RPLM
Beharry-Borg et al (2013[188])	GBR	January and June 2009	- Inorganic fertiliser application per acre: (current level; 25% less; 50 % less) - Farmyard manure application per acre: (current level; 25% less; 50 % less) - Blocking of drainage ‘grips’: (All; Block 50% of existing grips; Block 100% of existing grips) - Contract length (years): (3; 5; 10) - Compensation payment (GBP per ha per year): (2; 4; 10; 16; 22; 28)	97 (6 protest respondents are included)	18: 3 blocks of 6 choice sets	face-to-face	water quality protection	CLM and LCM
Bennett et al. (2018[189])	CHN	2012	- Contract length (year): (1; 5; 10) - Release option: (Cannot leave the programme; Can leave the programme without a penalty) - Land area enrolled (% of household land area): (20; 50; 100) - Annual pesticide use reduction (% in comparison to 2011): (5; 10; 20; 30) - Annual cash subsidy (CNY, per mu=1/15ha): (10; 50; 80; 120)	288	72: 9 blocks of 8 choice sets	face-to-face (Not mentioned clearly)	Coastal Wetlands Protection	CLM
Breustedt et al. (2013[190]) (in German)	GER	Summer 2010	3 labelled attributes [Grassland area enrolled (minimum %): (5; 10; 20)] - Fertilization: (Allowed organic and mineral fertilizers; Allowed organic fertilizer; No fertilizer allowed) - First mowing not before ...: (June 1st; June 22nd) - Maximum number of grazing animals (1 animal = 1 cow or 3 sheep, per ha): (2; 3; 4) - Duration of the contract (year): (1; 5; 10) - Annual compensation (EUR/ha): (250; 350; 450)	68	63: 8 blocks of 8 choice sets ( - 1 for a dominant choice)	face-to-face	AES for grassland	CLM
Broch et al. (2013[38]) and Broch and Vedel (2011)	DEN	Jan-Feb 2009	- purpose of afforestation (groundwater; recreation; biodiversity) - option of cancelling the contract (within 5 years; within 10 years; binding) - monitoring (visit) by authorities (0%, 1%, 10%, 25%OTSC) - compensation level (one-time compensation per hectare: EUR0; EUR3600-5600 in EUR400 steps)	842	36: 6 blocks of 6	Web-survey	afforestation	RPLM - Error Component Specification
Brouwer et al (2015[191])	GER	May-September 2012	- Area size (% of the farmer's total area of cultivated land): (10; 25; 50) - Forest type: (Commercial production forest; Non-commercial natural forest) - Availability of technical advice: (Y/N) - Public recreational access: (Y/N) - Return to farmland end of the contract: (Y/N) - Contract duration (years): (10; 25; 50) - Compensation (EUR/ha/year): (250; 500; 750; 1000; 1500; 2000)	Netherlands: 273, Germany: 206	120: 20 blocks of 6  choice sets	face-to-face	Afforestation agreement	RPLM and Latent class (LC) models
Chen et al. (2009[107])	CHN	May-August 2006	- Conservation payment (100 to 300 yuan/mu with an intermediate value of 200) ***after the first quarter of the survey, the high payment level was adjusted to 250 yuan/mu*** - Programme duration (3; 6; 10 years) - Neighbours’ behaviours (25%, 50%, or 75% of households in the same group would reconvert part or all of their enrolled land plots)	304	9: 3 blocks of 3 choice sets	Face-to-face	maintaining forest on their (GTGP) land plots (although experiment related to maintaining forest after programme ends)	PM
Chèze, David and Martinet (2017[92])	FRA	June 2016 to February 2017	- Profit variation: variation in average annual gross margin per hectare (-EUR50; 0; +EUR50; +EUR100) - Harvest risk: variation in no. years with poor harvest out of ten (0; +1 year; +2 years) - Additional commitments compared to SQ: (none; join AES; joining a Charter (flexible commitment); green certification) - Reduction in health / environmental exposure cf SQ: (0%; -20%; -50%; -80%)	83	16, blocked into 2 groups = 8 choice sets per respondent	Face-to-face and web-survey	Reduced use of pesticides	CLMand RPLM
Christensen et al. (2011[147])	DEN	December 2009-January 2010	- management flexibility: buffer zone width (6m; 6-24m) - management flexibility: use of artificial fertiliser and pesticides in buffer zone (fertiliser can be used; no pesticides or artificial fertiliser) - assistance with contract application (free assistance from extension service, common application form) - contract length (1 year; 5 years) - option to be released from a contract before it expires (no; released without costs once per year) - compensation level (per ha per year: EUR134; EUR 228; EUR 336, EUR510)	444	8	Web-survey	pesticide-free buffer zones	RPLM
Costedoat et al. (2016[169])	MEX	November through December 2014	- Forest parcels: (Individual decision; Negotiated by the community assembly; All forests of the ejido) - Technical intermediary: (External service provider; Community technician; CONAFOR) - Payment (MXN, per ha per year): (250; 500; 1000; 2000) - Use of payment: (100% cash; 50% cash + 50% collective agricultural productive project (tractors); 50% cash + 50% social project (community public good))	82	18: 3 blocks of 6 choice sets	face-to-face	PES for biodiversity-related ecosystem services	CLM and Latent class (LC) models
Danne and Musshoff (2017[71])	GER	January to March 2016	- Programme Financing: (Dairy company scheme; Governmental scheme; Food industry scheme) - Annual grazing period (days per year): (120; 150; 180) - Daily grazing period (hours per day): ( 6; 8; 16) - Feeding standards: (Staple feed consisting only of green fodder; Concentrated feed reduced by 20%; Amount of maize silage reduced by 30%) - Price Premium (eurocents per kg raw milk): (1; 3; 5)	293	12	Web-survey	Pasture grazing programmes	RPLM
Dhingra et al. (2015[192])	US	spring, summer and fall of 2014	- Maximum payment (% of NRCS local county rental rates: 80%; 100%; 120%) - Terms of contract payment (fixed at start; re-adjusted every 5 years) - Contract length (10 years; 15 years) - Establishment cost sharing (50%; 100% government cost share) - Land use restrictions (Idle; graze/hay every other year)	76	23	Face-to-face	Participation in US Conservation Reserve Programme (land retirement)	Exploded logit model with no ties in ranking
El Mokaddem et al. (2016[193]) (in French)	Morocco	?	- Collective development of land for anti-erosion (ha): (0; 1000; 3000; 5000) - Area with a specific pasture for sheep grazing (in proportion to the collective pasture area): (0; 1/4; 1/3; 1/2) - Planting fruit trees (in total number of trees planted): (10; 20; 50; 100) - Payment (MAD (1EUR=11,047MAD), per household per year): (200; 400; 500; 600) - Technical assistance (7 days/year): (None, Plant Production, Animal Production, Mixed)	144	16: 2 blocks of 8 choice sets	face-to-face	Common-pool pastures conservation	CLM
Espinosa-Goded and Barreiro-Hurlé (2010[194]) (in Spanish)	SPA	?	- Flexibility on the area under AES: (Free; 50% eligible area) - Flexibility over grazing/harvesting (Free; Prohibited between August and September) - Presence of a mandatory and free technical assistance service: (Y/N) - Fixed payment of EUR1,000 per contract regardless of the area (to cover adoption costs of AES): (Y/N) - Premium level (EUR, per year per ha): (60; 80; 100; 120)	200	96: 16 blocks of 6 choice sets	face-to-face	AES for Nitrogen Fixing Crops in rain-fed areas	RPLM - Error Component Specification
Espinosa-Goded, Barreiro-Hurlé and Ruto, (2010[37])	SPA	June–August 2008	- a compulsory enrolment of 50% of eligible area (free; 50% of eligible surface) - flexibility over grazing in the land under the AES (Free; limited, taking into account RDP specifications for specific regions) - fixed one-off payment of EUR1000 as part of the contract (yes; no) - availability of a compulsory and free of charge technical training and advisory service (yes; no) - per ha premium level (per year) (EU/ha: 60; 80; 100; 120)	300	96: 16 blocks of six choice sets	Face-to-face	introduction of nitrogen fixing crops in dry land areas	RPLM - Error Component Specification
Franzén et al. (2016[195])	SWE	?	- Annual subsidy (SEK, per ha): (Arable land 3000 (Other land use 1500); Arable land 4000 (Other land use 2250)) - Time frame for subsidy and commitment (year): (Min commitment 5 (Max extension 20); Min commitment 10 ( Max extension 30)) - Practical support: (No practical assistance for projecting and design of wetland; A collaboration forum, and practical assistance with projecting and designing a wetland) - Economic compensation for construction (% of cost within ceiling): (50–90; 100) - Cost ceiling for compensation (SEK): (100,000; 200,000)	29	8: 2 blocks of 4 choice sets	Mail	Wetland creation	generalized linear mixed model
Greiner (2015[196]) and Greiner et al. (2014[197])	AUS	April–July 2013	- Conservation requirement (short spelling; long spelling; total exclusion) - Compensation level (per ha per year: AUD1; 2; 4; 8; 16; 32) - Contract length (5 years; 10 years; 20 years; 40 years) - Flexibility (yes, no) - Monitoring (self-monitoring w 25% random spot checks; external monitoring)	104	24: 4 blocks of 6 choice sets	Face-to-face and mail	Conservation of (extensive) cattle grazing lands	RPLM and Latent class (LC) models
Hope et al. (2008[198])	India	?	- Land commitment to organic farming (acres): (25%; 50%; 75%; 100%) - Organic crop price increase (per 100 Rupees): (5; 7; 9; 11; 13; 15) - Cost of certification (Rupees, per acre): (1,000 as a group; 3,000 as a group; 3,000 as an individual) - Compost price (Rupees,per trolley): (600; 900; 1,200; 1,500) - Labour days to compost one trolley: (4; 8; 12; 16)	640	64: 8 blocks of 8 choice sets	face-to-face	Wetland conservation	CLM and LCM
Hoyos et al. (2012[199]), Etxano et al. (2015[200])	FRA / SPA (Basque Country)	? WTP is based on 2008 value	- Native forest (% of land converted by cork oak woodland): (2 (status quo); 10; 20; 30) - Biodiversity (Number of endangered species of flora and fauna): (25 (status quo); 15; 10; 5) - Recreation (Conservation status of walking pathways): (Low (status quo); Medium; High; Very high) - Exotic tree plantations (% of land area covered by pine forest): (40 (status quo); 30; 25; 15) - Vineyard (% of land covered by vineyards): (40 (status quo); 30; 20; 10) - Cost (EUR, per year): (0 (status quo); 5; 10; 30; 50; 100)	221	120: 20 blocks of 6 choice sets	face-to-face	Land use	CLM and RPLM
Hudson and Lusk (2004[201])	US	?	- Expected Income USD (135000; 150000; 165000) - Price Risk Shifted (None; Semi-Fixed; Fixed) - Autonomy (None; Some; Same) - Asset Specificity (10%; 30%; 50%) - Provision of Inputs (0%; 50%; 100%) - Length of Contract (1; 3; 5)	49	73: 4 blocks of 16 choice sets and 1 block of 9	Face-to-face	Undefined generic kind of contract	CLM and RPLM
Jaeck et al. (2014[202])	FRA	?	- Weed control technology: (Intensive chemical weeding (3 applications or more); Chemical weeding with 1 or 2 applications; Mechanical weeding; Manual weed removal) - Varietal choice: (Short cycle (140–150 days); Medium cycle (150–160 days); Long cycle (>160 days)) - Crop rotation: (Long rotation (1 year of rice every 5 years); ‘Cereal’ rotation (2 years of rice every 5 years); ‘Intensive cereal’ rotation (2 or 3 consecutive years of rice)) - Yield (tons, per ha): (yield< 2; 2<yield< 5; 5<yield<7; Yield<7) - Risk (Year): (0; 1; 3) - Single payment scheme (EUR, per ha): (0; 400; 700; 1000)	104	22: 2 blocks of 11 choice sets	face-to-face	CAP for rice cropping technologies	RPLM and LCM
Kaczan et al. (2013[203])	Tanzania	September and November, 2010	- Individual payment for maintenance of agroforest (Approximate USD, per acre per year): (0; 21; 50; 176) - Collective payment provided to a dedicated village development fund (Approximate USD, per acre per year): (0; 21; 50; 176) - Upfront fertilizer payment (Approximate USD, per acre, one-time): (0; 140 (binary variable)) - Conditionality low (No inspections—farmers are required to keep a log book documenting farm activities which may be audited): (Y/N) - Conditionality moderate (Inspecting farmers' farms once per year): (Y/N) - Conditionality high (Inspecting farmers' farms twice per year. Also will ensuring trees are indigenous species.): (Y/N)	220	32: 4 blocks of 8 choice sets	face-to-face	PES for avoiding deforestation	CLM and LCM
Kreye et al. (2017[204])	US	2014	- Incentive type (USD, per acre): (Annual payments; Reduction in the estate tax; Annual depredation payment; Safe harbor agreement) - Technical assistance: (Advice about stewardship practices; Advice about securing water resources; Advice about improving game populations; Help identifying other incentive programmes) - Acres enrolled: (25% of eligible acres; 50% of eligible acres; 75% of eligible acres; 100% of eligible acres) - Contract duration (year): (5 years; 10 years; 20 yearsl; 30 years) - Monitoring agency: (US Fish and Wildlife Service; Florida Fish and Wildlife Conservation Commission; US Department of Agriculture; Independent environmental consultant)	187	16: 2 blocks of 8 choice sets	Mail	Panther conservation	RPLM
Kreye et al. (2018[205])	US	December 2015 and February 2016	Management choices - BMP1: Implement 100% of applicable silvicultural BMPs. - BMP2: Implement at least 85% of applicable silvicultural BMPs. - WLD1: Locate concentrated heavy equipment away from active burrows or nests. - WLD2 Heavy equipment use must be minimized around nests during hatching season. - ADD1: Manage stands to have two age classes. - ADD2: Prescribed fire is applied every 3–5 years in stands over 10 years in age. Landowner empowering policy tools - TA1: Technical assistance is provided to help meet programme requirements. - TA2: NO technical assistance. - FA1: 50% cost-share is provided to help meet programme requirements. - FA2: NO cost-share assistance. - EXP1: Participating landowner is exempted from permitting for the incidental take of State Imperilled Species on their forestland. - EXP2: Participating landowner is NOT exempted from permitting for the incidental take of State Imperilled Species on their forestland.	200	12	Mail and web-survey	Wildlife Best Management Practices	RPLM
Kuhfuss et al. (2016[206]) and Kuhfuss, Préget and Thoyer (2014)	FRA	Summer 2012	- reduction of herbicide (-30%; -60%; -100%) - localised use of herbicide (yes, no) - final collective bonus of EUR 150/ha (yes, no) - free administrative and technical assistance (yes, no) - compensation level/ha (EUR 90; 170; 250; 330; 410; 500) Two-step experiment: - choice of contract - choice of acreage	290	18: 3 blocks of 6 choice sets	Web-survey	innovative herbicide-reduction contracts	Kuhfuss et al (2016): CLM and RPLM, FE and 2-stage Heckman model); Kuhfuss, Préget and Thoyer (2014): LCM
Latacz-Lohmann and Breustedt (2019[157])	GER	?	- Fertilisation (organic and mineral allowed; organic permitted; no fertilisation allowed) - First mowing not before (1 June; 22 June) - Maximum grazing with (2; 3; 4) animals per ha (1 animal = 1 cattle or 3 sheep) - Contract period (1; 5; 10 years) - Annual compensation EUR per ha (250; 350; 450)	68	63: 8 blocks of 8 choice sets ( - 1 for a dominant choice)	Face-to-face	Protection for breeding birds on permanent pasture	CLM (For enrolled land area, OLS and multinomial Heckman model)
Layton and Siikamäki (2009[207])	FIN	?	Two-steps: - choice of contract - choice of acreage - Payment (ERU): (ranged between about 85 and 11,770) - Contract length: (ranged between 10 and 50 years, in 5-year increments)	1129	3 choice sets	Mail	habitat preservation on private lands	Beta-binomial model and multivariate censored regression
Leinhoop and Brouwer (2015[208])	GER	May and September 2012	- Forest size (%) (5; 10; 25; 50) - Forest type (commercial; non-commercial) - Technical advice (yes, no) - Recreational access (yes, no) - Return to agriculture at end of contract (yes, no) - Contract length (10 years, 25 years, 50 years) - Compensation level (EUR per ha per year: 500; 750; 1000; 1500; 2000; 3000)	208	120: 20 blocks of 6 choice sets	Face-to-face	Afforestation	RPLM
Lizin, van Passel and Schreurs (2015[209])	BEL	December 2012–February 2013	- Lot size (ha): (0.5; 1.5; 2.5; 3.5) - Soil productivity: (Low; Rather low; Rather high; High) - Driving time to home (min): (5; 10; 15; 20) - Distance to other land (km): (0; 0.750; 1.500; 2.250) - Land use restrictions: (None; Crop restriction; Fertilizer restriction; Usage restriction) - Price (EUR per ha): (15,000; 25,000; 35,000; 45,000)	188	16: 2 blocks of 8 choice sets	face-to-face	Land use restrictions	RPLM and error component logit
Ma et al. (2012[210])	US	2008	Two-steps: - choice of contract - choice of acreage - annual payment for a period of 5 years (USD per acre, A specific range for each of 4 cropping systems): (A: 4-17; B: 10-36; C: 15-55; D: 20-75) - payment provider: (federal government; non-governmental organisation) - sequence of cropping practices (increasing or decreasing in complexity and expected environmental benefits)	1,688	16	Mail	PES in agriculture	Double hurdle model (comprised of a Probit for willingness to consider and a Tobit for acreage enrolment)
Pan et al. (2016[211])	CHN	between July and August 2014	- Technical support: (No technical support (baseline); Medium technical support; High technical support) - Pollution fees (Yuan, per head per month): (0 (baseline); 2.8; 5; 10) - Technical standards: (Y/N (baseline)) - Biogas subsidies (Yuan, per household): (0 (baseline); 1000; 1500; 2000) - Manure market (Yuan, ton): (0 (baseline); 100; 150) - Manure handling rate: (Increase 0% (baseline); Decrease 5%; Increase 15%)	754	12: 3 blocks of 4 choice sets	face-to-face	livestock pollution control policy	RPLM
Peterson et al (2007[212]); (2012[213]); (2014[214])	US	August 2006-January 2007	- Application time (hours): (4; 16; 24; 40) - Monitoring: (Annual verification; spot check) - Penalty USD/acre enrolled: (50; 100; 250; 500) - Trading revenue USD/acre enrolled: (3; 7; 15; 25) - Type of practice required: (Filter strip; No-till) - Haying/grazing allowed on filter strip (Yes/No) - Rotational no-till allowed (Yes/No)	135	32: 2 blocks of 16	Face-to-face	Water quality trading	RPLM
Pröbstl-Haider et al. (2016[215])	AUT	January and September 2012	- Type of management: (Cash crop cultivation; Short-rotation cultivation; Grassland cultivation) - Gross margin (EUR, per ha per year): (Cash crop cultivation: 300; 450; 750; 1200; 1650, Short-rotation cultivation: 150; 375; 550; 725, Grassland cultivation: 75; 150; 250) - Environmental premium (EUR, per ha per year): (Cash crop cultivation: None; Greening premium 50; Greening premium 150, Short-rotation cultivation: None; Climate premium 50; Climate premium 100; Climate premium 150, Grassland cultivation: Austrian AES-funding 300; Austrian AES-funding 600; Austrian AES-funding 900; Austrian AES-funding 1200) - Duration (year): (Cash crop cultivation [rotation period]: 1, Short-rotation cultivation [rotation period]: 15; 20; 25, Grassland cultivation [contract period]: 7) - Potential price fluctuations: (Cash crop cultivation: Low; Medium; High; Very high, Short-rotation cultivation: Low; Medium; High, Grassland cultivation: Low) - Likelihood of complete crop failure: (Cash crop cultivation: Every 2 years; Every 3 years, Short-rotation cultivation: Every 10 years; Every 25 years, Grassland cultivation: Every 5 years; Every 10 years; Every 15 years)	148	48	face-to-face	Land use under climate change (cash crop cultivation, short-rotation forestry, grassland cultivation)	CLM
Rabotyagov and Lin (2013[216])	US	February 2009.	- Payment (USD, per acre per year): (25; 50; 100; 200) - Contract length (years): (10; 30; 50; In perpetuity) - Extent of participation (share of forest stand): (More than 0, but less than 1/3; More than 1/3, but less than 2/3; More than 2/3, but less than entire stand; Entire stand) - “Biodiversity pathway” management: (Y/N)	678	32: 4 blocks of 8 choice sets	Mail	working forest conservation contracts (WFCC)	RPLM - Error Component Specification
Rocchi et al. (2017[217])	ITA	?	- Nature: (No surface; 1/3 surface; 1/2 surface) - Biodiversity: (Do not make it; Creation of hedges) - Landscape: (Do not make it; Creation of fences) - Seeds: (No surface; 1/2 surface; All the surface) - Lisciviation: (No surface; 1/2 surface; All the surface) - Money (EUR/ha per year): (50; 100; 150; 200)	244	16: 4 blocks of 4 choice sets	face-to-face	AES in buffer areas	LCM
Rossi et al. (2011[218])	US	?	- Replant with SPB resistant Pine: (Required; Not required) - Pre-commercial Thinning: (Required; Not required) - Commercial Thinning: (Required; Not required) - Prescribed Burning: Required (1 time; 2 times;3 times; Not required) - Local Landowner Participation Rate: (5%; 50%) - Incentive Payment (USD per acre): (30; 80; 120; 160; 200; 250)	173	48: 4 blocks of 6 choice sets (24 profiles as the basic foundation, shifting to an additional 24 profiles)	Mail	forest management practices (Southern Pine Beetle Risk Reduction Cost-Share Program)	Heteroskedastic Extreme Value (HEV) models
Ruto and Garrod (2009[81]), Arnaud et al. (2007[219])	Various a	May-December 2005	- Contract length (5, 10, and 20 years) - flexibility in terms of what areas of the farm are entered into the scheme (yes, no) - flexibility in undertaking some of the measures required under the scheme (yes, no) - average time spent on paperwork/administration (less than 2 h/week, 2–5 h/week, or more than 5 h/week). - compensation level (per ha: 5%; 10%; 20%)	2262	24: 6 blocks of 4 choice sets	Face-to-face	EU CAP AES (various)	RPLM and Latent class (LC) models
Saïd and Thoyer (2007[220])	FRA	August 2006.	- Level of financial needs - Level of compliance costs with AES - Level of environmental benefits - Level of compensation payment	32	9: 3 blocks of 3 choice sets	Face-to-face	Contracting for the grass premium	BLM
Santos et al. (2015[221])	POR	October and December 2013	- Area size (% of eligible farm land area): (25; 50; 75) - Cattle density (Livestock units per ha of forage area): (0.2; 0.5; 0.7) - Tree density (Number of trees per ha): (20; 30; 40) - Contract duration (Years): (5; 10; 20) - Compensation (EUR/ha/year): (100; 250; 450)	111	64: 8 blocks of 8 choice sets	face-to-face	AES for agro-forestry ecosystem	RPLM
Schulz et al. (2014[222])	GER	Summer of 2012	- Ecological Focus Area (EFA): (5%; 7%; 10% of a farm’s arable land) - Arable crop diversity: At least 3 crops (in excess of EFA), each covering no less than (5%; 15%; 25%) of arable land - Land creditable against EFA: (None; Land enrolled in AES; Landscape features (hedges, ponds, stone walls, etc.); Land in AES and landscape features) - Permissible use of EFA: (Leguminous crops; Leguminous crops, but they must be grown on twice the EFA; No productive use (EFA must be set aside)) - Choice of EFA location: (Location of EFA can be freely chosen each year; EFA location fixed for 3 years) - Reduction of single payment in case of opt-out (EUR per ha per year): (35; 70; 105; 140; 175)	128	25: 8 choice sets	Web-survey	“greening” of the CAP	BLM and LCM
Sorice et al. (2013[223])	US	?	- Conservation easement type: (no easement; permanent donated easement; permanent sold easement) - Contract length (years): (10; 40; 70; 100) - Credit profit margin (USD per credit): (100; 200; 400; 600; 1600) - Payment structure: (25% year 1, 75% year 5; 50% year 1, 50% year 5; 75% year 1, 25% year 5; 100% year 1) - Decision-making: (programme staff make all land management decisions; landowner and staff share decision-making; landowner makes all decisions) - Obligation once conservation agreement ends (if gopher listed as endangered species): (none; baseline; full) - Result (effectiveness, increase in no. gopher tortoises in country as a result of landowners opting in): (0%; 5%; 10%; 15%)	251	48: 8 blocks of 6 choice sets	Mail	Maintaining and managing habitat for an at‐risk species, the gopher tortoise (Gopherus polyphemus)	RPLM
Star et al. (2019[224])	AUS	February to April 2018	- Days of paid work: (0; 5; 10; 25) - Payment (AUD, per day): (0;100; 200; 500;1000) - Extra unpaid days will be required (50:50 risk): (0; 5; 10; 25) - Risk that the project will not fix the problem: (0; 10; 25; 50)	75	32: 8 blocks of 4 choice sets	face-to-face	projects to reduce gully erosion and subsequent sediment run-off	RPLM
Tesfaye and Brouwer (2012[225])	Ethiopia	July 2009.	- Principal (contract provider): (Regional Agricultural Bureau; Local Peasant Association) - Contract length (year): (1; 2; 3; 5; 10) - Payment (Birrr (USD 1=12.56 Birr in 2009), per month): (50; 100; 150; 200; 250; 300) - Land use certificate guarantee: (Y/N) - Soil conservation measure: (Stone bund; Soil bund; Fanya juu) - Number of times for additional extension service including monitoring (year): (1; 2; 4; 6)	750	162: 18 blocks of 9 choice sets	face-to-face	soil conservation	RPLM - Error Component Specification
Vaissière et al. (2018[226])	FRA	May-June 2016	- Management plan: (4 levels of increasingly restrictive (more environmentally friendly) management; opt-out) - Contract length: (9; 18; 25; 40 years; opt-out) - Conditional bonus EUR200/ha/year for additional ecological conditions in management plan (yes; no; opt-out) - Compensation level (base)/ha/year: (EUR800, EUR1100, EUR1500, EUR2000; opt-out)	144	16: 0 block of 8 choice sets	Web-survey	biodiversity offsets	RPLM
Vedel et al. (2015[227])	DEN	January and February 2009	- Purpose of afforestation: (Biodiversity; Ground water protection; Recreation) - Option of cancelling the contract: (Option of cancelling within 10 years; within 5 years; Binding contract) - Monitoring: (1%; 10%; 25%) will be monitored - Compensation (USD, one-time per ha): (3620–5525 in steps of EUR 400)	853	36: 6 blocks of 6 choice sets	Web-survey	afforestation	CLM and Latent class (LC) models
Villamayor-Tomas et al. (2019[228])	GER, SWITZ, SPA	?	- Location of trees: (Coordinated; Not Coordinated) - Share of farm: (1%;5%;10%) - Recommendation (endorsement of the programme): (by farmers; by scientists; no particular recommendation) - Payment for action (ERU, per year per ha): (50;100;150;200)	195	12 choice sets	Mail, Web-survey, face-to-face	a tree planting measure	CLM
Villanueva et al. (2015[229])		October 2013 to January 2014	- Cover crops area: (25%; 50%) - Cover crops management: (Free; restrictive management) - Ecological focus areas (EFA): (0%; 2%) - Collective participation: (Individual; collective participation) - Monitoring each year: (5%; 20%) - Payment (USD, per year per ha for a 5-year AES contract): (100; 200; 300; 400)	295	192: 24 blocks of 8 choice sets	face-to-face	AES permanent cropping	LCM
Villanueva et al. (2017[230])) (in Spanish)	La Sierra and Los Pedroches (Córdoba), and Sierra Norte (Sevilla), Spain	October-December 2016	- Plant cover surface: (10% (reference level); 30%; 50%; 100%) - Plant cover management: (Free (reference level); Limited; Brushcutter and/or Shredder blade disc; No practice) - Insecticide treatment: ( Free (reference level); Limited; Ecological; No treatment) - Premium for results (EUR/ha): (Non-inclusion of the premium (reference level); Inclusion of a premium for results of EUR 400/ha to be received in the 5th year of the programme) - Annual payment (EUR/ha/year, during the 5 years of AEP): (50; 150; 250; 350)	254	24: 4 blocks of 6 choice sets	face-to-face	AES for mountain olive groves	RPLM - Error Component Specification
Vorlaufer et al. (2017[231])	Zambia	May and September 2015	- Payment vehicle: (Annual cash payment; Monthly cash payments; Voucher payments; Input payments) - Payment levels (ZMW, per year per acre): (60 (8.2USD); 120 (16.4USD); 240 (32.9USD); 480 (65.8USD)) - Contract duration (year); (10; 20) - Implementing organization: (Government of Zambia; NGO) - Forest co-benefits: (No extraction; Firewood extraction; Subsistence extraction; Commercial extraction)	320	16: 4 blocks of 4 choice sets	face-to-face	PES for deforestation	CLM and LCM
Wachenheim et al. (2019[232])	US	2017	- Rental payment (% of local land rental rates reported by NASS): (70; 85; 100; 110%) - Mid-contract adjustment: (payment fixed at the start of the contract; readjusted up or down at mid-contract to reflect changes in local rental rates) - Length of contract (year): (5; 10; 15) - Managed burning: (allowed; not allowed) - Conservation practice: (required; not required)	672	30: 2 blocks of 15 choice sets	Mail	working wetlands conservation programme	Mixed rank ordered logit
Ward et al. (2016[233])	Malawi	Jun-14	- Intercropping required: (Y/N) - Zero tillage required: (Y/N) - Percentage of crop residues mulched: (0; 25; 50; 75; 100) - Programme implementer: (DLRC; NASFAM; TLC; World Vision) - Subsidy level (USD, per acre per year): (0;10; 20;30; 40)	1791	20: 2 blocks of 10 choice sets	face-to-face	Conservation Agriculture for soil quality and crop diversification	RPLM
Yeboah et al. (2015[234])	US	Summer 2016	- Contract length: (10-20 years) - Signing bonus (USD): (0-200) - % of cost share assistance for practice installation: (0-140%) - annual soil rental payment (USD/acre enrolled): (50-275)	1106	108: 36 blocks of 3 choice sets	Mail and web-survey	Filter strip programme for watershed protection	CLM
Yu and Belcher (2011[235])	CAN	Jul-07	- Compensation level: (CAN per acre: 10 - 55 in USD 5 increments)	212	Randomly assigned compensation level varying between CAN10-CAN55/acre	Mail	Conserving riparian areas	Binary PM and CLM

Notes: a BLM = binomial logit model, CLM = conditional logit model, FEM = fixed effects model, LCM = latent class model, OLS = ordinary least squares, PM = Probit model, RPLM = random parameter logit model. GTGP = Grain-to-Green Program.

Notes

← 1.  In the case of results-based schemes there are operational challenges in terms of having the necessary and appropriate agroecological expertise to deliver at farmer, advisor and controls level of paying agencies, since results-based schemes have a higher knowledge need and it will take time to scale up appropriate expertise.

← 2.  Note that environmental benefit index (EBI) is used in this document as a general term and it does not refer to the Environmental Benefit Index (EBI) employed to rank and select land parcels to the Conservation Reserve Program (CRP) established by the Food Security Act of 1985 and administered by the US Department of Agriculture (USDA).

← 3.  It is important to note that the risk premium will have to be borne by the society in any case. In the case of result-based payments, it is an explicit component of the payment rate paid for by the tax-payer. For practice-based payments, the risk (of not obtaining the desired environmental outcomes) is borne by the society and hence implicitly paid for by the citizen.

← 4.  Environmental quality refers here mainly to the inherent environmental quality of the field parcel. It could also include the environmental benefits of a particular environmental pratice adoption of a chosen agri-environmental contract in given field parcel.