# Guilty or scapegoat? Land consolidation and the hedgerow decline

Valentin Cocco, Raja Chakir, Lauriane Mouysset

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Valentin Cocco, Raja Chakir, Lauriane Mouysset. Guilty or scapegoat? Land consolidation and the hedgerow decline. Journal of Environmental Economics and Management, 2025, 133, pp.103196. 10.1016/j.jeem.2025.103196. hal-04658788v2

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# Guilty or scapegoat? Land consolidation and hedgerow decline

Valentin Cocco<sup>a, b,*</sup> <sup>●</sup>, Raja Chakir<sup>a</sup>, Lauriane Mouysset<sup>c</sup>

## ARTICLE INFO

JEL classification:

Q15

Q24

Q58

R14

Keywords:

Land tenure

Land consolidation

Agricultural policy

Landscape feature

Hedgerow

Environmental assessment

Difference-in-differences

## ABSTRACT

Land consolidation is a standard policy tool to reduce land fragmentation through the spatial redistribution of property rights; however, the risk of adverse effects on the landscape raises concerns about its environmental sustainability. This study investigates the landscape impacts of consolidation on the hedgerow network of Lower Normandy, France. Implementing a staggered difference-in-differences strategy on a longitudinal survey (1972-2010), we show that consolidation led to a significant reduction in hedgerow density of -14.3 m/ha (standard error: 2.33), accounting for 13.7% of the overall decline observed in consolidated areas. We also find a diminishing impact over time of consolidation and time since consolidation, an increasing impact with higher initial hedgerow density, no spillover effect, and a negative impact on network connectivity. An outline cost-benefit analysis suggests that the social costs of uprooting hedgerows outweigh the private benefits. Overall, this paper confirms that land consolidation has significantly contributed to the decline of hedgerows with economic costs, but it challenges prevailing beliefs about the policy's share of responsibility with respect to other landscape change factors.

## 1. Introduction

Land fragmentation often hinders productive agriculture by increasing production costs and limiting investment in machinery and infrastructure (Ali et al., 2019; Latruffe and Piet, 2014; Deininger et al., 2017). Land consolidation (LC) has been a prevalent policy response to this issue by organizing land transfers among landowners to create more cohesive farm holdings. National governments have implemented LC reforms in diverse economic contexts, such as post-war Western Europe, post-socialist Central and Eastern Europe, and during the reform and opening-up period in China. Today, countries in Africa, Asia, and Europe, including Serbia, Turkey, Vietnam, and Rwanda, are implementing or considering LC programs (Veršinskas et al., 2020). Yet, the contribution of LC to sustainable rural development remains uncertain, as the impacts of land fragmentation extend beyond yields (Knippenberg et al., 2020; Bentley, 1987). The potential for LC to heavily alter landscape composition and structure is of particular concern, given that landscapes provide essential services to human societies (Janečková Molnárová et al., 2023; Duarte et al., 2018). However, there is a lack of econometric evaluations providing causal identification of the effects of LC on the landscape.

This paper investigates the impact of LC on agricultural landscapes, using the case of the *bocage* landscape of Lower Normandy, France. Bocage, characterized by a dense hedgerow network delimiting parcels, is an emblematic landscape type in Western Europe (e.g., England, Galicia, Ireland, and Western France), even though hedgerows are common landscape features worldwide (Baudry et al., 2000).<fnref n="1" /> These managed woody linear elements serve multiple functions, such as demarcating property limits, enclosing

<footnote><sup>a</sup> Université Paris-Saclay, INRAE, AgroParisTech, Paris Saclay Applied Economics, Palaiseau, France</footnote>

<footnote><sup>b</sup> CIRED, AgroParisTech, Cirad, CNRS, EHESS, Ecole des Ponts ParisTech, Université Paris-Saclay, Nogent-sur-Marne, France</footnote>

<footnote><sup>c</sup> CNRS, UMR 8568 CIRED, Nogent-sur-Marne, France</footnote>

<footnote>* Corresponding author at: CIRED, Campus du Jardin Tropical, 45 bis, avenue de la Belle Gabrielle, 94736 Nogent-sur-Marne Cedex, France.</footnote>

<footnote>Email address: valentin.cocco@agroparistech.fr (V. Cocco).</footnote>

<footnote n="1">¹ The terminology for these landscape elements can vary (e.g., shelterbelts or windbreaks in North America), reflecting regional differences in composition or functions.</footnote>
livestock, providing timber, storing carbon, conserving soils, and having aesthetic value (see Montgomery et al. (2020) for a detailed review). Additionally, they serve as semi-natural habitats that support biodiversity in agricultural landscapes (Estrada-Carmona et al., 2022). However, French bocage experienced a drastic decline in its hedgerows over the second half of the 20th century (débocagement), leading to more open landscapes with fragmented hedgerow networks (Bazin and Schmutz, 1994; Pointereau, 2001).

The role of the French LC program in this landscape transformation remains an open question. More than 18,000 LC operations coordinated land transfers at the municipality level, mainly in the northern half of France. These operations included subsidized development works that would remove kilometers of hedgerows to accommodate newly merged plots and tractor-accessible lanes (Flatrès, 1979). These rapid, state-promoted clearances have earned LC widespread blame for the decline in hedgerows (Luginbühl and Toublanc, 2003). Nevertheless, rural geographers have challenged this perspective and pointed out that farmers' individual actions may have played a more significant role (Preux, 2019). Some even suggested that LC could have benefited the bocage landscape compared to laissez-faire by coordinating hedgerow uprooting to avoid unnecessary removal and preserve network connectivity (Renard, 1973; Guellec, 1971). Despite these debates, there is a lack of conclusive evidence to determine LC's responsibility in hedgerow loss.

Our study combines two original datasets to assess the landscape consequences of LC. The first dataset, created by Philippe and Polombo (2009), lists the French LC operations that occurred at the municipality level from 1944 to 2006. In Lower Normandy, 42 % of municipalities underwent consolidation, with a peak in the 1980s-90s. The second dataset, produced by a French government body, tracks hedgerow dynamics in the region from 1972 to 2010. To be precise, hedgerows were mapped based on the manual interpretation of aerial photographs for 1175 300-m-radius circles in 1972, 1984, 1997-98, and 2009-10. This survey thus offers a representative sample of Lower Normandy's bocage with extensive temporal depth – notably including pre-consolidation observations – and detailed object-oriented mapping to measure hedgerow density and spatial structure.

Our identification strategy leverages the availability of panel data to study a natural experiment with a binary treatment by applying difference-in-differences. We use the estimator developed by Callaway and Sant'Anna (2021), which is robust to treatment effect heterogeneity when treatment is staggered and reports the heterogeneity of treatment effects over time *since* treatment and time *of* treatment. This is crucial for our study, as we expect distinct short-term and long-term effects, as well as differentiated impacts depending on the legislation and the local context during policy implementation. We also assess the plausibility of parallel trends by examining pre-trends, and test the robustness of our results in the case of trend deviations in accordance with Rambachan and Roth (2023).

In our preferred specification, LC induced a significant average loss in hedgerow density of –14.3 m/ha (standard error: 2.33) compared to non-consolidated municipalities. This reduction accounts for 13.7 % of the total hedgerow loss in the consolidated municipalities during the study period. While LC has substantially contributed to the decline of hedgerows, it cannot explain most of it in our study area, challenging prevailing beliefs among stakeholders. Our results also indicate that the effects of LC varied over time, both in terms of the timing of consolidation and the time since consolidation. Municipalities that underwent consolidation earlier experienced greater hedgerow loss than those consolidated later, which could reflect higher environmental expectations and stricter regulations for LC in France over time. Furthermore, the impact of consolidation decreased with time since consolidation, as non-consolidated municipalities eventually experienced some hedgerow loss as well. Our analyses reveal no significant pre-trends and show robustness to trend deviations and alternative specifications.

We extend our primary findings in three ways. First, we investigate potential spatial spillover that could bias our main estimates by applying the adaptation of difference-in-differences to local spillover developed by Butts (2023). We do not detect significant spillover. If anything, LC is likely to have had a positive, rather than negative, effect on the hedgerow density of neighboring municipalities. Second, we apply Callaway and Li (2019)'s distributional difference-in-differences estimator to uncover distributional effects. The analysis highlights the fact that the average negative impact mainly relies on large negative impacts in the upper end of the hedgerow density distribution. In contrast, consolidation at the lower end of the distribution has no effect or may even have a preserving effect on hedgerow density. Therefore, LC caused landscape homogenization. Finally, we shift our focus from hedgerow density to network connectivity indicators to understand how LC affects the structure of the hedgerow network. Our findings align with those for hedgerow density, reinforcing our conclusions about the impact of LC.

Beyond these econometric analyses, we provide a partial assessment of the economic costs and benefits associated with additional hedgerow uprooting. Our back-of-the-envelope calculations, which consider agricultural production, hedgerow harvesting, and carbon storage, reveal a clear divergence between private and public interests. While farmers benefited from increased agricultural productivity, these private benefits are offset by public costs, including subsidies for removal and carbon emissions, ultimately resulting in a net loss to social welfare.

In addition to providing the first causal evidence on the LC-hedgerow debate, this work makes significant contributions to three areas of economic research. First, it adds to the growing empirical literature evaluating the impacts of LC reforms (Wu et al., 2005; Chabe-Ferret and Enrich, 2021; Loumeau, 2022; Nilsson, 2019; Orea et al., 2024; Zeng et al., 2018). These studies vary in their geographical focus (China, France, Rwanda, Spain), their identification strategies (structural model or natural experiment), and their outcomes of interest (e.g., crop yields, farm structure, population growth, far-right vote). Our research is most closely related to Chabe-Ferret and Enrich (2021) who use event studies to analyze the French LC program but focus on plot and farm size, tractor adoption, and productivity. We extend this corpus of literature by proposing the first evaluation of the environmental impacts of LC – in our case, its landscape impacts – using advanced causal inference methods.

Second, our work relates to the literature on agricultural intensification and land use changes. Abundant literature discusses the consequences of increased agricultural productivity on land conversion, particularly focusing on deforestation (Villoria et al., 2014; Busch and Ferretti-Gallon, 2023). Recent empirical works contribute to this question by showing that policies supporting agricultural
intensification, such as investments in infrastructure and subsidies for new technologies, reduce pressure on forests (Baehr et al., 2021; Abman and Carney, 2020). However, agricultural intensification can lead to other land use changes, including the loss of landscape elements (van Vliet et al., 2015). The challenge in studying these fine-scale transformations lies in the need for granular, highly-interpreted data. Our research uses a high-resolution, object-oriented dataset to show how LC, which aims to intensify agriculture through more efficient use of land, leads to scattered but significant land modifications.

Finally, our work provides insights into the relationship between land tenure and resource management. A large body of economic literature has examined how (in)secure property rights influence investments in land productivity. Key factors include legal certificates (Hou et al., 2023; Liscow, 2013), physical protection (Hornbeck, 2010; Hou et al., 2023), threats to property rights (Jacoby et al., 2002; Goldstein and Udry, 2008), and whether the landholder is an owner or a tenant (Soule et al., 2000; Eder et al., 2021). Beyond security, other aspects of land tenure can affect land management. For example, extending land transfer rights can help mitigate land erosion (Li and Zhu, 2023). Here, we pay attention to the spatial component of land tenure, an overlooked feature of tenure systems. The French consolidation program made spatial agglomeration of land property a national objective with prescriptive legislation (Sargent, 1952; Gatty, 1956).<fnref n="2" /> We exploit the LC program as a shock to land tenure to reveal land-related consequences of incorporating spatial principles into property rights.

The remainder of the article is organized as follows: Section 2 presents our study area and gives details of the French LC program before introducing our research hypotheses. Section 3 details the data, the sample, and the variables of interest, followed by Section 4, which justifies our empirical strategy. Section 5 develops our main results on the impact of LC on hedgerow density with robustness checks, probes possible spatial spillover and distributional effects, and extends the analysis to landscape indicators beyond hedgerow density. Section 6 presents an outline cost-benefit analysis based on back-of-the-envelope calculations. Section 7 concludes the article.

## 2. Background and hypotheses

The French LC program was deployed in the second half of the 20th century to promote the modernization of agriculture (Section 2.1). We focus our study on Lower Normandy, France's densest bocage region (Section 2.2). While many accused LC of being the source of hedgerow uprooting, French rural geographers have questioned its real responsibility (Section 2.3). Accordingly, we consider competing hypotheses on the impact of LC on the hedgerow network (Section 2.4).

### 2.1. The French land consolidation program

Following the end of World War II, France sought economic recovery. This involved transforming its agricultural sector, which struggled to meet its population's demand for food and had a negative trade balance. There was strong political will to move from traditional small-scale agriculture to mechanized, intensive, and market-integrated agriculture (Lambert, 1963). However, farmland fragmentation was particularly severe, causing land loss and labor inefficiencies and preventing adoption of machinery and investment in infrastructure. Previous attempts to reduce fragmentation in the first half of the 20th century had limited effectiveness and scope. The 1941 law anticipated the modernization of French agriculture to come after the war and laid the foundations for a modern, large-scale consolidation, followed by a decree in 1954 specifying the modalities to achieve a "rational use of land" (Gatty, 1956; Sargent, 1952).<fnref n="3" /> The established protocol steered a path between local dialogue and coercion (Gastaldi and Vallery-Radot, 1976; Peignot et al., 1999):

Landowners, farmers, or State civil servants could suggest initiating an LC operation in a municipality ("commune"). An ad hoc council of municipality stakeholders would be established to decide on the suitability of such an operation. If the council was favorable, the local State representative ordered an LC operation covering all the municipality's farmland, even if some landowners or farmers objected. Technical and consultation phases alternated until a consolidation project emerged. The proposal had to allocate land so that each owner received a land surface equal to their provision, accounting for the differences in the productive potential of each land type.

In addition to land transfers, the council planned development works ("travaux connexes") to support agricultural development. These works included the creation of new lanes for heavy machinery, the drainage of wetlands, and the elimination of natural obstacles, notably hedgerows. Once the council validated the consolidation project, a decree stated that land transfers were effective and development works could start. Until 1983, the State was the project owner of consolidation operations and paid the full cost of the operation per se. It also subsidized the development works up to 60 % of the total cost (landowners covering the rest). The 1983 law on decentralization made the "département", an administrative-territorial unit,<fnref n="4" /> (henceforth "department" in this paper) take responsibility for project administration and funding. The State continued to check legality and to advise local stakeholders.

The deployment of LC in France was gradual (Philippe and Polombo, 2009). The earliest operations, conducted from the late 1940s to the 1960s, focused on open-field areas with high potential for specialized field-crop production, such as the Beaude and Picardy. Over time, LC extended to the rest of northern France, where agronomic conditions favored large, intensive farms specializing in either field crops or cattle breeding. The final operations occurred in the 2000s, culminating in approximately 18,000 "remembrements

<footnote n="2">² The law on consolidation in France gave LC operations "public interest" status so that administrative authorities could execute them without the agreement of all, or even a majority of, landowners.</footnote>

<footnote n="3">³ Art. 13 of the law of 5 August 1960 on agricultural policy: "The purpose of agricultural and rural land use planning, [...], is to ensure that the structure of agricultural and forestry properties and holdings is consistent with a rational use of land and buildings."</footnote>

<footnote n="4">⁴ Lower Normandy consists of three departments.</footnote>
<figure><img src="image_4.png" /><figcaption>**Fig. 1.** Consolidation operations in lower normandy municipalities.
NOTE. Data source: Philippe and Polombo (2009).</figcaption></figure>

ruraux”. Lower Normandy reflects this sequence of events. Open-field landscapes dominated by field crops in the central plain corridor (Campagnes de Caen, Falaise et Argentan) and mixed landscapes in the southern parts of the corridor (Campagne d’Alençon) and along the southeastern border (Perche, Pays d’Ouche) were consolidated as early as the 1950s and 1960s. The remainder of the region, characterized mainly by grasslands (Fig. A.2), engaged in LC later and incompletely. Overall, LC events in Lower Normandy peaked in the late 1980s, with 42 % of the region’s municipalities eventually undergoing consolidation (Figs. 1 and A.1; Brunet (2001)).

## 2.2. *The Norman bocage*

Bocage predominantly characterizes the landscapes of Northwestern France (Flatrès and Flatrès, 1997). Hedgerow plantation (“embocagement”) was a progressive phenomenon, accelerating through the 19th century, with hedgerow density presumably reaching its highest level in the first half of the 20th century (Brunet, 2001; Flatrès, 1979; Bazin and Schmutz, 1994). Hedgerows were used for three main reasons (Bazin and Schmutz, 1994; Baudry et al., 2000). First, they provide a material demarcation of private property in the context of agrarian individualism and equality of succession. Second, they support the development of cattle farming by containing livestock in meadows (or protecting field crops from straying livestock). Third, they are “short-rotation linear forests” that supply wood in poorly forested regions. These landscape elements were fully integrated into traditional farming: they protected, produced, and were intensively managed based on farmers’ knowledge and techniques (Magnin, 2021).

While their historical functions have become less significant, hedgerows are now valued for the public goods they provide in the form of ecosystem services, such as climate change mitigation, the regulation of hydro-sedimentary flows, and landscape amenities (Appendix I.3). Recent national and supranational policies reflect the renewed public interest in hedgerows. Following the COVID-19 pandemic, the French Ministry of Agriculture allocated €45 M to plant 7000 km of hedgerows in 2021–2022 as part of the economic recovery plan “France Relance”. The “France Nation Verte” plan for ecological transition expanded these efforts, allocating €110 M in 2024 to a national “hedgerow deal” which aims for a net increase of 50,000 km of hedgerows by 2030. At the European level, since 2015, farmers have been able to declare their hedgerows (among other options) as environmentally valuable elements to comply with the minimum agro-environmental requirements for access to CAP subsidies.
Lower Normandy is home to the Norman bocage. This administrative French region even shows the highest present density of hedgerows: 80 linear meters of hedgerow per hectare, compared to the national average density of 28 m/ha (ANBDD, 2021).<fnref n="5" /> There is a traditional association between bocage and cattle farming and Lower Normandy has the second highest share of permanent grassland in France (32 % of land cover in 2014), even though this has reduced to the benefit of temporary grassland and forage crops (Brunet, 2001; Preux, 2019). More broadly, it also has the highest share of farmland (73 %) and the second smallest share of woodland (15 %) (Fontes-Rousseau and Jean, 2015). This land distribution supports an agricultural sector mainly oriented towards dairy and mixed farming. Nevertheless, the regional aggregates hide infra-regional heterogeneity. For instance, the Lower Normandy plains have historically produced cereals, and their landscape is much closer to the open fields of the Parisian basin and Northeastern France (Fig. A.2; Brunet (2001)).

## 2.3. The debate about the role of LC in hedgerow decline

In the latter half of the 20th century, the French bocage suffered drastic hedgerow loss of the order of several tens of percent at least. In Lower Normandy, hedgerow density decreased by 59 m/ha (46 %) between 1972 and 2010, with considerable spatial heterogeneity (Fig. A.3).<fnref n="6" /> LC is often held responsible for this. “Blank slate” operations could uproot tens of kilometers of hedgerows with bulldozers within a few weeks and abruptly transform the landscape (Flatrès, 1979). The accusations against LC were sometimes mixed with social conflicts due to unfair land redistribution and forced modernization. Overall, LC concentrated criticism of excessive agricultural intensification, symbolized by a “monument to nature and the victims of land consolidation” erected in Geffosses, a village in Lower Normandy (Preux, 2019).

Nonetheless, French rural geographers have qualified the actual contribution of LC to “débocagement”. Flatrès (1979) argues that State administrative bodies were aware of the environmental risks and took good care to prevent unnecessary removals. Although some uprooting was inevitable for agricultural modernization, the concerted approach led to a “controlled and rational clearance” (Renard, 1973). Not only would LC limit the number of uprooted hedgerows, but it would also determine conservation priorities to maintain a well-connected and functional network (Flatrès and Flatrès, 1997). In that sense, it may have even been beneficial to the bocage, as, in its absence, farmers would have removed more hedgerows in a disorderly way (Guellec, 1971). To quote Renard in Missonnier (1976):

> "It is wrong to believe and repeat that official land consolidation is the main cause of débocagement. First, because some clearance of hedges retained by the land surveyor is observed after consolidation. Second, in all regions, individual clearance is considerable, although difficult to assess. [...] From this point of view, there is no doubt that well-conducted consolidation would preserve the bocage better than anarchic individual clearances."

One possible counter-argument is that the consolidation operation induced the clearance activity observed afterwards. But this safety-net conjecture may be all the more accurate over time, as environmental expectations grew, eventually becoming embodied in legislation. For instance, laws introduced in 1975 and 1976 made it mandatory for an expert environmentalist to attend the consolidation council and for an *ex ante* environmental impact assessment to be made, including proposals for compensatory measures (Baudry and Burel, 1984; Husson and Marochini, 1997). From that perspective, LC could be more of a scapegoat, hiding the actual causes of hedgerow loss (Luginbühl and Toublanc, 2003).

However, quantitative elements to corroborate these assumptions are lacking. To our knowledge, the only exception is Preux (2019), who observes no substantial difference between consolidated and non-consolidated municipalities, but only uses recent post-consolidation data for a limited geographical area.

## 2.4. Hypotheses

Based on the above discussion, we formalize competing hypotheses on the impact of LC on the Norman bocage.

* **The ripper.** LC operations caused hedgerow uprooting that would not have happened otherwise. LC triggered land transfers by decreasing private transaction costs through dedicated institutions (local committees) and public funding for technical support (land surveyors). Alternatively, it may have forced some land transfers in the public interest because if a farmer's appeal against the committee's consolidation plan failed, they could not refuse the land transfers. Once the land transfers were proclaimed, public authorities subsidized uprooting of hedgerows between newly merged parcels or when they obstructed the development of tractor-accessible lanes. Beyond subsidized uprooting, LC may have induced private uprooting initiatives as a consequence of the new agricultural production models adopted by regrouped farm holdings. If LC favored the intensification observed in the Norman bocage, i.e., the transition of cattle farming from grazing permanent grasslands to cultivating fodder crops and

<footnote n="5">⁵ Lower Normandy is actually a former administrative region. In 2016, French administrative regions merged to reduce their total number from 22 to 13. Lower and Upper Normandy merged to form the new Normandy region. Although the name “Norman bocage” does not explicitly refer to Lower Normandy, Lower Normandy is much richer in hedgerows than Upper Normandy (24 m/ha).</footnote>

<footnote n="6">⁶ At the national scale, the forestry inventory measured a 43 % decrease for the period 1975–1987 (Pointereau, 2001). Quantifying the loss since the 1950s requires data that do not exist at a large scale. For a regional natural park in the south of Lower Normandy, hedgerow density fell by 50 % between 1945 and 2000 (PNR Maine-Normandie).</footnote>

<footnote n="7">⁷ “Il est faux de croire et de répéter que le remembrement officiel soit le grand responsable du débocagement. D’une part, parce que des araisements sont constatés après le remembrement, sur des haies conservées par le géomètre. D’autre part, dans toutes les régions, l’arasement individuel est considérable bien qu’il soit difficile à apprécier. [...] De ce point de vue, il ne fait pas de doute qu’un remembrement bien fait préserverait sans doute plus le bocage que des araisements individuels anarchiques.”</footnote>
temporary grasslands (Fig. A.2), hedgerows would represent a higher cost for mechanized and intensified farms, motivating further uprooting.

* **The neutral.** The same length of hedgerow would disappear with or without LC. Farmers could be interested in hedgerow uprooting without LC as hedgerows' original functions became obsolete: the land register protects property rights, electricity and gas are cheaper alternatives to firewood, and electric fences are a flexible solution for enclosing cattle. As the labor force in agriculture declined over the years, pruning became a costly, fruitless chore. Moreover, it may be argued that land transfers and intensification may have happened anyway (Chabe-Ferret and Enrich, 2021). In that case, LC subventions only engendered dead-weight effects.

* **The conservationist.** However, official LC operations differed from *laissez-faire* by coordinating land transfers and hedgerow removal at the municipality level. Moreover, the expert environmentalist in the committee would raise awareness of the underestimated benefits of conserving a fair length of hedgerow, and the (non prescriptive) compensatory measures in the environmental impact assessment could include hedgerow planting. Hence, the concerted rationalization of hedgerow uprooting during LC had a positive impact on hedgerow density compared to *laissez-faire* in the sense that it led to a spatial organization of parcels more beneficial to hedgerows, prevented unnecessary removal even from a private perspective, or went along with compensatory planting.

We propose analogous hypotheses regarding network connectivity upon which LC may have positive, negative, or no impact. However, the two effects may not be parallel. For instance, LC could have a global negative effect on density but no impact on connectivity because LC operations were careful to avoid network fragmentation.

## 3. Data

We use a longitudinal survey produced by the French government that describes changes in the Lower Normandy hedgerow network between 1972 and 2010, using a sample of 1175 300-m-radius circles. We combine these panel data with a census of land consolidation operations at the municipality level. Covariates from additional data sources allow us to control for potential confounding factors related to environmental, administrative, and agricultural conditions. Section 3.1 presents the data sources. Section 3.2 describes the construction of our cleaned dataset. Section 3.3 provides descriptive statistics.

### 3.1. Data sources

**Hedgerows.** We use the panel dataset produced by DREAL Normandie, the body that represents the national Ministry of Ecology at the regional level. Their sample is composed of 1175 circles covering the entire region. Within each circle, hedgerows are mapped as linear objects based on human interpretation of aerial photographs. Circles are 300 m in radius (28.3 ha) and grouped into sets of four so that adjacent circles in the same lot are 2 km apart and adjacent sets are 7.9 km apart (Figs. B.1 and B.2). DREAL Normandie chose this two-level systematic sampling plan to ensure representative coverage of the whole region and the ability to compute aggregated statistics at the sub-regional level with reasonable precision and limited monitoring costs (Vadaine, 2002).<fnref n="8" /> Data are available for all 1175 circles for four time periods: 1972, 1984, 1997/98, and 2009/10. Aerial photography campaigns took place between May and August, were black and white for 1972, 1984, and 1997/98, and color for 2009/10. The sample was established in 2002, with the first three observation periods constructed retrospectively. Designed solely for regional monitoring, the data are not used to enforce standards or economic policies. These characteristics ensure the survey sample does not interfere with hedgerow dynamics and strengthen its representativeness of Lower Normandy.

**Consolidation operations.** We have access to the LC operations carried out in France since 1944 thanks to the data collected by Philippe and Polombo (2009) from the archives of the French Ministry of Agriculture. Each year, state administrative bodies at the department level sent a table to the Ministry listing LC operations in their area. The Ministry switched to a computer database around 2000, which was subsequently augmented by Philippe and Polombo (2009) with operations occurring after 2005, when the data were no longer stored centrally. The file specifies, for each consolidation event, the municipality (*commune*), the announcement date of the LC operation *per se* (elaboration of the land transfer project by the committee), and the application date of the finalized consolidation plan, starting the development works.

**Municipality map.** We use the vector map of French municipalities produced by OpenStreetMap from municipalities' land registers to make the spatial junction between the hedgerow sampling plan and municipality-level consolidation operations. The administrative boundaries are those as at March 6, 2014, which is the cleaned version with high-resolution municipality boundaries (5 m-simplification) closest to our last observation (2010).

**Sources for covariates.** We benefit from additional datasets to compute variables, which we use to clean up our sample or include in our models (see Table B.1 for a summary of the data sources). The 1970 National Agricultural Census provides information about the crop areas and farm characteristics (size, labor intensity) at the municipality level. BDTOPO (IGN) provides vector maps of woodland cover for the years 2001/2, 2009/10 and 2012 with a lower threshold of 0.05 ha. The High-Resolution Layer on sealing rate (Copernicus) is a raster layer with a 20 m × 20 m (0.04 ha) resolution; the value of each pixel estimates the soil sealing rate.

<footnote n="8">⁸ For instance, based on 137 circles, they assess the 1998 hedgerow density of Pays d'Auge, about one tenth of Lower Normandy, with a relative precision of approximately 8.5 % (95 % confidence level). Lot grouping was chosen because it limits the number of aerial photographs needed in comparison to single-level sampling.</footnote>
The raster layers on soil physical properties produced by the European Soil Data Centre (ESDAC) describe grain texture (clay, silt, sand rates) at 500 m × 500 m (25 ha) resolution. BDALTI (IGN) is a digital terrain model covering France with a 25 m resolution, from which we can compute elevation, slope, aspect and ruggedness. Population density and growth at the municipality level are computed from the 1968 and 1975 National Population Censuses. The Official Geographic Code (INSEE) reports events related to administrative divisions since 1943. In particular, there were cases where several municipalities merged to form one, bigger unit.

## 3.2. Dataset construction

Sampling circles in the hedgerow dataset can overlap neighboring municipalities. They may especially cover consolidated and non-consolidated municipalities. To ensure homogeneity of treatment status within each unit, we intersect circles with the municipality map to obtain mono-municipality “chunks”. From 1175 circles, we obtain 1763 chunks of diverse shapes and sizes. These chunks form the individual units that we allocate to the control group (never consolidated) and three treatment groups based on their consolidation date relative to our observation years (e.g., a chunk whose municipality started development works in 1986 belongs to the 1984–1998 treatment group).<fnref n="9" />

We compute our primary variable of interest depicting the bocage landscape for each chunk at each period: hedgerow density, i.e., the length of hedgerow (in meters) per surface unit (in hectares). We exploit the spatially explicit feature of our data to compute two complementary measures related to the connectivity of the hedgerow network: connection density, i.e., the number of connections per hedgerow length unit, and a coherence index that compares the number of connections with the number of dead ends in the network (see Appendix C for exact definitions). These two indicators go beyond the sole quantity of hedgerows and consider configurational aspects of the landscape that are essential properties for regulation services and biodiversity conservation. For instance, interruptions in the hedgerow network can undermine its buffering role against hydro-sedimentary flows (Reulier et al., 2019). Hedgerow connectivity also facilitates the dispersal of woodland specialist species (Lenoir et al., 2021; Uroy et al., 2019). Because LC coordinates hedgerow uprooting at the municipality level, its impact on network configuration may qualitatively differ from its impact on density, with particular attention given to preserving the ecological functionality of hedgerow networks (Baudry and Burel, 1984).

We also compute 27 time-invariant variables listed in Table D.1 from previously introduced data sources. These are potential covariates to control for and condition our parallel trend identification assumption (Section 4). However, piling up covariates can compromise the overlap condition between our control and treated groups. Our heuristic selects a limited number of time-invariant covariates that effectively capture the heterogeneity of hedgerow dynamics based on the following criteria: the frequency of missing values, relatively high correlation with the change in hedgerow density for the 1972–2010 period within non-consolidated chunks, and low correlation with previously selected covariates. Appendix D details the selection rules and the corresponding outputs. This process retains three covariates: administrative department, 1970 average farm size at municipality level, and clay rate. We also consider subsets of these covariates to check the robustness of our results.

Following the selection of covariates, a series of rules determines which chunks in the raw sample should be used in our empirical analysis.<fnref n="10" /> These rules aim to obtain a representative sample of the Norman bocage while ensuring proper treatment assignment, removing outliers in a sensible way, and limiting the risk of spatial autocorrelation. Our selection reduces our sample size from 1763 to 897 chunks, with “1-chunk-per-circle” being the most exclusive rule. It also removed municipalities consolidated before 1972, starting with the open-field plain corridor. Appendix E details the rules and Table E.1 reports the contribution of each rule to the sample size. We run alternative estimations with more or less exclusive rules as robustness checks.

## 3.3. Descriptive statistics

Table 1 presents descriptive statistics conditioned on treatment groups and the results of distribution independence tests. Regarding initial hedgerow density, the independence test detects significant differences in distributions between groups, but the large standard deviations reflect extensive overlap. Grouped means show that consolidated municipalities do not systematically have a lower or higher hedgerow density than their non-consolidated counterparts: the average initial hedgerow density of the never-consolidated group (147 m/ha) lies between those of the first (140 m/ha) and second (156 m/ha) treated groups. However, within consolidated chunks, the higher the initial density, the later the treatment date. The last treated group notably stands out from the others with an initial density of 182 m/ha (it is also substantially smaller with 34 chunks).

In contrast, we observe strong homogeneity within treated groups in terms of the average change in hedgerow density between 1972 and 2010: around –80 m/ha, compared to the average loss for never consolidated chunks –60 m/ha with relatively high standard deviation for all groups (~45). This suggests a negative impact of LC on hedgerow density. Nonetheless, our independence tests reveal distinct distributions between groups for all our selected covariates (farm size, clay rate and department), justifying controlling for these when estimating the impact of LC. Chunk area distributions do not differ significantly between groups (mean ~25ha; full circles are 28.3 ha), indicating that our sample construction process does not engender bias between groups regarding chunk size.

<footnote n="9">⁹ Successive treatment groups share upper/lower bounds because group attribution is actually based on the *day* of treatment compared to the *day* when the aerial photograph was taken.</footnote>

<footnote n="10">¹⁰ The composition of our sample may be impacted by the previous selection of covariates because of missing values. To ensure that the sample was consistently representative, during covariate selection the correlation analysis was run for a sample cleaned using most of the selection rules employed in the final cleaning (see Appendix D for further details).</footnote>
<table><caption>**Table 1**

Descriptive statistics per treatment group.</caption><thead><tr><th></th><th>Never</th><th>1972-84</th><th>1984-98</th><th>1998-2010</th><th>p value</th></tr></thead><tbody><tr><td>Nb of chunks</td><td>574</td><td>110</td><td>179</td><td>34</td><td></td></tr><tr><td>Nb of municipalities</td><td>417</td><td>76</td><td>117</td><td>17</td><td></td></tr><tr><td>H. density 1972 (m/ha)</td><td>147.0 (65.2)</td><td>140.4 (58.4)</td><td>156.2 (58.5)</td><td>181.6 (50.4)</td><td>0.001</td></tr><tr><td>H. density 2010 (m/ha)</td><td>87.0 (46.9)</td><td>60.3 (33.1)</td><td>76.3 (34.6)</td><td>101.9 (39.8)</td><td>&lt; 0.001</td></tr><tr><td>H. change 1972-2010 (m/ha)</td><td>-60.0 (43.2)</td><td>-80.0 (44.5)</td><td>-79.9 (43.4)</td><td>-79.7 (47.1)</td><td>&lt; 0.001</td></tr><tr><td>Chunk area (ha)</td><td>24.8 (5.4)</td><td>25.6 (5.0)</td><td>25.4 (5.0)</td><td>25.7 (3.9)</td><td>0.312</td></tr><tr><td>Average farm size 1970 (ha)</td><td>21.0 (9.0)</td><td>18.0 (7.5)</td><td>16.8 (6.5)</td><td>17.9 (6.0)</td><td>&lt; 0.001</td></tr><tr><td>Clay rate (%)</td><td>21.1 (4.3)</td><td>20.1 (2.9)</td><td>19.7 (3.2)</td><td>19.8 (2.9)</td><td>&lt; 0.001</td></tr><tr><td>Department</td><td></td><td></td><td></td><td></td><td>&lt; 0.001</td></tr><tr><td>A (Calvados)</td><td>208 (36 %)</td><td>37 (34 %)</td><td>94 (53 %)</td><td>20 (59 %)</td><td></td></tr><tr><td>B (Manche)</td><td>208 (36 %)</td><td>29 (26 %)</td><td>23 (13 %)</td><td>11 (32 %)</td><td></td></tr><tr><td>C (Orne)</td><td>158 (28 %)</td><td>44 (40 %)</td><td>62 (35 %)</td><td>3 (9 %)</td><td></td></tr></tbody></table>

NOTE. Mean (standard deviation) for continuous variables, counts (percentage) for the categorical variable (administrative department). Last column reports *p*-values of Kruskal–Wallis independence test for continuous variables and Chi-square test for categorical variables. “H.” stands for hedgerow.

<figure><img src="image_5.png" /><figcaption>**Fig. 2.** Average hedgerow density over time per treatment group.

NOTE. Weighted averages, with weights proportional to chunk surface area.</figcaption></figure>

Fig. 2 provides graphical insights into the trend deviation presumably caused by consolidation. For pre-treatment periods, the trend differences are small, thus favoring the parallel trends assumption. During their respective treatment intervals, consolidated groups show visibly sharper declines in hedgerow density than the never-consolidated group. The deviation generated during the treatment period also seems to continue over the longer term, suggesting persistent impact.

## 4. Empirical framework

We use a natural experiment setup to evaluate the impact of LC on hedgerow density with a difference-in-differences strategy. Our preferred specification employs the estimator proposed by Callaway and Sant'Anna (2021) to account for treatment effect heterogeneity under staggered treatment. The nonparametric doubly-robust estimator conditions the parallel trends hypothesis on time-invariant covariates. We assess the sensitivity of our results to deviations from parallel trends following Rambachan and Roth (2023). We run alternative estimates to check the robustness of our results to different covariates, estimation options, estimators, samples, and treatment definitions. We also investigate possible local spillovers by incorporating a dedicated variable following Butts (2023). Furthermore, we examine the distributional effects by estimating Quantile Treatment effects on the Treated (QTTs) with the distributional difference-in-differences estimator proposed by Callaway and Li (2019).

### 4.1. *Estimation strategy*

Following Rubin's causal modeling framework, we aim to estimate the Average Treatment effect on the Treated (ATT), i.e., the average effect of consolidation for all chunks that have ever been in a consolidated municipality:

$$ATT = \mathbb{E}[\mathbb{E}[Y_{i,t}(1) - Y_{i,t}(0) | t \geq G_i] | D_i = 1] \quad (1)$$
$Y_{i,t}(1)$, $Y_{i,t}(0)$ being the potential consolidated and non-consolidated hedgerow densities of chunk $i$ at time $t$, $D_i$ a dummy for belonging to consolidated chunks, and $G_i$ the individual treatment time ($\infty$ if never treated). The global ATT can break down into sub-group ATTs accounting for treatment effect heterogeneity over time of treatment $g$ (group 1972–1984, 1984–1998, or 1998–2010) and time of observation $t$ (1972, 1984, 1998, or 2010):

$$ATT_{g,t} = \mathbb{E}[Y_{i,t}(1) - Y_{i,t}(0) | G_i \in g] \qquad (2)$$

$$ATT_g = \mathbb{E}[Y_{i,t}(1) - Y_{i,t}(0) | G_i \in g, t \ge G_i] \qquad (3)$$

$$ATT_t = \mathbb{E}[Y_{i,t}(1) - Y_{i,t}(0) | D_i = 1, t \ge G_i] \qquad (4)$$

We also introduce the Average Treatment Share in the dynamics of the treated (ATS) at the group and global levels:

$$ATS_{g,t} = \frac{ATT_{g,t}}{\mathbb{E}[Y_{i,t}(1) - Y_{i,t_0}(1) | G_i \in g]} \qquad (5)$$

$$ATS_t = \frac{ATT_t}{\mathbb{E}[Y_{i,t}(1) - Y_{i,t_0}(1) | t \ge G_i]} \qquad (6)$$

with $t_0 = 1972$, the first year of observation for our study period. Computed from the ATTs estimated with $Y$ in level, i.e., the change of hedgerow density caused by consolidation in meters per hectare, the ATSs represent interpretable and meaningful measures of treatment effect in the debate on LC's responsibility for hedgerow decline. In particular, $ATS_{t=2010}$ is the average share of the total density change in consolidated municipalities from 1972 to 2010 that is attributable to consolidation.

Taking advantage of our panel dataset and our natural experiment setup with a binary treatment, our estimation strategy relies on difference-in-differences. More specifically, we use the Callaway and Sant'Anna (CS) estimator (2021). First, group-time ATTs are estimated separately following the standard non-parametric difference-in-differences estimator:

$$ATT_{g,t} = \mathbb{E}[Y_{i,t} - Y_{i,g-1} | G_i \in g] - \mathbb{E}[Y_{i,t} - Y_{i,g-1} | D_i = 0] \qquad (7)$$

and its weighted sample analog:

$$\widehat{ATT}(g,t) = \frac{1}{S_g} \sum_{i,g_i \in g} s_i(y_{i,t} - y_{i,glow}) - \frac{1}{S_{D_i=0}} \sum_{i,D_i=0} s_i(y_{i,t} - y_{i,glow}) \qquad (8)$$

$y_{i,t}$ and $g_i$ being the observed value of $Y_{i,t}$ and $G_i$, $g_{low}$ the lower bound of the treatment period $g$, $s_i$ the chunk surface area, and $S_g$ and $S_{D_i=0}$ the sum of chunk surface areas in treatment group $g$ and the control group. Potential confounders on outcome trends can be accounted for with regression adjustment, inverse probability weighting, or a doubly-robust estimator (Sant'Anna and Zhao, 2020). Weighted means of group-time $\widehat{ATT}_{g,t}$s return aggregated $\widehat{ATT}_t$s:

$$\widehat{ATT}_g = \sum_t \frac{1}{T_g} \widehat{ATT}_{g,t} \qquad (9)$$

$$\widehat{ATT}_t = \sum_g \frac{S_g}{S_{D=1}} \widehat{ATT}_{g,t} \qquad (10)$$

$$\widehat{ATT} = \sum_g \frac{S_g}{S_{D=1}} \widehat{ATT}_g \qquad (11)$$

with $T_g$ the number of post-treatment periods of cohort $g$, and $S_{D=1}$ the sum of consolidated chunks' areas.<fnref n="11" /> $\widehat{ATS}$s are deduced straightforwardly from $\widehat{ATT}$s:

$$\widehat{ATS}_{g,t} = \frac{\widehat{ATT}_{g,t}}{\sum_{i,g_i=g} \frac{s_i}{S_g} (y_{i,t} - y_{i,1972})} \qquad (12)$$

$$\widehat{ATS}_t = \frac{\widehat{ATT}_t}{\sum_{i,D_i=1} \frac{s_i}{S_{D_i=1}} (y_{i,t} - y_{i,1972})} \qquad (13)$$

There are several reasons behind our choice of the CS estimator compared to other difference-in-differences estimators:

<footnote n="11">¹¹ Another aggregation rule proposed by Callaway and Sant'Anna (2021) is a direct averaging of all $\widehat{ATT}(g,t)$ with weights proportional to the number of observations rather than individuals, but doing this gives more influence to the groups treated first. Our preferred aggregation gives equal weights to each treatment group (beyond their size), irrespective of their treatment date.</footnote>
1. When treatment adoption is staggered with heterogeneous treatment effects over time or across cohorts, the common two-way-fixed-effects estimator (TWFE) may suffer from negative weights or contamination issues because of “forbidden” comparison groups (Goodman-Bacon, 2021; Sun and Abraham, 2021). Additionally, the weighting of individual treatment effects by TWFE is opaque and not necessarily policy-relevant (Roth et al., 2023). As one may expect changes in the impact of consolidation over time (see Section 2), we turn towards recent heterogeneity-robust difference-in-differences estimators. These methods avoid negative weights and contamination issues while providing transparent control groups and aggregation weights. Such features ensure a clear parallel trends assumption and a well-defined parameter of interest.

2. Among heterogeneity-robust estimators, we distinguish imputation estimators (Borusyak et al., 2024; Gardner, 2022; Wooldridge, 2021) and aggregation estimators (Sun and Abraham, 2021; Callaway and Sant'Anna, 2021; de Chaisemartin and D'Haultfœuille, 2020). While Borusyak et al. (2024) show their estimator to be BLUE for independent and identically distributed errors, Harmon (2022) shows that agglomeration estimators are more efficient when serial correlation is high. Here, hedgerow mapping at a given period is built “backwards” from the hedgerow mapping in the period just after, so, regarding measurement error, a random walk seems more plausible than idiosyncratic error. More broadly, we expect inertia in hedgerow dynamics so that persistent shocks dominate transitory ones.

3. Another helpful property of aggregation estimators is that we can conveniently access group-time ATTs (compared to imputation estimators that directly return time-since-treatment ATTs). As we only have three treatment groups with reasonable sample sizes (the last being small), it is worth analyzing treatment effect heterogeneity at the group-time level.

4. While the other agglomeration estimators include covariates linearly, CS incorporates a doubly-robust control for covariates, combining the strengths of outcome regression and propensity score modeling. This is particularly interesting because our outcome is non-negative, and a linear outcome regression may not adequately describe the relationship between covariates and a limited dependent variable.

5. Finally, CS also proposes simultaneous estimation of confidence bands to overcome multiple-testing problems, which is especially valuable when computing many group-time ATTs.

We compare the outputs from the CS estimator with those from four other estimators (Borusyak et al., 2024; Gardner, 2022; Sun and Abraham, 2021, TWFE) to examine the sensitivity of our results to this choice, regarding both point estimation and efficiency.

As for inference, the multiplier bootstrap estimator proposed by Callaway and Sant'Anna (2021) computes clustered standard errors at a given level. We cluster standard errors at the municipality level to allow for dependence within municipalities. Because the bootstrap estimator requires large samples at the cluster level, we check the number of municipalities in our total sample and each treatment group. The last treated group (1998–2010) only contains 17 different municipalities so the standard error of the corresponding estimated ATT is not reliable. The other groups are a priori large enough to estimate a sensible standard error (76 and 117 municipalities for groups 1972–84 and 1984–98 respectively). A non-nested alternative is to cluster at the lot level, which we consider in the robustness checks. In any case, clustering with such spatial units is an imperfect way to account for plausibly continuous spatial correlation, as the CS estimator is at the current time not accompanied by a spatial-HAC standard error estimator robust to spatial assignment. We also explore the sensitivity of inference to clustering versus spatial-HAC using a TWFE estimator instead of the CS estimator. Regarding the ATS, we implement a standard block bootstrap estimator based on resampling with replacement at the cluster level to estimate its standard error (MacKinnon et al., 2023). For disaggregated ATT and ATS (group ATTs and ATSs, and group-time ATTs), we use simultaneous confidence bands rather than pointwise confidence intervals to account for multiple testing.

## 4.2. Identification assumptions

Causal identification of treatment effect in our difference-in-differences framework is based on three assumptions: parallel trends, absence of anticipation, and absence of spillover (SUTVA).

### 4.2.1. Parallel trends

The parallel trend assumption depends on the relationship between participation in consolidation and the potential trends of the hedgerow network. French rural geographers give qualitative insights into the criteria influencing LC participation. At the national scale, there was a clear regional prioritization based on the preponderant production systems and their expected development pathways (Philippe and Polombo, 2009). However, at the regional scale, with more homogeneous agricultural orientations, LC participation seemed to be influenced by multiple factors. Guellec (1971) discusses how the spatial distribution of LC correlates with production orientations, pedoclimatic conditions, urban proximity, and farmers' age structure in their study areas. Government bodies could also push for consolidation in municipalities considered to be more agriculturally backward (Flatrès and Flatrès, 1997; Husson and Marochini, 1997). Nevertheless, the most frequently reported driver is the presence of local stakeholders with political influence who support (or reject) consolidation (Flatrès and Flatrès, 1997; Renard, 1973; Preux, 2019; Guellec, 1971). For instance, Renard (1973) reports situations where the government was overwhelmed by consolidation requests, so the order of priority resulted from insistent requests by local public figures and from diffusion effects.

Determining how these factors applied to the Norman bocage and how they may have correlated with average hedgerow trends in the absence of LC is not straightforward. However, these elements guided us in computing the pool of covariates introduced earlier in Section 3.2. Our selection heuristic retained three covariates: department (reflecting, among other things, local political and administrative characteristics), 1970 farm size (labor intensity), and clay rate (soil fertility). All are correlated with long-term
hedgerow density variation and show significantly different distributions among treatment groups (Tables 1 and D.2). Therefore, we relax the general parallel trend hypothesis to parallel trends conditional on these covariates.

Another factor affecting the parallel trends assumption is the composition of the control group used to build the counterfactual. Because the never-treated group is much larger than the others, our preferred specification only uses never-treated chunks. Another option would be to include the not-yet-treated observations as well. This would slightly increase the size of the control group, but also expand the scope of the identification assumption (Callaway and Sant'Anna, 2021). Finally, the pre-treatment observations in the second and third treated groups allow us to test for pre-trends and investigate the plausibility of our hypothesis (Roth, 2024). Alternatively, we use these pre-trends to bound the violation of parallel trends and assess the sensitivity of our results to such deviations (Rambachan and Roth, 2023). Because these rely on at least two pre-treatment observations, we focus on the second treated group (1984–1998).<fnref n="12" />

### 4.2.2. Absence of anticipation

Regarding the absence of anticipation, the large time range between two observation periods (12–14 years) dilutes the risk of an upcoming LC event influencing a pre-treatment value. Nevertheless, chunks whose consolidation occurs shortly after the last pre-treatment observation may be sensitive to this. Once an LC procedure had started, farmers may have waited until it was complete (less than 3 years later on average) for subsidies in development works to uproot hedgerows they would have removed sooner otherwise. As a robustness check, we ran an alternative estimation where treatment groups depend on the start date of the LC operation per se rather than the start date of development works.

### 4.2.3. Absence of spatial spillover (SUTVA)

Hedgerow removal in consolidated municipalities may induce similar clearance in neighboring municipalities, violating the stable unit treatment value assumption (SUTVA). Farm holdings are often spread across several municipalities. Suppose that consolidation was associated with changes in agricultural practices at the farm level facilitated by hedgerow removal (e.g., purchase of a larger machine). In that case, farmers may also remove hedgerows in non-consolidated parts of their farms. Farmers without parcels in consolidated municipalities may also imitate or associate with their neighbors as they witness the economic benefits.

The CS estimator, being of the 2x2 aggregator type, is not adequate to simultaneously assess total and spillover effects.<fnref n="13" /> In the context of staggered treatment, we turn towards the adaptation of the Gardner estimator proposed by Butts (2023) to test for the presence of local spatial spillover. As an imputation-type estimator, the Gardner estimator first estimates $Y(0)$ based on the not-yet-treated observations, and then plugs $\hat{Y}(0)$ into a second stage regression to estimate the treatment effect. Butts amends the second stage with a spillover dummy:

$$ \tilde{Y}_{i,t} \equiv Y_{i,t} - \hat{Y}(0)_{i,t} = \hat{\beta}_c D_{i,t} + \hat{\beta}_s S_{i,t}(1 - D_{i,t}) + \epsilon_{i,t} \quad (14) $$

with $D_{i,t}$ a dummy for consolidation of chunk $i$ at time $t$, and $S_{i,t}$ a dummy for having at least one consolidated chunk as a neighbor at time $t$. Under the assumption that spillovers are local (no spillover beyond a certain threshold delimiting neighborhood) and the extension of parallel trends to the non-consolidated neighbors of the consolidated chunks, $\hat{\beta}_c$ is an unbiased estimate of the total effect of consolidation in treated chunks (direct and spillover effects), and $\hat{\beta}_s$ is an unbiased estimate of the spillover effect in their neighborhood.<fnref n="14" /> Here, two chunks are neighbors if their municipalities have a common border. Alternatively, we replace the dummy $S_{i,t}$ by the count of consolidated neighbors to capture the potential additivity of spillover effects. We also run the event-study equivalent of Eq. (14).

## 4.3. Distributional analysis

The CS estimator unveils treatment effect heterogeneity across time $t$ and cohort $g$, but not within a given $(g, t)$ combination. However, the $ATT_{g,t}$ may hide substantial distributional effects of LC. For instance, we may expect the magnitude of the impact on hedgerow density to vary with initial density, while keeping its sign constant. LC may also have distinct impact directions depending on the initial density: in line with the safety net hypothesis (Section 2), a plausible scenario would be to observe a positive effect of LC on hedgerow density at least along the lower tail of the distribution.

We investigate such propositions by estimating the Quantile Treatment effects on the Treated (QTTs) with the distributional difference-in-differences estimator proposed by Callaway and Li (2019). QTTs are the differences in the quantiles of each potential outcome distribution:

$$ QTT(\tau) = F_{Y_{i,t|D_i=1}(1)}^{-1}(\tau) - F_{Y_{i,t|D_i=1}(0)}^{-1}(\tau) \quad (15) $$

for quantile $\tau$, $F^{-1}$ being the inverse cumulative distribution function of the potential outcome in the treated population $Y_{i,t|D_i=1}$.

The identification of QTTs with the estimator of Callaway and Li (2019) relies on two assumptions. The first is a direct extension of the mean independence assumption of the outcome's temporal change with respect to the entire distribution (distributional

<footnote n="12">¹² The small size of the third group (1998–2010) prevents us from using it in a credible manner.</footnote>

<footnote n="13">¹³ Running the CS estimator for a sample cleaned of potentially “spillover-ed” observations is not feasible here because consolidated chunks often are themselves previous neighbors of other consolidated chunks, and few chunks are neither consolidated nor neighbors of consolidated chunks for the whole time frame.</footnote>

<footnote n="14">¹⁴ It is not an unbiased estimate of the average spillover effects in the sense that our neighborhood may include non-affected individuals, and does not include consolidated chunks that may also be sensitive to spillover (Butts, 2023).</footnote>
<table><caption>**Table 2**
Global *ATT* for different sets of covariates.</caption><thead><tr><th></th><th>(1)</th><th>(2)</th><th>(3)</th><th>(4)</th><th>(5)</th></tr></thead><tbody><tr><td>ATT (m/ha)</td><td>-17.86***<br>(2.42)<br>[-22.61; -13.11]</td><td>-16.55***<br>(2.36)<br>[-21.19; -11.92]</td><td>-14.5***<br>(2.28)<br>[-18.96; -10.04]</td><td>-12.97***<br>(2.34)<br>[-17.57; -8.38]</td><td>-14.27***<br>(2.33)<br>[-18.84; -9.71]</td></tr><tr><td>Pre-test p-value</td><td>0.005***</td><td>0.078*</td><td>0.373</td><td>0.237</td><td>0.343</td></tr><tr><td>Department</td><td>No</td><td>Yes</td><td>Yes</td><td>No</td><td>Yes</td></tr><tr><td>Farm size</td><td>No</td><td>No</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Clay rate</td><td>No</td><td>No</td><td>No</td><td>Yes</td><td>Yes</td></tr><tr><td>Nb of chunks</td><td>897</td><td>897</td><td>897</td><td>897</td><td>897</td></tr></tbody></table>

NOTE. Estimations of the ATT at the global level, with the Callaway and Sant'Anna estimator. Possible covariates for doubly-robust control: department, farm size and clay rate. Sample weighting with chunk area. Bootstrap standard errors clustered at the municipality level. Standard errors in parentheses. 95 % confidence intervals in square brackets. *, **, ***: significance at the 10, 5 and 1 % levels.

difference-in-differences assumption). Because this sole assumption leads to partial identification only, the authors add a copula stability assumption: the dependence (copula) between the outcome's temporal change and its initial state is constant over time. Based on these two assumptions, the counterfactual distribution of $Y_{i,t|D_t=1}(0)$ can be restored and the QTTs point-identified. As estimating the copula requires at least two pre-treatment periods, we only assess the QTTs for the second treatment group "1984-1998".<fnref n="15" /> Other methods such as change-in-changes (Athey and Imbens, 2006) do not necessitate supplementary pre-treatment observations but the identification assumptions are more distant from the parallel trends assumption used in our main estimation.

# 5. Results

Results are presented in the following order. Section 5.1 shows the average effects of LC on hedgerow density under our preferred specification at the aggregated and disaggregated levels, along with a sensitivity analysis to trend deviation following Rambachan and Roth (2023). Section 5.2 checks the robustness of the results for alternative estimation options, estimators, samples and treatment definitions. Section 5.3 discusses the sources of heterogeneity in treatment effects observed in the previous sections. Section 5.4 studies potential spatial spillovers. Section 5.5 focuses on the distributional effects of LC through QTT. Section 5.6 looks at the impact of LC on other hedgerow metrics that reflect network connectivity.

## 5.1. Main results

Table 2 presents the global ATT estimated with the CS estimator and five different sets of covariates. All specifications show a negative and highly significant impact of LC on hedgerows. Our preferred specification (model 5) estimates the impact to be -14.3 m/ha with a standard error (SE) equal to 2.33. The joint test for pre-trends shows no significant deviation from parallel trends ($p-value = 0.34$) in favor of the plausibility of conditional parallel trends. Models 1-4 are variants with no or a subset of covariates. Point estimates of ATTS vary between -13.0 and -17.9 m/ha, and all the 95 % confidence intervals overlap with each other. However, pre-tests reveal significant pre-trend deviations for models 1 and 2, hampering the plausibility of parallel trends that are unconditional or conditioned on the administrative department only.

The first part of Table 3 presents the breakdown of the global ATT for our preferred specification into group ATTS ($ATT_g$) and highlights the heterogeneity of the consolidation impact across groups. Only the first two groups have a significant ATT, with substantial differences in their point estimates, although their confidence bands overlap at the 95 % level. The chunks that were consolidated first (1972-1984) experienced a relatively high loss of -21.9 m/ha (SE = 4.09), compared with -10.0 m/ha (SE = 2.95) for the chunks consolidated during the period 1984-1998. For units consolidated during the period 1998-2010, the point estimate is comparable to the others (-11.6 m/ha), but the large standard error (5.77) prevents us from concluding a significant impact, presumably due to the small sample size of the last group (34 chunks).

The second part of Table 3 reformulates the global and group impacts of LC in terms of ATS, i.e., as a percentage of the total loss of hedgerow density observed for the period 1972-2010. It appears that LC accounts for 13.7 % of the observed loss of density in the total treated population (SE = 3.29). Point estimates of each treatment group show heterogeneous ATS, from 9.17 % in the second treated group, to 20.6 % in the first treated group, and only the first treated group is significantly different from 0 (at the 99 % level). Taken together, these results emphasize that LC only explains a limited part of the total hedgerow decline over the study period.

Fig. 3 displays the impact of consolidation over time for each treatment group ($ATT_{g,t}$). In line with the non-significant joint pre-test (Table 2), the confidence bands overlap zero for all three pre-treatment estimations. Consolidation in the first group has a persistent and significant impact. The point estimates indicate that the impact decreases over time since consolidation. This trend reveals that in the longer term, the decline of hedgerow density in consolidated municipalities becomes slower than in non-consolidated ones, partly compensating for the abrupt negative shock that consolidation initially caused on the hedgerow network. However, as the confidence bands overlap, it is only possible to make cautious conjectures. For the second treated group, ATT is only significant in the short term. In the longer term, the point estimate is smaller, resembling the dynamics observed in the first treated group, although

<footnote n="15">¹⁵ The small sample size of the third group "1998-2010" (34 chunks) makes QTT estimations irrelevant.</footnote>
<table><caption>**Table 3**
ATTs and ATSs at the global and group levels.</caption><thead><tr><th></th><th>Treated</th><th>1972-1984</th><th>1984-1998</th><th>1998-2010</th></tr></thead><tbody><tr><td colspan="5"><strong>A - ATT: impact in level</strong></td></tr><tr><td>ATT (m/ha)</td><td>-14.27***<br>(2.33)<br>[-18.84; -9.71]</td><td>-21.94***<br>(4.09)<br>[-31.57; -12.32]</td><td>-10.04***<br>(2.95)<br>[-16.97; -3.1]</td><td>-11.58<br>(5.77)<br>[-25.16; 2]</td></tr><tr><td>ATT<sub>t=2010</sub> (m/ha)</td><td>-11.06***<br>(2.84)<br>[-17.59; -4.54]</td><td>-16.88***<br>(4.63)<br>[-29.04; -4.72]</td><td>-7.36<br>(3.58)<br>[-16.77; 2.04]</td><td>-11.58<br>(5.69)<br>[-26.53; 3.37]</td></tr><tr><td colspan="5"><strong>B - ATS: impact in share of total loss</strong></td></tr><tr><td>1972-2010 loss (m/ha)</td><td>-80.95 (43.7)</td><td>-82.12 (44.05)</td><td>-80.28 (43.09)</td><td>-80.66 (45.6)</td></tr><tr><td>ATS<sub>t=2010</sub> (% tot. loss)</td><td>13.67***<br>(3.29)<br>[7.2; 20.14]</td><td>20.55***<br>(4.43)<br>[9.11; 32]</td><td>9.17<br>(4.32)<br>[-1.98; 20.33]</td><td>14.36<br>(6.6)<br>[-2.67; 31.38]</td></tr><tr><td>Nb of chunks</td><td>323</td><td>110</td><td>179</td><td>34</td></tr></tbody></table>

NOTE. Estimation of the ATTs and the ATSs at the global and group levels, with the Callaway and Sant'Anna estimator. ATS divides the ATT in 2010 by the average total loss observed for the 1972-2010 period. Doubly-robust control with covariates: department, farm size, clay rate. Sample weighting with chunk area. Bootstrap standard errors clustered at the municipality level. Standard errors in parentheses (standard deviation for historical loss). 95 % simultaneous confidence bands in square brackets. *, **, ***: significance at the 10, 5 and 1 % levels.

<figure><img src="image_6.png" /><figcaption>**Fig. 3.** $\hat{ATT}(g, t)$.</figcaption></figure>

NOTE. Estimation of group-time ATTs with the Callaway and Sant'Anna estimator. Doubly-robust control with covariates: department, farm size, clay rate. Sample weighting with chunk area. 95 % bootstrap-estimated simultaneous confidence bands.

large standard errors limit conclusions. Finally, the last treated group shows a point estimate noticeably below zero compared to the pre-trend point estimates, although its confidence band overlaps zero.

Previous results rely on conditional parallel trends. Fig. 4 checks the robustness of our conclusions when allowing for deviations from parallel trends. Because at least two pre-treatment observations are necessary to calibrate the authorized deviation, this is only done for our second and biggest treatment group (1984–1998). In the short term (1998), the impact of LC on hedgerow density is robust to trend deviations, as it remains significantly negative until it reaches twice the observed pre-treatment deviation. The long term impact (measured in 2010) is significant under parallel trends (in the absence of correction for multiple testing, see Fig. 3), but becomes non significant even for a small deviation.

## 5.2. Robustness checks

Our preferred specification indicates a negative and highly significant impact of LC on hedges, with robust evidence even with a potential deviation from parallel trends. This provides the first causal evidence that consolidation decreased hedgerow density,
<figure><img src="image_7.png" /><figcaption>Fig. 4. Sensitivity analysis of $\hat{ATT}(g,t)$ to trend deviation.</figcaption></figure>

NOTE. Confidence interval at 95 % of $ATT(g,t)$ for the 1984–1998 group, allowing for $\bar{M}$ times the average deviation between control and treated groups in the pre-treatment observation. Interval estimation following Rambachan and Roth (2023) with our preferred specification (with no correction for multiple testing). For example, the short-term impact of LC on hedgerow density for the treatment group 1984–1998 is strictly negative at the 95 % level when allowing up to twice the trend deviation observed pre-treatment.

although only by a small proportion of the total decline observed in consolidated municipalities. The results may still be sensitive to some of the empirical choices made. We conduct several robustness checks with alternative modeling choices regarding (i) CS estimator estimation options, (ii) the difference-in-differences estimator, (iii) sample composition, (iv) treatment assignment reference date, and (v) the standard error estimator. We find no statistical difference from our main results. Fig. 5 groups together the results of all specifications expressed in global ATT. Detailed, disaggregated results are available in the Appendices.

**Alternative estimation options.** First, we change the estimation options selected when running the CS estimator: control group (never-treated only, or never-treated and not-yet-treated), sampling weights (chunk area or uniform), covariate control method (doubly-robust, inverse probability weighting, outcome regression), standard-error clustering (municipality, lot or chunk). The estimations show little sensitivity to these modifications (Fig. 5).

**Alternative difference-in-differences estimators.** Second, we use alternative difference-in-differences estimators: TWFE, Borusyak et al. (2024); Gardner (2022); Sun and Abraham (2021). Because the identified parameter and the corresponding assumptions slightly differ, even after adjusting estimation options, comparing their outputs requires careful interpretation (Roth, 2024). Nonetheless, Fig. 5 shows statistically similar estimations of the global treatment effect. Fig. F.1 plots disaggregated treatment effects as event-studies. For each estimator, we find no evidence of pre-treatment deviation, and consolidation has a negative, significant and persistent impact on our outcome.

The similarity between the outputs of each estimator stems from the composition of our sample: the never-treated group, comprising 574 of the 897 chunks, is much larger than any treated group and serves as the dominant control group for every estimator. For TWFE, the Bacon decomposition reveals that the weight of treated vs. never-treated comparisons in the aggregated ATT is 88 %, whereas the weight of forbidden comparisons (lately treated vs. early treated) is only 7 %. Since our CS estimator exclusively uses never-treated units as controls, the estimates from CS and TWFE are closely aligned. Furthermore, we observe no substantial differences in the size of the confidence intervals, indicating that using a heterogeneity-robust estimator does not lead to an efficiency cost. While we have considered CS to be the best fit for our study case (Section 4.1), it is reassuring that our results are not highly sensitive to this choice.

**Alternative rules for sample selection.** Third, we check the sensitivity of our results to the rules of sample selection. A strict and a loose set of rules are respectively more and less exclusive compared to our preferred sample regarding chunk size, maximum proportion of non-agricultural land, uniqueness of consolidation,<fnref n="16" /> and one-chunk-per-circle only. The corresponding samples contain 487, 897 and 1429 chunks. Estimates are statistically similar at the aggregated and disaggregated levels (Fig. 5, Table F.2, Fig. F.2).

**Alternative reference date.** Fourth, we use the announcement of an LC operation as a reference date for treatment group assignment instead of the start date of the development works to check for potential anticipation. We observe no statistical differences (Fig. 5, Table F.3, Fig. F.3).

<footnote n="16">¹⁶ On the one hand, a few municipalities underwent more than one consolidation process. On the other hand, one current municipality can result from several former municipalities that had different consolidation histories.</footnote>
<figure><img src="image_8.png" /><figcaption>Fig. 5. Sensitivity of ATT estimation to alternative specifications.</figcaption></figure>

NOTE. Results grouped by “dimension” of our sensitivity analysis: (i) covariates, (ii) estimator, (iii) sampling rules, (iv) treatment date, (v) estimation options, (vi) standard error estimator. Confidence interval at 95 %. Preferred specification in red. CS: Callaway and Sant’Anna; SA: Sun and Abraham; G: Gardner; BJS: Borusyak, Jarvel and Spiess; TWFE: two-way fixed effects.

**Robustness to spatial autocorrelation.** Finally, we switch to the TWFE estimator, relying on the qualitatively similar results we obtain using CS or TWFE, to explore the sensitivity of inference when using two-way clustering and spatial-HAC standard error estimators. The spatial-HAC estimators allow for correlation in the TWFE error terms up to 20, 50 and 150 km between chunks. Although the standard errors tend to be bigger for large geographical distances, estimates remain significantly different from zero (Figs. 5, F.4 and F.5).

### 5.3. On the heterogeneity of treatment effects

Table 3 and Fig. 3 show substantial heterogeneity in treatment effects across cohorts and over time since treatment, as confirmed by robustness checks. We discuss plausible explanations for these patterns.

Regarding heterogeneity across cohorts, the impact of the first wave of consolidation (1972–1984) is noticeably higher, with its point estimate being approximately twice that of the subsequent waves (Table 3). We propose two interpretations related to the timing of LC operations. The first source of heterogeneity relates to the remaining stock of hedgerows available for uprooting. Suppose that LC in a municipality aims to achieve a “satisfactory” hedgerow density, regardless of when the operation takes place. The later LC occurs, the fewer hedgerows need to be uprooted to reach this density, as some uprooting may have already occurred outside LC. This is illustrated by the decline in hedgerow density observed in the not-yet-treated observations (Fig. 2). Fig. 2 also shows that the hedgerow density in the last pre-treatment observation is substantially higher for the first treated group (144 m/ha) compared to the other two groups (131 m/ha and 126 m/ha): more hedgerows remained to be uprooted in the first treated group. Additionally, the fact that the total variation in density over the period 1972–2010 is very similar across treated groups, approximately -80 m/ha, provides additional support for this interpretation.

Nevertheless, this cannot be the sole source of heterogeneity, as the impact of LC for a given year would otherwise be identical across all groups. This is not supported by the point estimates in Fig. 3, although the confidence bands warrant caution. The second explanation we propose, also related to the timing of LC, concerns changes in the regulatory framework surrounding these operations. Specifically, a first law, introduced in 1975, mandated the presence of a person qualified in nature conservation within the municipal commission responsible for LC. This person ensured that environmental considerations were included in discussions. Notably, this law came into effect after the peak in LC in 1973 (Fig. A.1). A second law, introduced in 1976, added the requirement for an
environmental impact assessment prior to implementing LC. These studies would identify sensitive areas and propose non-binding mitigation measures. The law came into effect in 1978 for new LC projects. Given that an average of three years typically passed between the declaration of a project and the start of development works, this law applies in our study to LC projects whose uprooting began around 1981.

These regulatory developments reflect the growing influence of environmental movements and increased societal expectations regarding environmental protection (Preux, 2019). These societal pressures may have also curbed hedgerow uprooting, making it difficult to disentangle their effects from regulatory impacts. Furthermore, another significant regulatory change was the transfer of LC financing from the State to the departments (administrative-territorial units) in 1983. While departments continued to fund development works, it is possible that some were less inclined to finance uprooting, particularly in a context of heightened local environmental expectations.

An alternative source of heterogeneity across cohorts relates to group composition. However, Fig. 1 shows that consolidated municipalities in our study period are relatively dispersed. Moderate-sized clusters are observable, but there is no systematic spatial aggregation based on the period of land consolidation. Furthermore, covariates related to soil characteristics and farm size exhibit similar means across treated groups, particularly compared to the control group (Table 1). Similarly, while the 1970 share of grassland in farmland is lower for the first treated group (77.1 %, SD = 13.1) than for the two others (85.7 %, SD = 9.09; 87.5 %, SD = 5.3), these differences diminish when looking at the last pre-treatment observation (77.1 %, SD = 12.5; 77.4 %, SD = 12.4; 73.4 %, SD = 10.7). Although variations in treatment effects related to group composition cannot be ruled out, they are likely to be a secondary explanation.

With regard to the heterogeneity of treatment effects over time since treatment, the point estimates in Fig. 3 suggest a decreasing impact over time. Combined with the steady decline in hedgerow density in never-consolidated municipalities (Fig. 2), these trends reveal that the impact of LC is composed of a persistent negative effect and a temporary accelerating effect. Specifically, LC led to earlier uprooting of hedgerows that would have otherwise been removed at a later date. Under these conditions, the never-consolidated municipalities gradually “catch up” with the hedgerow decline, leading to a reduction in the gap between consolidated and non-consolidated municipalities. Another potential explanation for the time-since-treatment heterogeneity is the reallocation of land clearance resources over time between consolidated and non-consolidated municipalities. As this involves spatial spillover, we consider this channel in the next section.

## 5.4. Spatial spillover

Previous specifications assumed the absence of spillover. However, LC operations could trigger uprooting beyond the borders of the consolidated municipalities, for example, from farmers with land in consolidated and non-consolidated municipalities, or by prompting other farmers to imitate their neighbors. Such interferences between units would violate the SUTVA assumption on which our identification strategy relies (see Section 4.2.3), and would bias our estimate downward (in absolute value).

Table 4 considers this scenario by introducing neighboring variables in specifications estimated with Gardner's estimator, as suggested by Butts (2023).<fnref n="17" /> We find no statistical difference regarding the local effect on the consolidated chunks with or without spillover variables, although the point estimate is smaller in absolute value when including these variables (−12.2 m/ha against −14.2 m/ha). The spillovers on non-consolidated chunks are also non-significant, using a dummy variable (model 2) or a count variable (model 4). Fig. G.1 extends the spillover analysis with an event-study plot for the total and the spillover effects.<fnref n="18" /> Here again, the dynamic total local effect when including a spillover dummy is not statistically different from our default specification for all time periods. As for dynamic spillover, no significant effect appears at the 95 % confidence level, although the short term effect narrowly overlaps zero, as well as the long-term placebo (but no correction for multiple testing is applied, and the long-term placebo only accounts for the last group with few individuals).

Taken together, these estimations do not provide substantial evidence for a spillover effect. If any, point estimates suggest a (short-term) positive effect, which can seem surprising as we first assumed a propagation of the negative impact on hedgerow density. A first explanation for a positive spillover would be an anticipation effect from neighboring municipalities that expect to be under consolidation soon and wait for development subsidies. In that case, we would expect the positive spillover effect to be mainly conveyed by to-be-consolidated neighbors. Model 3 in Table 4 follows this lead by differentiating the spillover impact for never and not-yet consolidated municipalities. However, the estimated impact in not-yet-treated chunks is not significantly different from zero, and the point estimate is actually negative. A second explanation, consistent with the decreasing effect of LC over time since treatment discussed in Section 5.3, is the temporal reallocation of land-clearance resources between consolidated and non-consolidated municipalities. LC could saturate the local resources available for uprooting hedgerows, whether it be the farmers' time to plan and monitor clearance in a holding covering several municipalities, the availability of equipment (e.g., bulldozers) and human resources for uprooting, or the attribution of clearance subsidies at the department level. In the absence of significant effects, this channel is

<footnote n="17">¹⁷ As explained in Section 4.2.3, we cannot use Callaway and Sant'Anna's estimator. Nevertheless, the robustness checks in Section 5.2 suggest we can use Gardner's estimator as a reasonable alternative to address the case of spatial spillover.</footnote>

<footnote n="18">¹⁸ Unlike Callaway and Sant'Anna (2021), Gardner (2022) does not compute the group-time effects so treatment effect heterogeneity is only investigated in terms of time since treatment through an event-study. However, because the number of observation periods is only 4, the treatment effects here are only representative of a subset of the total treated sample, except for the short term impact: e.g., the treatment effect 24–36 years *after* consolidation is only representative of the group treated in 1972–1984, whereas the placebo effect 24–36 years *before* consolidation only accounts for the group treated in 1998–2010.</footnote>
<table><caption>**Table 4**
ATT under spatial spillover.</caption><thead><tr><th></th><th>(1)</th><th>(2)</th><th>(3)</th><th>(4)</th></tr></thead><tbody><tr><td>Consolidated</td><td>-14.16***<br>(2.78)<br>[-19.61, -8.71]</td><td>-12.20***<br>(3.55)<br>[-19.17, -5.23]</td><td>-12.20***<br>(3.67)<br>[-19.39, -5.01]</td><td>-12.20***<br>(3.53)<br>[-19.12, -5.28]</td></tr><tr><td>At least 1 conso. neigh.</td><td></td><td>3.55<br>(2.48)<br>[-1.31, 8.41]</td><td></td><td></td></tr><tr><td>Never-treated × At least 1 conso. neigh.</td><td></td><td></td><td>4.26<br>(2.78)<br>[-1.19, 9.72]</td><td></td></tr><tr><td>Not-yet-treated × At least 1 conso. neigh.</td><td></td><td></td><td>-1.35<br>(3.34)<br>[-7.90, 5.20]</td><td></td></tr><tr><td>Nb of conso. neigh.</td><td></td><td></td><td></td><td>1.66<br>(1.11)<br>[-0.51, 3.84]</td></tr><tr><td>Nb of chunks</td><td>714</td><td>714</td><td>714</td><td>714</td></tr></tbody></table>

NOTE. The table displays the impact of LC on hedgerow density for specifications with or without spillovers, following Butts (2023). (1): no variable for spillover; (2): dummy equal to 1 when a non-consolidated chunk neighbors at least one consolidated chunk (at the municipality level); (3): distinct dummies for never and not-yet consolidated chunks; (4): count variable that equals the number of consolidated neighbors for non-consolidated chunks (at the municipality level). Two municipalities are neighbors if they share a common border. Covariates: department, farm size and clay rate. Standard errors clustered at the municipality level. Sample weighting with chunk area. Standard errors in parentheses. 95 % confidence intervals in square brackets. *, **, ***: significance at the 10, 5 and 1 % levels.

<figure><img src="image_9.png" /><figcaption>**Fig. 6.** Quantile treatment effects on the treated (QTT).</figcaption></figure>

NOTE. Estimation of the QTTs of consolidation on hedgerow density with Callaway and Li (2019)'s distributional difference-in-differences estimator. Pointwise confidence intervals (long-dashed) and simultaneous confidence bands (short-dashed) at 95 % from bootstrap standard errors. Control for covariates (department, farm size and clay rate) with quantile regression. For example, the QTT of the 80th percentile for the 1984–1998 treatment group is negative in the long term and significantly different from zero at the 95 % level only without correction for multiple testing.

arguably limited, although our estimations show large standard errors. In any case, our analysis remains consistent with the scenario of a significant but moderate impact of LC on the decline of hedgerows in our study area.

## 5.5. Distributional effects

The distributional difference-in-differences implemented for the second treatment group (1984–1998) reveals heterogeneous effects of LC across hedgerow distribution (Fig. 6). QTTs follow an overall decreasing trend with quantiles $\tau$, so the loss in hedgerow density is biggest on the right-hand tail of the distribution. If we assume (partial) rank correlation between potential outcomes, i.e., a chunk (roughly) conserves its position in the distributions of $Y(0)$ and $Y(1)$, and across time, i.e., a chunk (roughly) conserves its position in the distribution of $Y(0)$ between two periods, this means that places initially having more hedgerows tend to lose more
<table><caption>**Table 5**
Aggregated $\hat{ATT}$ and $\hat{ATS}$ for network connectivity indicators.</caption><thead><tr><th></th><th>H. density (m/ha)</th><th>Connection density (/km)</th><th>Coherence index (p.p.)</th></tr></thead><tbody><tr><td>ATT</td><td>-14.58***<br>(2.29)<br>[-19.07; -10.09]</td><td>-4.22***<br>(0.62)<br>[-5.44; -3.01]</td><td>-7.52***<br>(1.09)<br>[-9.65; -5.39]</td></tr><tr><td>ATS<sub>t=2010</sub></td><td>13.89***<br>(3.14)<br>[7.82; 19.95]</td><td>22.48***<br>(4.15)<br>[14.39; 30.56]</td><td>19.68***<br>(3.77)<br>[12.29; 27.07]</td></tr><tr><td>Pre-test p-value</td><td>0.26</td><td>0.2</td><td>0.13</td></tr><tr><td>Nb of chunks</td><td>873</td><td>873</td><td>873</td></tr></tbody></table>

NOTE. Connection density: number of connections per hedgerow kilometer. Coherence index: connections as a percentage of intersections (connections and dead-ends). The table shows the global ATT (expressed in levels) and the 2010 ATS (expressed as a percentage of the 1972–2010 loss) for each dependent variable, estimated using the Callaway and Sant'Anna estimator. The common sample for all three indicators is slightly different from that used in our preferred specification because the connectivity indicators can take non-defined values (typically when there is no hedgerow in a chunk). Covariates (doubly-robust control): department, farm size and clay rate for density and connectivity, department and grassland share for coherence. Sample weighting with chunk area. Standard errors in parentheses. 95 % confidence intervals in square brackets. *, **, ***: significance at the 10, 5 and 1 % levels.

of them under consolidation. This is credible as we may expect the consolidation effort to be greater in initially more “cluttered” municipalities in order to reach a “satisfactory” state.

Most interestingly, we find some evidence of a *null or positive* impact of LC on the left-hand part of the distribution. In the long term, the 30th, 40th and 50th percentiles have a quasi-null point estimate of their QTT, whereas the 10th and 20th percentiles experience a positive impact of +14.4 and + 9.84 m/ha, respectively (but the 95 % confidence bands include zero). Such reversal of treatment effect suggests that for places with low initial hedgerow density, the conservationist hypothesis applies: LC preserved the hedgerow network in the sense that more hedgerows would have been uprooted in its absence.

We use the change-in-changes estimator as a robustness check and as an extension of the QTT estimations to the first treatment group (Fig. H.1). We broadly observe the same decreasing pattern but no evidence of a positive impact on the left-hand tail and large confidence bands.

Overall, these results also indicate that LC contributed to a homogenization of hedgerow density at the regional scale, by causing higher losses in hedgerow-rich areas and smaller losses (or even relative gains) in hedgerow-poor areas.

## 5.6. Network connectivity

This section extends the analysis to two configurational indicators that describe the state of the hedgerow network beyond the sole length of hedgerows. Connection density refers to the number of connections per unit of hedgerow length, whereas the coherence index compares the density of connections with the density of dead ends. Descriptive statistics indicate that the historical loss in the density of the hedgerow network (−52 %) has been accompanied by a loss in its connectivity: connection density fell by 17 connections per kilometer, or −47 %, and the coherence index lost 36 p.p., i.e., a decrease of −45 % compared to 1972 (Table C.1).

Table 5 assesses the extent to which LC contributed to this decline. Similarly to hedgerow density, the ATTs show that LC had a highly significant negative impact on connection density (−4.22 connections/km, SE = 0.62) and coherence (−7.52 p.p., SE = 1.09). The ATSs compare the impact of LC with the historical loss. LC is responsible for 22 % and 20 % of the historical decrease in connection density and coherence index respectively, compared to 14 % of the loss in hedgerow density. Fig. 7 complements these aggregated results. The temporal heterogeneity of the treatment effect on connectivity measures closely resembles that observed for hedgerow density, but a noticeable difference is the temporal persistence of the impact of LC for the second group.

Overall, a loss in configurational quality has accompanied the loss in quantity in the hedgerow network. As we observed for hedgerow density, the involvement of environmental experts and impact assessments may have contributed to the reduction in impact across cohorts, but could not fully prevent it. Furthermore, the more persistent impact and higher ATS suggest that the catching-up phenomenon described in Section 5.3 is weaker regarding network connectivity. LC operations arguably had a more detrimental effect on connectivity than later private uprooting initiatives. These trends imply that, from the consolidators' perspective, some of the benefits associated with a less dense hedgerow network are linked to a reduction in its connectivity. In particular, reduced connectivity may improve the accessibility of agricultural plots by creating lanes and field entrances that are accessible to tractors. More broadly, minimizing connectivity loss can conflict with the goals of LC, as this constraint prioritizes hedgerow uprooting at the edges of the network. This can quickly become incompatible with the primary objective of consolidating neighboring plots across the entire municipality.

However, the loss of connectivity can generate environmental costs, as part of the hedgerow network's functionality — and the ecosystem services it provides — depends on this connectivity. This is especially important for regulating hydro-sedimentary flows and biodiversity conservation (Reulier et al., 2019; Uroy et al., 2019). The spatial, non-linear nature of these effects places a quantitative assessment beyond the scope of this study (Appendix I.3). Connectivity indicators serve as useful second-best proxies and are relevant for regional administrative bodies, which include them in their monitoring procedures (ANBDD, 2021).
<figure><img src="image_10.png" /><figcaption>Fig. 7. ATT(g,t) for different hedgerow indicators.</figcaption></figure>

NOTE. Estimation of group-time ATTs with the Callaway and Sant'Anna estimator. Connection density: number of connections per hedgerow kilometer. Coherence index: connections as a percentage of intersections (connections and dead-ends). The common sample for all three indicators is slightly different from the one used in our preferred specification because the connectivity indicators can take non-defined values (typically if there is no hedgerow in a chunk). Covariates (doubly-robust control): department, farm size and clay rate for density and connectivity, department and grassland share for coherence. Sample weighting with chunk area. 95 % bootstrap-estimated simultaneous confidence bands.

## 6. An outline cost-benefit analysis of hedgerow removal

Our econometric analyses have established the negative impact of LC on the hedgerow network, quantifying the decline in hedgerow density and connectivity. While gains in agricultural productivity have motivated these landscape changes, the overall welfare implications are unclear, as productivity gains must be balanced against the costs of development work and the loss of the environmental benefits provided by hedgerows. This section presents an outline of a cost-benefit analysis of hedgerow loss due to LC (see Appendix I for a comprehensive description). Given the exclusion of several environmental impacts and the reliance on simplifying assumptions, these back-of-the-envelope calculations should be viewed as illustrative, providing a preliminary sense of the likely magnitude and direction of the net economic effects.

Our partial cost-benefit analysis translates the estimated effects of LC on hedgerow dynamics into monetary terms. Given the inherent complexity of fully accounting for the wide range of ecosystem services provided by hedgerows, we focus on a subset of costs and benefits for which reliable valuation is available in the literature. Specifically, we consider agricultural production, wood production, and carbon emissions, as these components are well-documented and allow for transparent and tractable quantification. Gains in food production account for both the additional land available for cultivation and the efficiency gains from larger, more accessible parcels following hedgerow removal. Hedgerow harvesting includes the costs of uprooting and maintenance, as well as the benefits from wood production. Finally, carbon release encompasses the carbon emissions from soil and biomass, along with the substitution of wood for fuel as an energy source.

All parameters are fixed based on reference values from the literature, except for two that are either unknown or reflect normative choices: the technical efficiency gain, which equals 1 %, 3 % or 7 %, and the social discount rate, which is set at 2 %, 3 %, or 4 %. The detailed sources, methodological choices, and assumptions are presented in Appendix I.1. We emphasize that our calculations ignore environmental impacts such as the regulation of erosion and runoff, biodiversity conservation, or landscape aesthetics, as corresponding reference values, whether monetary or biophysical, are, to our knowledge, unavailable. Appendix I.3 succinctly reviews the study of these benefits in environmental sciences and the challenges with their (monetary) quantification.

Table 6 summarizes the net results of the cost-benefit analysis for different combinations of social discount rates and technical efficiency gains. It considers three perspectives: farmers’ private interest, public interest excluding carbon emissions, and public interest including carbon emissions. From the farmers’ perspective, hedgerow uprooting is estimated to be profitable in most cases, with their net benefit ranging from –€13 M to +€123 M (2011 constant €). However, from a public perspective, excluding carbon externalities, the outcome ranges from –€202 M to +€3 M and is therefore negative in every scenario but one. This gap reflects the 60 % subsidy covering the substantial uprooting costs (Table I.4). The production gains compensate for the initial uprooting costs only under a corner scenario with optimistic efficiency gains and a low social discount rate. Finally we estimate the total cost of carbon emissions to range from –€209 M to –€55 M, depending on the discount rate, mainly due to biomass stock depletion and the substitution of firewood for fuel (Table I.5). With costs of a comparable magnitude to market benefits, including carbon externalities significantly worsens the aggregated estimates. The net public impact of hedgerow uprooting is consistently negative, ranging from –€310 M to –€108 M.

Overall, the net benefits of the additional hedgerow loss induced by LC are, at best, ambiguous. While public subsidies have made these landscape changes privately profitable for farmers, production gains exceed uprooting costs only under a low discount rate
<table><caption>**Table 6**
Net present value of hedgerow loss from the farmers' and public perspectives.</caption><thead><tr><th rowspan="3"></th><th rowspan="3"></th><th colspan="4">Farmers</th><th colspan="4">Public without carbon</th><th colspan="4">Public with carbon</th></tr><tr><th colspan="4">Discount rate</th><th colspan="4">Discount rate</th><th colspan="4">Discount rate</th></tr><tr><th>2 %</th><th>3 %</th><th>4 %</th><th>2 %</th><th>3 %</th><th>4 %</th><th>2 %</th><th>3 %</th><th>4 %</th><th>2 %</th><th>3 %</th><th>4 %</th></tr></thead><tbody><tr><th>Technical</th><td>1 %</td><td>1</td><td>-5</td><td>-13</td><td>-101</td><td>-144</td><td>-202</td><td>-310</td><td>-227</td><td>-257</td><td>-310</td><td>-227</td><td>-257</td></tr><tr><th>efficiency</th><td>3 %</td><td>35</td><td>34</td><td>32</td><td>-66</td><td>-104</td><td>-157</td><td>-276</td><td>-187</td><td>-211</td><td>-276</td><td>-187</td><td>-211</td></tr><tr><th>gain</th><td>7 %</td><td>105</td><td>114</td><td>123</td><td>3</td><td>-25</td><td>-66</td><td>-206</td><td>-108</td><td>-120</td><td>-206</td><td>-108</td><td>-120</td></tr></tbody></table>

NOTE. Net present values are in millions of 2011 euros. The farmers' perspective includes the gain in land for cultivation, efficiency gains from larger plots, the loss in wood, savings in maintenance costs, and 40 % of the uprooting cost. The public perspective, excluding carbon, adds the remaining subsidized 60 % of the uprooting cost to the net result from the farmers' perspective. The public perspective, including carbon, incorporates the cost of carbon release.

and optimistic assumptions about efficiency gains. Accounting for the carbon externality further worsens the outcome, resulting in a negative net present value on the order of several hundred million euros over the study period.

## 7. Conclusion

We evaluated the impact of land consolidation on landscape dynamics in the context of the French consolidation program, focusing on changes in the Lower Normandy hedgerow network between 1972 and 2010. Using a difference-in-differences framework with a heterogeneity-robust estimator, we found nuanced results regarding the contribution of LC to historical hedgerow decline.

On the one hand, we found that LC had a highly significant negative impact on hedgerow density, reducing it by 14.3 m/ha. This decline was also reflected in indicators of network connectivity. Land transfers and development work associated with LC have led to more hedgerow removal than in the absence of intervention. This finding contradicts the proposition that consolidation played a protective role by maintaining the hedgerow network, at least on average (Guellec, 1971; Renard, 1973). On the other hand, we estimated that the impact of LC operations on hedgerow density accounted for only 13.7 % of the total decline observed during our study period. Interestingly, in areas with low hedgerow density, there is even some evidence of a beneficial impact of consolidation. In some municipalities, coordinating and rationalizing hedgerow clearance may have mitigated the decline compared to *laissez-faire*. We also found that the average negative effect decreased with the timing of consolidation, possibly due to the integration of higher environmental expectations or norms, and with time since consolidation, as non-consolidated municipalities experienced more hedgerow loss later on. Additionally, we found no evidence of spillover effects beyond consolidated areas. This research demonstrates that land consolidation cannot be solely blamed for the decline of hedgerows and refutes the rhetoric that uses LC as a convenient scapegoat (Preux, 2019). It is also consistent with recent analyses that found LC did not trigger the structural transformation of French agriculture (Chabe-Ferret and Enrich, 2021, preprint).

Nevertheless, this additional hedgerow uprooting had economic and environmental impacts, for which we have provided a preliminary and partial estimate based on back-of-the-envelope calculations. Our analysis suggests that farmers are likely to have benefited from subsidized hedgerow clearance, with estimated net gains ranging from –€13 M to +€123 M (2011 constant euros). However, from a public perspective, the present net value of hedgerow removal falls between –€310 M and –€108 M. A major driver of this public cost is the opportunity cost of carbon emissions, estimated between –€209 M and –€55 M. Although these figures are tentative, they suggest that from a societal perspective, the economic benefits of increased production did not outweigh the combined costs of landscape alteration and climate impact.

While the present paper provides valuable insights into the impacts of LC on landscapes, it is essential to acknowledge certain limitations. One restriction was our sample size, which may have limited the strength of our results, notably for the analysis of treatment effect heterogeneity. As manual hedgerow mapping is a laborious task, automated methods of hedgerow identification with remote-sensed data and systematic classification could help expand the spatial coverage and temporal depth of our dataset (Scholefield et al., 2016). Machine learning algorithms can be particularly beneficial, using existing data as training or validation sets (Lucas et al., 2019). Another limitation of this study is that it focuses on the *net* dynamics of the hedgerow network. Specifically, available data cannot break down variations in hedgerow density and connectivity into gross losses and gains or into subsidized and induced uprooting. Such disaggregation would help better understand the mechanisms behind the net trends. Has hedgerow uprooting been partially offset through planting? Has LC caused hedgerow clearance beyond subsidized development works?

The findings from our study have relevant implications for policy. As this work displayed the negative impact of LC on the landscape *ex-post*, we first emphasize the need to anticipate these effects in decision-making. Environmental impact assessments are suitable tools, but developing credible counterfactual arguments appears to be critical for fair evaluation. Moreover, the decreasing impact of LC over the consolidation period plausibly supports the value of integrating local environmental expectations through strong environmental safeguards, such as qualified experts who advocate for landscape conservation and provide knowledgeable guidance (Husson and Marochini, 1997). However, since LC only explained a minor part of the total decline, systematic protection measures beyond LC are paramount to curtail the loss of semi-natural elements within farmlands. Mandatory retention of landscape features for subsidy eligibility in the European Common Agricultural Policy is a move in this direction, although its effectiveness depends upon its application conditions (Magnin, 2021). In future LC initiatives, a clear articulation of consolidation regulations with
national environmental laws seems key to ensuring the additionality of good practices during consolidation while bringing a coherent rationale to address the unavoidable environmental impacts.

This paper calls for more thorough environmental evaluations of LC reforms using robust causal inference methods. Modern LC programs claim to incorporate environmental concerns into their goals and designs, but the reality of this assertion remains to be formally tested (Janečková Molnárová et al., 2023). Ultimately, integrating economic, social, and environmental outcomes into a unified evaluation framework will enhance our understanding of how LC, as part of a land tenure system, advances sustainable rural development. Conversely, because our study demonstrates that LC is a limited contributing factor in hedgerow decline, further research is required to investigate other factors influencing farmers' decisions to maintain or remove landscape elements, including market dynamics, public policies, and social interactions (Plieninger et al., 2016). Leveraging more detailed data and advanced mapping technologies could facilitate this effort through more thorough measurement of landscape elements' spatial and temporal patterns. Addressing these challenges, which go beyond the scope of this study, will offer valuable knowledge for more sustainable land management, with policies that balance agricultural productivity and environmental protection.

**CRediT authorship contribution statement**

**Valentin Cocco:** Writing – original draft, Visualization, Validation, Software, Methodology, Investigation, Formal analysis, Data curation, Conceptualization. **Raja Chakir:** Writing – review & editing, Supervision, Resources, Funding acquisition, Conceptualization. **Lauriane Mouysset:** Writing – review & editing, Supervision, Resources, Conceptualization.

**Declaration of generative AI and AI-assisted technologies in the writing process**

During the preparation of this work the authors used ChatGPT and Grammarly in order to improve the readability and language of the manuscript. After using this tool/service, the authors reviewed and edited the content as needed and take full responsibility for the content of the published article.

**Financial disclosure statement**

This work was supported by the French Research Agency within the CLAND project [ANR-16-CONV-0003] and the ACCLIMATE project [ANR-22-CE55-0004], as well as by the European Union's Horizon Europe Research and Innovation program within the LAMASUS project [101060423].

**Declaration of competing interest**

The authors declare the following financial interests/personal relationships which may be considered as potential competing interests:

Raja Chakir reports financial support was provided by French National Research Agency. Raja Chakir reports financial support was provided by Horizon Europe. If there are other authors, they declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

**Acknowledgements**

The authors thank the editor Roger H. von Haefen and two anonymous referees for their constructive feedback throughout the revision process. They are very grateful to François Bareille, Yannick Guyonvarch and Clément Nedoncelle for their helpful insights and suggestions. We also thank the participants at the seminars at CIRED, PSAE, and the LAMASUS working group, as well as at the IPWSD workshop and the AES, EENR, and EAERE conferences. Finally, we are grateful to DREAL Normandie, Marc-André Philippe and Nadine Polombo for producing and sharing their data on hedgerows and consolidation. The authors are solely responsible for any omissions or deficiencies.

**Appendix A. Supplementary data**

Supplementary data to this article can be found online at doi:10.1016/j.jeem.2025.103196.

**Data availability**

Code and data to replicate the paper are available on a dataverse (https://doi.org/10.57745/MXDUTQ).

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