How Peace Treaties Can Influence Countries’ Climate Policies

Abstract

Peace agreements increasingly contain environmental provisions, yet we know little about the impact these clauses might have. This study is the first to systematically assess whether and how environmental terms in peace treaties can influence states’ climate policies. I argue that peace accords help establish the foundations for legalization and the rule of law in post-conflict societies, which facilitates that environmental terms lead to actual legislation. I analyze over 2,000 peace agreements and thousands of climate mitigation policies from 1990 to 2024, using multiple estimation strategies. The findings show that peace agreements’ environmental provisions are associated with the adoption of climate policies in post-conflict periods. This relationship holds even when exploring environmental outcomes, accounting for countries’ self-selection into environmental terms, or considering moderating factors like institutional capacity. The study contributes to the literature on peacebuilding and environmental politics, offering new insights into how peace agreements can shape post-conflict settings.

Keywords

climate policy environmental terms peace agreements post-conflict society quantitative methods

Introduction

The “Belfast Agreement” between the United Kingdom and the Republic of Ireland includes several provisions related to air pollution, water quality, and waste management (see Ellis 2001; Nagle 2018). As it focuses on ending violence and the peace process in Northern Ireland, the fact that environmental clauses have been considered as well may come as a surprise. However, a growing number of peace agreements now incorporate environmental terms, including prominent cases such as the 2016 Colombian peace accord (see Salazar et al. 2021), the 2006 Darfur Peace Agreement (see De Juan 2015), or the 2005 Aceh Peace Agreement (see Green 2013).¹

Existing research indicates that the design of peace agreements is shaped by a strategic process (e.g., Druckman and Wagner 2019; Harbom et al. 2006; Hartzell et al. 2001; Hartzell and Hoddie 2015; Jarstad and Nilsson 2018; Joshi and Quinn 2015; Walter 1999, 2002; Werner and Yuen 2005), and considering environmental clauses in this context is unlikely to be an exception. The inclusion of specific provisions, including environmental ones, becomes more likely when negotiators view them as necessary. That is, treaty commitments are usually not costless, and they are only incorporated in the final agreement when their perceived benefits outweigh the associated trade-offs (Bakaki et al. 2025). While we thus know why environmental provisions can be included in peace treaties, despite their primary focus on peace, security, and post-conflict stability, we know relatively little about their impact. Do environmental clauses in peace treaties meaningfully shape the post-conflict context?

This study is the first to systematically assess whether and how environmental provisions in peace agreements can influence countries’ environmental legislation, with a focus on climate mitigation policies (see Nachtigall et al. 2024; Steinebach et al. 2024). I build on the widely accepted view that peace agreements are often legally binding and can serve as foundational legal frameworks in post-conflict societies (see Bell 2018; Bell and O’Rourke 2007; Reid 2021; Werner and Yuen 2005).² As such, the content of these treaties can affect policymakers’ authority, incentives, and decision-making flexibility (see Böhmelt and Butkutė 2018; Bueno de Mesquita et al. 2005). When environmental clauses are embedded within such “quasi-constitutional frameworks” (Bell and O’Rourke 2007), designed to establish the rule of law and promote legalization in post-conflict settings, they are more likely to translate into actual legislative outcomes, including the adoption of climate policies.

To test this argument empirically, I combine the Peace Agreements Database (Bell and Badanjak 2019), which includes 2,011 peace treaties from 1990 to 2024, with data from the Climate Policy Database (CPDB).³ After evaluating various data sources based on country and year coverage as well as data quality (Nachtigall et al. 2024; Steinebach et al. 2024), the CPDB seems to be the most suitable option, covering over 6,000 policies across 198 countries. I employ several estimation strategies, including two-way fixed effects models, the within-between estimator by Bell and Jones (2015), and a general error correction model (Tromborg 2014), to examine whether and how environmental provisions in peace agreements influence climate policy adoption. Additional analyses presented afterwards and several robustness checks in the Supporting Information (SI) confirm the core finding discussed below, and they provide further support for the theory, even when exploring environmental outcomes, accounting for countries’ self-selection into environmental terms, or considering moderating factors like institutional capacity.

This research contributes to the existing literature in several ways. First, it expands the scholarship on peace treaties, their design, and their effects in post-conflict societies (e.g., Druckman and Wagner 2019; Harbom et al. 2006; Hartzell et al. 2001; Hartzell and Hoddie 2015; Jarstad and Nilsson 2018; Joshi and Quinn 2015; Walter 1999, 2002; Werner and Yuen 2005). While previous studies largely focus on the “core” components of peace agreements, such as security guarantees and the promotion of post-conflict stability (see Badran 2014; Call 2012; Prorok and Cil 2022; Quinn et al. 2007; Werner 1999), environmental objectives have received little attention. This study addresses that gap by examining the impact of peace agreements’ environmental provisions on countries’ climate mitigation policies and, in additional analyses below as well as in the SI, also when taking into account several other confounding (moderating) factors.

Second, within the environmental peacebuilding literature, it is argued that environmental cooperation can foster broader collaboration and reduce the risk of conflict recurrence (e.g., Ide and Detges 2018; Dresse et al. 2019; Ide 2019; Ide et al. 2021; Johnson et al. 2021; von Uexkull and Buhaug 2021; Sändig et al. 2024; Kedem et al. 2024). The findings presented here suggest an additional mechanism by which peace and security can be advanced: if environmental provisions in peace agreements prompt climate policy action, governments are not only addressing one of the most urgent global challenges, climate change (see Cornell and Gupta 2019), but are also potentially reinforcing peace through proactive mitigation and adaptation measures. In this way, environmental clauses may contribute directly to sustaining post-conflict peace.

Third, this study contributes to the research on environmental policy adoption and implementation (e.g., Bättig and Bernauer 2009; Bernauer and Böhmelt 2013; Kammerlander and Schulze 2020, 2021; Lindvall and Karlsson 2024; Nachtigall et al. 2024; Steinebach et al. 2024). The wider literature has identified a broad range of factors shaping the likelihood of environmental policy outputs. For example, regime type features prominently in these studies, which highlight that more democratic countries tend to pursue more and more ambitious policy legislation (see Bättig and Bernauer 2009; Böhmelt et al. 2016). Other works point to the influence of, e.g., political leaders, electoral processes, interest group lobbying, and, of course, economic conditions (see Bernauer 2013). These and related factors are most likely more influential than peace agreements’ environmental provisions, and, to begin with, peace treaties are rarely viewed as instruments for addressing environmental challenges, given their traditional focus on conflict resolution and political stabilization (see Badran 2014; Call 2012; Prorok and Cil 2022; Quinn et al. 2007; Werner 1999). However, it may still be plausible that there is some kind of relationship and effect, which I address by examining whether and how environmental provisions in peace agreements are implemented, leading to legislative action in the post-conflict period. This positions peace treaties also as potential “promoters of environmental protection.”

Finally, I contribute to the growing body of research on the indirect effects and positive externalities of conflict management and resolution (e.g., Bakaki and Böhmelt 2021; Bove et al. 2022; Di Salvatore 2019). The broader, less direct implications of peace agreements beyond security and stability have only recently gained scholarly attention. My study seeks to advance the field by tracing a specific pathway from environmental provisions in peace accords to the adoption of climate policies – an area that is typically less obviously related to (the root causes of) political violence. In doing so, I seek to offer new insights into the wider benefits of effective peace processes.

Environmental Provisions in Peace Agreements and Climate Policy

I develop the argument linking environmental provisions in peace agreements to states implementing climate policies in three steps. First, following conflict, countries often face institutional collapse and must rebuild their political and legal systems. Peace agreements play a foundational role here, due to their potentially legally binding, quasi-constitutional nature. Second, the impact of peace treaties depends on design: broad, comprehensive treaties, which address environmental concerns, create credible legal commitments that can facilitate post-conflict reforms. Therefore, third, incorporating environmental provisions into peace agreements can increase the likelihood of governments adopting actual climate-mitigation policies.

After the end of civil conflicts, interstate wars, or other forms of political violence, whether within or between states, countries are often forced to reorganize their political and social landscapes. As DeMeritt et al. (2014: 347) note, conflict termination represents a “break-down in status-quo traditions, morals, customs, and community.” Pre-conflict institutions, rules, and regulations may no longer exist, do not apply, or are not enforceable anymore. In this socio-political vacuum, peace agreements can play a crucial role by laying the groundwork for legalization and the rule of law in post-conflict societies (Bell 2018; Bell and O’Rourke 2007; Reid 2021; Werner and Yuen 2005).

Since the end of the Cold War (Badran 2014), peace treaties have evolved beyond mere instruments for ending violence. They now serve as key mechanisms for establishing long-term, positive peace by shaping the structures, behavior, and legal frameworks of the post-conflict state. Peace treaties often attain “quasi-constitutional status” (Bell and O’Rourke 2007), are commonly legally binding (Badran 2014), and act as “road maps” for future institutions, norms, and practices (Reid 2021: 1226). As such, they not only impose legal constraints through binding provisions (see Bell 2018), but also create incentives for leaders and policymakers (see Böhmelt and Butkutė 2018; Bueno de Mesquita et al. 2005; see also Mattes and Savun 2010) that help steer societies toward sustainable peace.

The extent to which peace agreements shape post-conflict societies – and their overall effectiveness – significantly depends on their content and design (Reid 2021; see also Walter 1999, 2002; Werner and Yuen 2005; Harbom et al. 2006; Hartzell and Hoddie 2015; Joshi and Quinn 2015; Jarstad and Nilsson 2018; Druckman and Wagner 2019). Design variations are frequently strategic responses to specific cooperation challenges. When institutional design is aligned with the core issues that need to be addressed, peace agreements are more likely to be effective and durable (Hartzell and Hoddie 2015; Reid 2021). Such treaties are therefore not ad hoc documents, but strategically crafted instruments. As the primary legally binding frameworks in post-conflict settings, they not only offer strong and credible commitments to long-term peace and stability, but also promote political inclusivity and broader societal reform effectively (Joshi and Quinn 2015; Jarstad and Nilsson 2018; Thomas 2024; see also Bell 2018; Good 2025).

In this context, incorporating environmental provisions into peace agreements can be both beneficial and impactful – to the extent that governments implement these clauses and adopt climate mitigation policies in the post-conflict period. The scope and comprehensiveness of peace agreements significantly influence their effectiveness and the likelihood of sustained peace (Joshi and Quinn 2015, 2017). Agreements that leave key issues unaddressed might result in prolonged negotiations or renewed violence (Joshi and Quinn 2015). By contrast, broader agreements that address diverse policy areas, such as minority rights (Bell 2018; Good 2025; Reid 2021; Thomas 2024) or environmental issues (Bakaki et al. 2025), are better positioned to reduce conflict recurrence by resolving potential points of contention through binding commitments. This effectiveness is partly rooted in the legal authority (Badran 2014; Reid 2021) and quasi-constitutional nature of peace agreements (Bell and O’Rourke 2007), which makes them the foundational blueprints for post-conflict institutions, norms, and regulations (Reid 2021: 1226).

Ultimately, inclusive and wide-ranging agreements foster stronger commitment, compliance, and enforcement among signatories (Joshi and Quinn 2015; see also Fazal 2013, 2014).⁴ Conflict parties and their negotiators have a “greater willingness to comply with and enforce [such broad] treaties” (Joshi and Quinn 2015: 10). All this raises the benefits of including environmental provisions in peace agreements – and, in turn, their potential impact in the post-conflict context.

That is, for post-conflict societies, peace treaties might be able to offer a “window of opportunity” to institutionalize environmental governance (Badran 2014; Harrowell and Özerdem 2019), rebuild trust, and reinforce cooperation. If implemented effectively (Jarstad and Nilsson 2018; Joshi et al. 2017; Joshi and Quinn 2015, 2017), environmental provisions in peace agreements could contribute not only to ecological outcomes, but also to long-term peace and stability. Environmental clauses, when embedded within the legally binding and constitution-like structure of peace agreements, may serve as credible commitments that facilitate the development of climate policies in the post-conflict period. I thus argue that environmental clauses in peace treaties can significantly facilitate post-conflict climate policy adoption.

Research Design

To explore the relationship between environmental provisions in peace agreements and countries’ adoption of climate mitigation policies, I create a country-year-level data set based on more than 2,000 peace treaties between 1990 and 2024 as taken from the Peace Agreements Database (PA-X, Bell and Badanjak 2019). The unit of analysis is the country-year, since the dependent variable focuses on states’ policy outputs at that level. To enter the sample, countries must have been involved in a conflict (interstate and/or intrastate) in the past and concluded their hostilities via a peace agreement.⁵ Once a country is included in the data set (due to its peace agreement), it remains in it until the year 2024. States without a peace agreement since 1990 are not considered for the analysis. Ultimately, my final data set provides annual time-series cross-sectional information on post-conflict countries’ environmental provisions in peace agreements and their environmental policies in 1990–2024.

For the dependent variable, I require a proxy for environmental policies. In the end, I choose the logged number of climate mitigation policies adopted in a given year for each country. After considering different sources and evaluating them in terms of country coverage, years included, and quality (Nachtigall et al. 2024; Steinebach et al. 2024), I opted for the Climate Policy Database (CPDB).⁶ The CPDB comprises information on more than 6,000 policies in 198 countries. I consider all six sectors (agriculture, buildings, electricity, general, industry, and transport) that may be impacted by a policy and all nine policy instrument types (barrier removal, climate strategy, economic instruments, information and education, policy support, regulatory instruments, research and development, target, and voluntary approaches) in the data. According to the CPDB, “policy instruments provide a link between desired policy outcomes and implementation. The term refers to instruments used to put the policy in practice, e.g. subsidies or carbon pricing.”

Figure 1 plots the distribution of policies in my sample across sectors. About a quarter of all policies affect the whole economy (“General,” 25.75 percent), followed by “Buildings” (e.g., policies that target energy-use in buildings, 16.30 percent) and “Electricity and heat” (e.g., policies related to energy supply and enabling infrastructure, 16.30 percent). The lowest number of policies pertains to the sector “Agriculture and forestry” (e.g., policies to increase sustainable practice in agriculture and those targeting better forest management, 8.24 percent). Across these six sectors, there are nine different policy instruments with “Climate strategy” being arguably one of the most relevant types from a policy perspective. Instruments in this category capture formal and legally binding strategies (laws), climate change economy-wide strategies not enshrined in law and rather adopted through policy documents published by government agencies, and the creation of a dedicated institution to regulate and monitor climate change responses and strategies. However, despite its policy importance, only about 12 percent of the cases in my sample are coded as “Climate strategy.” My final dependent variable captures climate mitigation policy adoption as measured by the logged number of policies (across all sectors and instruments) implemented in a given country-year, it ranges between 0 and 3.97, has a mean of 0.41 (standard deviation of 0.69), and is covered by 2,688 observations.

Figure 1.

Overview of CPDB sectors. Note. Graph displays distribution of CPDB policies by sector. There are six sectors in total: Agriculture and forestry, Buildings, Electricity and heat, General, Industry, and Transport. Economy-wide policies are categorized as “General.” For a detailed discussion of the sectors, see the CPDB Codebook available at: https://tinyurl.com/bdhvyaww.

For the main estimations, I rely on three distinct modeling approaches to facilitate interpretation of the findings and to control for confounding factors, including temporal dependencies, comprehensively: two-way fixed effects linear regression, a within-between estimator, and a general error correction model. Two-way fixed effects models comprise binary variables for the spatial (countries) and temporal units (years) in the data, which control for unobserved, time-invariant unit-level influences as well as common temporal shocks. However, other estimation strategies may outperform fixed-effects models (see Imai and Kim 2021). I thus employ alternatives to address some of the limitations of two-way fixed effects models. On one hand, I present the results from Bell and Jones’s (2015) within-between random effects estimator. All explanatory variables enter this model as a time-demeaned, time-varying measure for within-country effects and a country-level average to capture between-country effects. This allows me to analyze both within and between effects of time-varying country-level variables. On the other hand, I examine temporally lagged effects and more short-term (contemporaneous) impacts when discussing general error correction models (De Boef and Keele 2008). This estimation approach addresses potential concerns about non-random error structures (Tromborg 2014) by expressing the outcome variable as changes rather than as levels. In addition, the explanatory variables are included in both their differenced and lagged forms, while country and year fixed effects are considered, too.

The main explanatory variable is based on the PA-X (Bell and Badanjak 2019) information on environmental clauses in peace agreements. Environmental provisions are defined by PA-X as any reference to the environment, pollution, environmental disasters, epidemics, ecology, food security, and governmental bodies associated with environmental affairs, parks, and wildlife management. Examining the treaty terms in more detail, they all have a “pro-environment” nature. In my sample of 2,011 agreements, environmental terms are included in about 11 percent of them. To convert the treaty-level information to the country-year unit of analysis, I have calculated the cumulative sum of the number of environmental provisions across treaties and over time. This measure captures the combined impact of peace agreements’ environmental provisions in the past. To illustrate the coding, consider Afghanistan: there were peace treaties in, e.g., 2006 and 2008, each of them had an environmental provision in the respective agreement. While the coding of this country’s environmental-provisions variable is therefore 1 in 2006 and 2007, it is raised to 2 in 2008 and keeps the coding until a new peace agreement with another set of environmental provisions is passed (in 2010 in this case). The final variable capturing the cumulative sum of peace treaties’ environmental provisions ranges between 0 and 34, has an average value of 4.46 (provisions per country-year), and is characterized by a standard deviation of 6.44. In additional analyses after discussing the main results and in the SI, I assess the robustness of my findings using different operationalizations of that variable.

I control for several other influences, which capture political and economic effects as derived from the literature on the Environmental Kuznets Curve (EKC; see Stern 2017). The EKC was originally developed to explain environmental outcomes rather than policies (see Grossman and Krueger 1995). However, the underlying rationale can be extended to my context, since policy adoption is a key driver of outcomes (e.g., Bättig and Bernauer 2009; Böhmelt et al. 2016; Bystrom et al. 2025; Kammerlander and Schulze 2020, 2021).⁷ First, I account for countries’ economic development and their size when including measures for GDP per capita (in current US dollars) and population. For either item, I source the data from the World Bank. More populous countries have an overall higher demand for energy and burning fossil fuels. The World Bank defines population as a country’s midyear total population, which counts all residents regardless of legal status or citizenship (except for refugees not permanently settled). GDP per capita is defined as the gross domestic product (GDP) – the sum of gross value added by all resident producers in the economy plus any product taxes and minus any subsidies not included in the value of the products – divided by midyear population. Both GDP per capita and the population variable are log-transformed to correct for their skewed distributions, and I control for a curvilinear impact of income by adding the squared term of GDP per capita.

Second, I control for globalization (e.g., Neumayer 2002) and regime type (e.g., Kammerlander and Schulze 2020, 2021). On one hand, given that countries more economically integrated in the global system might want to appeal to other states’ values (Blanton and Apodaca 2007), including environmental ones, I use the KOF Globalization Index (Dreher 2006). A higher value of this variable signifies a greater embeddedness in the global political, economic, and social network. On the other hand, I account for countries’ democracy scores, as higher levels of democracy have been associated with greater concern for environmental justice and the inclusion of related provisions (see, e.g., Gellers and Jeffords 2018). I operationalize regime type and the level of democracy via the electoral democracy index from the Varieties of Democracy (V-Dem) data set.

Empirical Results

Table 1 summarizes six two-way fixed effects estimations, which study within-country variation after removing the over-time variation that is common to all countries. I complement the table with two graphs on substantive quantities of interest (Figures 2 and 3, based on Model 4). In Models 1–2, I leave out the temporally lagged dependent variable due to concerns over Nickell (1981) bias. These two estimations differ, however, in the inclusion of the control variables: they are considered in Model 2, but not in Model 1. Models 3–4 follow the setup of Models 1–2 when it comes to the control items, but I now incorporate the temporally lagged dependent variable. Finally, Models 5–6 include the lagged dependent variable, and their specifications differ in in- or exclusion of the control variables, but all explanatory variables are temporally lagged by 1 year to avoid potential problems stemming from endogeneity and the temporal order of events.

Table 1.

Environmental provisions in peace agreements and national climate policy – main models

	Model 1	Model 2	Model 3	Model 4	Model 5	Model 6
Lagged dependent variable			0.340^***	0.329^***	0.333^***	0.314^***
Lagged dependent variable			(0.019)	(0.021)	(0.019)	(0.021)
Environmental provisions	0.012^***	0.016^***	0.007^***	0.010^***	0.006^**	0.009^***
Environmental provisions	(0.003)	(0.003)	(0.002)	(0.003)	(0.003)	(0.003)
Democracy		−0.003		0.005		0.045
Democracy		(0.004)		(0.110)		(0.111)
GDP per capita (ln)		−0.804^**		−0.633^*		−0.750^**
GDP per capita (ln)		(0.374)		(0.355)		(0.358)
GDP per capita (ln)²		0.060^***		0.046^**		0.052^**
GDP per capita (ln)²		(0.022)		(0.021)		(0.021)
Population (ln)		−0.334^***		−0.171		−0.084
Population (ln)		(0.120)		(0.114)		(0.114)
Globalization		−0.002		−0.001		0.005
Globalization		(0.004)		(0.004)		(0.004)
Constant	−0.040	7.856^***	−0.025	4.729^***	−0.086	3.600^**
Constant	(0.116)	(1.893)	(0.113)	(1.809)	(0.110)	(1.814)
Observations	2,688	2,239	2,685	2,237	2,591	2,239
Country fixed effects	Yes	Yes	Yes	Yes	Yes	Yes
Year fixed effects	Yes	Yes	Yes	Yes	Yes	Yes

Table entries are coefficients; standard errors reported in parentheses.

^*p < 0.10, ^**p < 0.05, ^***p < 0.01.

Figure 2.

Simulated effect of environmental provisions. Note. Graph displays distribution of simulated effect of Environmental Provisions (N = 1,000 simulations); dashed vertical line stands for mean value of the variable’s marginal effect (0.011); graph based on Model 4.

Figure 3.

Predicted values of climate policies (ln). Note. Graph displays predicted values of Climate Policies (ln) for given values of Environmental Provisions; dashed lines stand for 95 percent confidence intervals; graph based on Model 4.

The results in Table 1 – irrespective of which model, sample, or controls are used – are consistent with my theoretical expectation that environmental provisions in peace agreements lead to the adoption of environmental – specifically, climate change mitigation – policies. Across Models 1–6, the coefficient of Environmental Provisions is positively signed and statistically significant at conventional levels. This means that environmental clauses in peace treaties are indeed associated with countries’ climate policies in the post-conflict period. Table 1 highlights that a 10 percent increase in environmental provisions of peace agreements leads to a rise in the number of climate policies adopted by about 6–17 percent. This impact is both significant and substantively important when considering that the average number of climate policies adopted per country-year is around 1.5.

Briefly discussing the control variables, most of them are not related in significant or consistent ways to climate policy outputs in the post-conflict era. Population, for example, is linked to a negative effect in Model 2, but it becomes insignificant once the lagged dependent variable is taken into account (Model 4 and Model 6). Globalization and Democracy, moreover, consistently display statistically insignificant coefficients. GDP per capita, however, seems to exert a curvilinear effect on climate policies that mirrors a U-shaped relationship between income and policy adoption (see Stern 2017): fewer climate policies are adopted with increasing income, but only until a tipping point has been reached. After that tipping point, we observe a higher rate of climate policy outputs. In Model 4, for example, that tipping point is at around $1,100 of GDP per capita. Subscribing to the link between policy outputs and environmental outcomes (e.g., Bättig and Bernauer 2009; Böhmelt et al. 2016; Bystrom et al. 2025; Kammerlander and Schulze 2020, 2021), the U-shaped pattern is in line with the original EKC finding that environmental quality first deteriorates with higher income – but only until a tipping point. Afterwards, environmental quality improves as pollution declines.

Figures 2 and 3 summarize substantive quantities of interest pertaining to the effect of Environmental Provisions, which also provide empirical support for my argument that such clauses can prompt post-conflict environmental legislation. First, I simulate the effect of Environmental Provisions 1,000 times (see King et al. 2000). According to Figure 2, the simulations based on Model 4 give us an average marginal effect of 0.011 – an increase of about 12 percent in climate mitigation policies for a rise of 10 percent in Environmental Provisions, which is virtually identical to the coefficient estimates in Table 1. Moreover, out of these 1,000 simulations, there is not a single simulation that has a marginal-effect estimate smaller than or equal to 0. Hence, there is robust evidence emphasizing that the relationship between environmental provisions in peace treaties and climate policy outputs is, in fact, positive and statistically significant.

Figure 3 shows the predicted values of (the logged number of) climate change mitigation policies for the range of Environmental Provisions. When there are no such clauses, my main model predicts that, on average, 1.7 climate policies would be adopted in the post-conflict period (i.e., Climate Policies (ln) has a predicted value of around 0.5). As the number of environmental provisions increases, so does the predicted value of climate policies, though. The point estimate is raised to more than two policies (i.e., Climate Policies (ln) has a predicted value of around 0.8) when the number of environmental terms approaches its sample maximum (34). In sum, Table 1, Figures 2 and 3 consistently and robustly point to the conclusion that environmental provisions in peace agreements are strongly linked to climate-change related legislation in the post-conflict context. The identified effect is both statistically significant and substantially important. Having said that, while the models in Table 1 are qualitatively similar when it comes to the main effect, the two-way fixed effects models provide conservative estimates and cannot assess the between-country effects.

I thus combine the first set of results with Table 2, which presents the findings of within-between random effects models (Bell and Jones 2015). These estimations consider the item on environmental provisions and any other explanatory variable both in their time-varying (demeaned) form and as an over-time country-average. The former captures the within-country effect, while the latter estimates the between-country impact. I obtain evidence for a within-effect, but not a between-effect. The size of the within-effect is equally strongly pronounced as in Models 1–6. This is not surprising, though, as the within-country-level in Models 7–8 corresponds to the two-way fixed effects models of Table 1. In total, the within-between random effects model produces results that are consistent with the findings presented in Table 1: environmental provisions in peace agreements are associated with post-conflict climate change mitigation policies. But as shown in Table 2, jointly modelling between-country and within-country effects is not essential in this case.

Table 2.

Environmental provisions and national climate policy – within-between estimation

	Model 7	Model 8
Lagged dependent variable (between)	1.009^***	1.007^***
Lagged dependent variable (between)	(0.014)	(0.031)
Lagged dependent variable (within)	0.348^***	0.335^***
Lagged dependent variable (within)	(0.018)	(0.021)
Environmental provisions (between)	0.001	0.001
Environmental provisions (between)	(0.001)	(0.002)
Environmental provisions (within)	0.009^***	0.011^***
Environmental provisions (within)	(0.002)	(0.003)
Democracy (between)		−0.012
Democracy (between)		(0.056)
Democracy (within)		0.001
Democracy (within)		(0.109)
GDP per capita (ln) (between)		0.068
GDP per capita (ln) (between)		(0.109)
GDP per capita (ln) (within)		−0.981^***
GDP per capita (ln) (within)		(0.328)
GDP per capita (ln)² (between)		−0.004
GDP per capita (ln)² (between)		(0.006)
GDP per capita (ln)² (within)		0.068^***
GDP per capita (ln)² (within)		(0.019)
Population (ln) (between)		0.006
Population (ln) (between)		(0.009)
Population (ln) (within)		0.002
Population (ln) (within)		(0.093)
Globalization (between)		−0.001
Globalization (between)		(0.002)
Globalization (within)		0.005^*
Globalization (within)		(0.003)
Constant	−0.216^*	−0.514
Constant	(0.111)	(0.491)
Observations	2,685	2,237
Country fixed effects	Yes	Yes
Year fixed effects	Yes	Yes

Table entries are coefficients; standard errors reported in parentheses.

^*p < 0.10, ^**p < 0.05, ^***p < 0.01.

Table 3 presents two general error correction models, which temporally disaggregate the impact of environmental provisions to distinguish between short-term and long-term effects. To capture the former, I introduce the first-difference term of Environmental Provisions, i.e., this variable pertains to inter-annual shifts in the number of peace agreements’ environmental provisions. To measure the latter, I employ the 1-year lagged variable of environmental clauses (as in Models 5–6 above). All other explanatory variables are also introduced as first differences and 1-year lags. The dependent variable captures shifts, and I include a lagged dependent variable in the estimations.

Table 3.

Environmental provisions and national climate policy – general error correction models

	Model 9	Model 10
Lagged dependent variable	−0.669^***	−0.683^***
Lagged dependent variable	(0.019)	(0.022)
Δ Environmental provisions	0.050^*	0.058^*
Δ Environmental provisions	(0.026)	(0.030)
Environmental provisions_t–1	0.006^**	0.009^***
Environmental provisions_t–1	(0.003)	(0.003)
Δ Democracy		−0.144
Δ Democracy		(0.234)
Democracy_t–1		0.063
Democracy_t–1		(0.122)
Δ GDP per capita (ln)		0.076
Δ GDP per capita (ln)		(0.165)
Δ GDP per capita (ln)²		0.318
Δ GDP per capita (ln)²		(0.473)
GDP per capita (ln)_t–1		−0.687^*
GDP per capita (ln)_t–1		(0.376)
GDP per capita (ln)²_t–1		0.050^**
GDP per capita (ln)²_t–1		(0.022)
Δ Population (ln)		−0.735
Δ Population (ln)		(0.886)
Population (ln)_t–1		−0.117
Population (ln)_t–1		(0.122)
Δ Globalization		−0.018^**
Δ Globalization		(0.008)
Globalization_t–1		0.001
Globalization_t–1		(0.004)
Constant	−0.103	3.859^**
Constant	(0.110)	(1.925)
Observations	2,591	2,149
Country fixed effects	Yes	Yes
Year fixed effects	Yes	Yes

Table entries are coefficients; standard errors reported in parentheses.

^*p < 0.10, ^**p < 0.05, ^***p < 0.01.

According to Models 9 and 10, I obtain evidence for a long-term and a short-term (contemporaneous) effect. The long-term effect is estimated at 0.006–0.009 (statistically significant at the 1–5 percent level). The short-term effect is calculated at 0.050–0.058, though statistically significant at the 10 percent level only. Annual changes in the number of environmental provisions thus have an immediate effect on whether governments pass climate mitigation legislation, and increases in the number of environmental clauses are associated with more climate policies in the long run. Figure 4 leverages the dynamics of the error correction models to plot the effects of peace agreements’ environmental revisions on climate policy over time. Model 10 provides the baseline for the calculations to display a forecast of climate policy action when the environmental provisions item is increased by one unit. As predicted, having one more environmental provision from a peace agreement increases the values of Climate Policies (ln) steadily over time when comparing this to a scenario of no environmental provisions. However, in the short-run, the differences are rather weakly pronounced; only in the long-run, after about 3 years into the simulation, does the difference between no environmental provisions and one such clause materialize in significant and substantive ways. This result connects back to the discussion of the distribution of policies across sectors (Figure 1 above): almost a third of all policies pertains to sectors that require larger investments or infrastructure development: “Buildings” and “Electricity and heat.” In turn, it likely takes more time for an effect to materialize.

Figure 4.

Dynamic simulation of climate policies. Note. Graph displays expected values of Climate Policies (ln) when Environmental Provisions is set to 0 and 1. Estimates are based on Model 10 (N = 1,000 simulations). Vertical bars report 95 percent confidence intervals.

Putting the results across all tables and figures together, I obtain strong and robust evidence for the argument: fixed-effects models, within-between estimator, and general error correction models all suggest that peace agreements’ environmental clauses tend to be implemented successfully as they are systematically associated with climate change mitigation policies in the post-conflict era.

Additional Empirical Analyses

For the main analyses above, the dependent variable considers all six sectors (agriculture, buildings, electricity, general, industry, and transport) and all nine policy instrument types (barrier removal, climate strategy, economic instruments, information and education, policy support, regulatory instruments, research and development, target, and voluntary approaches) in the CPDB data. For Table 4, I re-estimate the main model (Model 4 above) with a more narrowly defined operationalization for climate policies. That is, I only code a case as a form of policy output when the policy instrument is classified as “climate strategy.” In all other cases, I do not consider a CPDB policy for the estimation. While the overall effect somewhat decreases in Model 11, the effect of Environmental Provisions remains positively signed and statistically significant.

Table 4.

Environmental provisions in peace agreements – narrowly defined climate policy

	Model 11
Lagged dependent variable	−0.045^***
Lagged dependent variable	(0.015)
Environmental provisions	0.004^***
Environmental provisions	(0.001)
Democracy	−0.006
Democracy	(0.039)
GDP per capita (ln)	0.055
GDP per capita (ln)	(0.126)
GDP per capita (ln)²	−0.002
GDP per capita (ln)²	(0.007)
Population (ln)	−0.112^***
Population (ln)	(0.040)
Globalization	−0.001
Globalization	(0.001)
Constant	1.531^**
Constant	(0.641)
Observations	2,237
Country fixed effects	Yes
Year fixed effects	Yes

Table entries are coefficients; standard errors reported in parentheses.

^*p < 0.10, ^**p < 0.05, ^***p < 0.01.

I also consider capturing climate outcomes (rather than outputs). The argument I develop is strongly based on implementation. If environmental clauses are not (fully or effectively) implemented in a post-conflict society, we would hardly see that climate policies are being passed in turn; and we would not observe significant improvements in environmental quality. I thus re-estimate the main model, but now use carbon dioxide (CO₂) emissions measured in metric tons per capita, as provided by the World Development Indicators. CO₂ emissions, as captured by this data set, stem from the burning of fossil fuels and the manufacture of cement. They include carbon dioxide produced during consumption of solid, liquid, and gas fuels, and gas flaring. The World Development Indicators’ data were originally compiled by the World Resources Institute. I use the logged version of this variable. According to Model 12, I find support for a negative impact on CO₂ emissions and, thus, a positive impact on environmental quality. A (long-term) implication of my theory is that peace treaties can be, via their environmental provisions, “promoters of environmental protection.” The analysis in Table 5 does lend empirical support to this notion.

Table 5.

CO₂ emissions

	Model 12
Lagged dependent variable	0.798^***
Lagged dependent variable	(0.009)
Environmental provisions	−0.001^**
Environmental provisions	(0.000)
Democracy	0.027^**
Democracy	(0.013)
GDP per capita (ln)	0.113^***
GDP per capita (ln)	(0.043)
GDP per capita (ln)²	−0.001
GDP per capita (ln)²	(0.003)
Population (ln)	0.009
Population (ln)	(0.014)
Globalization	0.003^***
Globalization	(0.000)
Constant	−0.980^***
Constant	(0.219)
Observations	2,131
Country fixed effects	Yes
Year fixed effects	Yes

Table entries are coefficients; standard errors reported in parentheses.

^*p < 0.10, ^**p < 0.05, ^***p < 0.01.

The main models focus on a homogenous treatment effect. However, given the literature on environmental politics and the implementation of policies (e.g., Kammerlander and Schulze 2021), I also consider a moderating influence: state capacity. State capacity is a key factor in addressing climate change (see Böhmelt et al. 2025): for any government to address climate change effectively, it must have sufficient capacity to draft and implement corresponding laws and regulations. The core argument developed above is related to this concept, too, when I refer to legal aspects and the rule of law. Considering this, state capacity relates to the government’s ability to implement its goals or policies. It concerns government performance to the extent that it comprises “material resources and organizational competencies internal to the state that exist independently of political decisions about how to deploy these capabilities” (Hanson and Sigman 2021: 1496). To capture state capacity empirically, I use the International Country Risk Guide’s (ICRG) Indicator of Quality of Government. This item is the average value of the ICRG variables “corruption,” “law and order,” and “bureaucracy quality,” scaled from 0 to 1. Higher values indicate higher quality of government. To account for the item’s skewed distribution and to facilitate interpretation, the final variable State Capacity is a binary item, which receives a value of 1 if government quality is larger than the median value of the ICRG variable (0 otherwise).

According to Model 13 (Table 6), there is little evidence for a moderating influence as the interaction term Environmental Provisions × State Capacity is statistically insignificant, though the constituent term of Environmental Provisions continues to be positively signed and significant at conventional levels (this scenario captures State Capacity being set to 0). Having said that, Model 14 estimates the parameters for a model on environmental outcomes as I use the carbon-dioxide item from Model 12 as the dependent variable. In that model, Environmental Provisions × State Capacity is negatively signed and significant at the 1 percent level, highlighting that state capacity can matter – but arguably more so for the long-term impact for outcome measures (Table 6).

Table 6.

State capacity as a moderating influence

	Model 13	Model 14
	Policy	Emissions
Lagged dependent variable	0.305^***	0.823^***
Lagged dependent variable	(0.023)	(0.011)
Environmental provisions	0.009^***	0.000
Environmental provisions	(0.003)	(0.000)
State capacity	0.038	−0.002
State capacity	(0.051)	(0.005)
Environmental provisions × State capacity	−0.002	−0.001^***
Environmental provisions × State capacity	(0.004)	(0.000)
Democracy	−0.052	0.004
Democracy	(0.133)	(0.014)
GDP per capita (ln)	−0.337	0.092^**
GDP per capita (ln)	(0.444)	(0.046)
GDP per capita (ln)²	0.033	−0.001
GDP per capita (ln)²	(0.026)	(0.003)
Population (ln)	−0.335^**	0.030^**
Population (ln)	(0.137)	(0.014)
Globalization	0.007	0.002^***
Globalization	(0.005)	(0.000)
Constant	5.669^**	−1.164^***
Constant	(2.290)	(0.239)
Observations	1,846	1,759
Country fixed effects	Yes	Yes
Year fixed effects	Yes	Yes

Table entries are coefficients; standard errors reported in parentheses.

^*p < 0.10, ^**p < 0.05, ^***p < 0.01.

The inclusion – and, hence, the impact on climate policies – of environmental provisions in peace treaties might differ across intrastate and interstate conflicts. Domestic-level, i.e., intrastate conflicts tend to focus on one domestic setting, potentially involve fewer state actors (as it is the government fighting against non-state actors), and thus allow for more interference in domestic politics. However, another strand exists in the literature on interstate conflict and cooperation over transboundary environmental issues, which implies that environmental clauses in international peace treaties could also shape domestic-level policy legislation (e.g., Ide and Detges 2018). I consider a binary variable Civil Conflict on whether a dispute is at the intrastate level (1) as opposed to the interstate level (0). The definition follows the Uppsala Conflict Data Program (UCDP). In Model 15, I add this variable as a control. In Model 16, I use the variable to restrict the sample to intrastate conflict contexts only, i.e., I omit peace processes based on international-level, interstate conflict. Having said that, while the item Civil Conflict is insignificant in Model 15, the effect of Environmental Provisions is virtually unaffected in either the first or the second model of Table 7. I thus conclude that although peace processes may differ across interstate and intrastate wars, this distinction is less relevant in my context of how environmental provisions affect post-conflict climate policies. But it is interesting to see that the sample is indeed mostly about civil conflicts (domestic warfare) and interstate conflicts are addressed in the minority of cases only. But this reflects the actual distribution of domestic-level conflicts and fighting between states in the post-World War II era. Either way, the results are not driven by interstate outliers.

Table 7.

Considering civil conflict

	Model 15	Model 16
Lagged dependent variable	0.334^***	0.337^***
Lagged dependent variable	(0.021)	(0.022)
Environmental provisions	0.010^***	0.012^***
Environmental provisions	(0.003)	(0.003)
Civil conflict	0.041
Civil conflict	(0.057)
Democracy	0.006	0.002
Democracy	(0.112)	(0.115)
GDP per capita (ln)	−0.621^*	−0.547
GDP per capita (ln)	(0.357)	(0.363)
GDP per capita (ln)²	0.046^**	0.042^*
GDP per capita (ln)²	(0.021)	(0.022)
Population (ln)	−0.137	−0.183
Population (ln)	(0.116)	(0.120)
Globalization	0.000	0.000
Globalization	(0.004)	(0.004)
Constant	4.029^**	4.491^**
Constant	(1.844)	(1.896)
Observations	2,172	2,031
Country fixed effects	Yes	Yes
Year fixed effects	Yes	Yes

Table entries are coefficients; standard errors reported in parentheses.

^*p < 0.10, ^**p < 0.05, ^***p < 0.01.

I also consider additional confounding factors. All of these are included as multiplicative interactions in the models of Table 8, which allows me to capture their moderating impact on the relationship between environmental provisions and post-conflict legislation. First, in Model 17, I incorporate a variable on foreign aid. The international community often plays a pivotal role in pressuring states to adopt environmental reforms by leveraging diplomatic influence, economic incentives, and public opinion. International organizations, donor countries, and advocacy groups can condition development assistance on the implementation of environmental measures. Furthermore, global norms and agreements, such as those established by the UN, create expectations for states to prioritize environmental sustainability, especially in post-conflict settings where rebuilding efforts provide an opportunity to integrate such reforms. Countries that receive foreign aid might be encouraged to advance environmental protection and thus include environmental provisions in peace agreements and seek to implement them in the form of legislation. To account for these mechanisms, I include a variable capturing the net official development aid received from OECD Development Assistance Committee (DAC) members (log-transformed).

Table 8.

Additional confounding factors

	Model 17	Model 18	Model 19
Lagged dependent variable	0.324^***	0.329^***	0.327^***
Lagged dependent variable	(0.021)	(0.021)	(0.021)
Environmental provisions	−0.011	0.013^***	0.015^***
Environmental provisions	(0.012)	(0.003)	(0.003)
Foreign aid	−0.035^***
Foreign aid	(0.012)
Environmental provisions × Foreign aid	0.002^*
Environmental provisions × Foreign aid	(0.001)
UN		−0.007
UN		(0.036)
Environmental provisions × UN		−0.009^*
Environmental provisions × UN		(0.005)
External power			0.036
External power			(0.034)
Environmental provisions × External power			−0.013^***
Environmental provisions × External power			(0.004)
Democracy	0.042	0.002	0.028
Democracy	(0.111)	(0.114)	(0.112)
GDP per capita (ln)	−0.591	−0.611^*	−0.607^*
GDP per capita (ln)	(0.364)	(0.357)	(0.356)
GDP per capita (ln)²	0.043^**	0.044^*	0.043^**
GDP per capita (ln)²	(0.022)	(0.021)	(0.021)
Population (ln)	−0.206^*	−0.125	−0.178
Population (ln)	(0.114)	(0.116)	(0.116)
Globalization	−0.001	0.001	0.001
Globalization	(0.004)	(0.004)	(0.004)
Constant	5.468^***	3.844^**	4.747^**
Constant	(1.862)	(1.8)	(1.847)
Observations	2,237	2,172	2,172
Country fixed effects	Yes	Yes	Yes
Year fixed effects	Yes	Yes	Yes

Table entries are coefficients; standard errors reported in parentheses.

^*p < 0.10, ^**p < 0.05, ^***p < 0.01.

Second, in Model 18, I control for the involvement of the UN. Derived from the previous point, several peace agreements are supported by the UN, which increasingly emphasizes environmental provisions as part of post-conflict peacebuilding (see, e.g., Ide et al. 2021; Böhmelt 2024a, 2024b). The inclusion of such provisions stems from the organization’s general policies, e.g., the UN Sustainable Development Goals, and they can improve the legitimacy of a peace agreement by attracting international support (Streich and Mislan 2014). I thus create a binary variable that is coded as 1 when the UN were formally involved in the peace process. This measure is taken from the PA-X data.

Third, Model 19 comprises the binary variable External Power. Intrastate and interstate peace processes can attract global attention, often involving humanitarian aid from influential individual countries. In exchange for aid, donors may push for specific reforms and policies that are consistent with their strategic interests (Hannigan 2013; Streich and Mislan 2014). To control for an effect along those lines, I created a dichotomous item on major powers’ involvement in peacebuilding, including the US, Russia, or the EU. The variable is also based on the PA-X data set.

According to the estimates in Table 8, all three additional variables exert a moderating influence. First, the interaction with Foreign Aid is positive and statistically significant, suggesting that outside development assistance does indeed enhance the positive impact of environmental clauses in peace treaties on climate policy outputs. Second, UN involvement has the opposite impact: only when the UN is engaged in a peace process does the variable Environmental Provisions exert a positive effect that is significant at the 1 percent level. Finally, a similar effect is estimated for external major powers. In sum, these results lead to a two-fold conclusion. On one hand, external influence can be helpful for environmental provisions to lead to climate policy legislation, but only when it is less “active” (UN or major powers) and more “passive” (foreign aid). On the other hand, the unconditional effect does essentially prevail, too, as demonstrated by the positive and significant estimates of Environmental Provisions when setting the added binary variables UN and External Power to 0.

In the SI, I present additional analyses, and the results from these continue to support the main findings presented above. First, Table A1 concentrates on a binary version of the environmental-provisions item. Table A2 is based on a different estimation approach that seeks to uncover a causal effect of environmental provisions. In Table A3, I examine the time elapsed since the last environmental provision in a peace agreement. A Heckman selection model is used in Table A4 to control for states’ self-selection into environmental provisions in peace agreements. I also examine the influence of government ideology (Table A5) and democracy (Table A6) as moderating influences. In Table A7, I concentrate on conflict recurrence.

Conclusion

This study offers significant insights into whether and how environmental provisions in peace agreements can influence environmental policymaking in post-conflict societies. By analyzing the PA-X data (Bell and Badanjak 2019) and applying rigorous empirical methods, I provide consistent evidence that such provisions are both significantly and substantively associated with the adoption of climate policies. This finding contributes to multiple strands of scholarship, including the design and impact of peace agreements, environmental peacebuilding and politics, and the broader fields of conflict management and post-conflict governance.

While peace treaties have historically prioritized political reconciliation, security guarantees, and institutional restructuring (e.g., Harbom et al. 2006; Walter 1999, 2002; Werner and Yuen 2005), this study shows they can also advance “pro-environment” policies. Peace agreements, traditionally viewed as mechanisms for ending violence and establishing political order (e.g., Badran 2014; Harbom et al. 2006; Joshi and Quinn 2015, 2017; Walter 1999, 2002; Werner and Yuen 2005), also have the capacity to function as legal frameworks that integrate environmental considerations.

One of the key conclusions from this analysis is that peace treaties, by virtue of their legally binding and often quasi-constitutional nature (Bell and O’Rourke 2007), can serve as enduring frameworks for environmental governance. Of course, more environmental provisions are not always “better” or will lead to improved environmental legislation under all circumstances. But they implication of this research is that their inclusion – versus exclusion – creates important opportunities for embedding environmental concerns and implementing environmental policies in post-conflict reconstruction. This underscores the need for policymakers and international organizations to recognize the untapped potential of peace agreements as tools for environmental protection, which is especially pertinent in light of growing recognition that environmental issues can be both drivers and consequences of violent conflict (Bernauer et al. 2012; Buhaug et al. 2014; Jiang et al. 2025; Koubi 2019; Xie et al. 2024).

In fact, since environmental degradation can exacerbate tensions and contribute to instability, integrating environmental clauses in peace agreements may offer a forward-looking approach to conflict prevention. This aligns with the environmental peacebuilding literature (e.g., Ide and Detges 2018; Ide 2019; Ide et al. 2021, 2023; von Uexkull and Buhaug 2021; Balinskaia 2025; see also Bakaki and Böhmelt 2021; Böhmelt 2024a, 2024b), which suggests that environmental cooperation can serve as a confidence-building measure and facilitate post-conflict recovery.

My research opens several avenues for future research. First, scholars could further investigate the causal mechanisms linking environmental provisions in peace agreements to specific policies and their outcomes. Second, there is room to explore variation across contexts: under what conditions are environmental provisions most likely to be adopted, and when do they fail to take root? In this context, I have explored the influence of state capacity (above) and political ideology (in the SI), but other factors may well matter, too. Third, the environment-conflict nexus itself deserves more attention. While a growing body of research points to the potential for environmental change to contribute to conflict (Bernauer et al. 2012; Buhaug et al. 2014; Jiang et al. 2025; Koubi 2019; Xie et al. 2024), this relationship may be mitigated by climate adaptation and mitigation policies (Mach et al. 2019; Pearson and Newman 2019). If peace agreements facilitate the institutionalization of such policies through environmental provisions, they could directly reduce the structural drivers of future conflict.

Finally, I raise important normative questions about the role of environmental considerations in peace negotiations. While I argue that the inclusion of environmental provisions can be beneficial, it is important to consider potential trade-offs. For instance, do environmental commitments create accountability or distribution challenges in post-conflict governance? Addressing these questions could offer deeper insight into how environmental clauses operate across different political, ecological, and institutional landscapes.

In conclusion, I have sought to contribute to a growing body of literature at the intersection of peace agreements and environmental governance. I have provided empirical evidence that environmental provisions in peace treaties are substantively linked to the adoption of climate policies, and I point toward the broader implications for sustainable peacebuilding. By emphasizing the transformative potential of peace agreements, not only as tools for ending violence, but also for promoting ecological sustainability, I sought to offer valuable insights for scholars, practitioners, and policymakers working at the intersection of conflict resolution and environmental politics. Peace agreements, when thoughtfully designed, can serve as foundational platforms for advancing peace and environmental resilience in post-conflict societies.

Supplemental Material

Supplemental Material - How Peace Treaties Can Influence Countries’ Climate Policies

Supplemental Material for How Peace Treaties Can Influence Countries’ Climate Policies by Tobias Böhmelt in Journal of Conflict Resolution.

Supplemental Material

Supplemental Material - How Peace Treaties Can Influence Countries’ Climate Policies

Supplemental Material for How Peace Treaties Can Influence Countries’ Climate Policies by Tobias Böhmelt in Journal of Conflict Resolution.

Footnotes

ORCID iD

Tobias Böhmelt

Funding

The author disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was supported by the British Academy/Leverhulme Trust SRG25\250220.

Declaration of Conflicting Interests

The author declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Data Availability Statement

The data and replication materials used for this article are available at the Harvard Dataverse (https://doi.org/10.7910/DVN/ZG1NRA), . I thank the editor and the anonymous reviewers for their constructive feedback.

Supplemental Material

Supplemental material for this article is available online.

Notes

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