Climate change impact assessment: the role of institutional variables

Climate change impact assessment: the role of institutional variables | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Climate change impact assessment: the role of institutional variables Encarna Esteban, Yolanda Martínez, Sara Calvo This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7150109/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract The climate crisis represents one of humanity’s greatest challenges. The world’s GDP could shrink by as much as 14% by the mid-21st century, particularly affecting the agriculture sector, which is among the most vulnerable. To effectively mitigate climate impacts, assessing them from an economic perspective and examining how institutions and political systems influence societal and economic adaptability is essential. Ricardian models (Mendelson et al., 1994), which incorporate many climatic, physical, and socio-economic variables, have been used to evaluate the impacts of climate change on agriculture. However, these analyses have traditionally excluded political and/or institutional variables. This field requires a comprehensive study on how these variables shape the future impacts of climate change and how they affect the ability to mitigate and adapt to those impacts. Along this paper we document the systematic weaknesses in how existing models treat institutional variables, drawing on examples from both developed and developing country contexts. Our results demonstrate how improved treatment of institutions can enhance both the explanatory power of Ricardian models and their relevance for climate policy design. Ricardian models climate change agriculture institutional variables political variables Figures Figure 1 Figure 2 Figure 3 Figure 4 1. Introduction Climate change has emerged as one of the most urgent environmental challenges of the 21st century. Its negative consequences are escalating around the world, including rising sea levels, extreme temperature fluctuations, increased variability in daily weather conditions, changes in precipitation and humidity patterns, evapotranspiration rates and a higher incidence and frequency of extreme weather events (Baylie and Fogarassy, 2021; IPCC, 2018). Estimates indicate that if environmental degradation continues at its current rate, global GDP could shrink by 14% by the middle of the century (Swiss Re Institute, 2021). Furthermore, the European Union reported that between 1980 and 2023, climate-related extreme events caused economic losses amounting to EUR 738 billion. This trend is expected to increase, as over EUR 162 billion (22%) of those losses occurred between 2021 and 2023 (European Environmental Agency, 2024). Agriculture is one of the most vulnerable economic activities, highly affected by climate change due to its dependence on weather, water, and natural resources. Approximately 27% of the world’s population relies directly or indirectly on farming for their livelihoods, a figure that is even higher in many developing countries (FAO, 2018). The various impacts of climate change highlight the urgent need to evaluate and quantify these effects. It is essential to assess the consequences of climate change in order to develop mitigation and adaptation policies, especially in the most vulnerable geographical areas and economic sectors (IPCC, 2022). Given the significance of farming and its vulnerability to climate change, many empirical studies have been conducted to evaluate the impacts on agricultural production and the possible future implications (see, for example, Mendelsohn et al, 1994; Abidoye et al, 2017; Massetti & Mendelsohn, 2020; Seo et al, 2005; Migliore et al, 2019). The Ricardian approach, as described by Mendelsohn et al. (1994) analyzes the impact of climate change on the value of farmland. This model is based on the hypothesis that farmers aim to maximize their profits, taking into account exogenous variables beyond their control and responding to the prevailing circumstances (Mendelsohn & Dinar, 2009). The models incorporate not only climatic variables but also physical and socio-economic factors to assess land value (Mendelsohn & Massetti, 2017). One major advantage of the Ricardian methodology is that it considers how farmers adapt; the models imply that they maximize their profits under all weather conditions and adjust their crops accordingly (Mendelsohn & Massetti, 2017; Su & Chen, 2022). Using econometric regressions, several studies have projected future economic impacts for various countries and regions based on forecasts of changing precipitation and temperature patterns (Mendelsohn et al, 1994; Seo et al, 2005; Mendelsohn et al, 2010; Fezzi & Bateman, 2015; Hossain et al, 2019; Ortiz-Bobea, 2020). Although the Ricardian approach is widely used, it has faced criticism due to its limitations. One key issue is the need to incorporate the impact of agricultural policies into the analysis (Kurukulasuriya & Mendelsohn, 2006). Despite recognizing the importance of social, economic and political variables, many studies often exclude them and rely only on a narrow range of traditional factors. Institutional variables, in particular, are significantly underrepresented. This lack of focus can lead to biased estimates and incomplete policy recommendations. There is increasing evidence that institutions play a crucial role in shaping both climate vulnerability and the capacity for adaptation. Unfortunately, only a handful of studies have included institutional variables –such as formal and informal rules, standards, and regulations- within their Ricardian analyses (Marquardt et al, 2023). Although incorporating these variables poses challenges due to the complexities of data collection, it is essential for effective analysis. The effectiveness of governmental and institutional frameworks is vital for successfully mitigating and adapting to the impacts of climate change (Gyimah et al, 2024). The objective of this paper is to highlight the gaps in institutional analysis within Ricardian models. By reviewing the literature on Ricardian modeling, this paper analyzes empirical studies that incorporate institutional variables, economic policy factors, regulatory elements, or cooperation and association variables among actors and institutions. We document the systematic weaknesses in how existing models address institutional variables, providing examples from both developed and developing countries. Our analysis shows that improving the treatment of institutions can enhance both the explanatory power of Ricardian models and their relevance for climate policy design. The findings indicate that only a limited number of studies included these types of variables into Ricardian modeling, highlighting a significant gap in integrating institutional and governance issues into climate change assessment. Furthermore, due to data difficulties in representing institutional variables, they have often been included as dummy variables, which creates a problem for interpreting them (not including elements such as institutional quality or frequency). Finally, the difficulty in measuring and defining institutions makes the comparison of the same variable challenging since it may include very diverse elements (e.g., what is understood by farmers' association or the facilities included in ‘extension services’). Overall, the outcomes suggest that the most commonly used variables pertain to actors’ associations or cooperation, subsidies, and public planning services (such as extension services). The results demonstrate that institutional variables generally have a positive impact across all models, suggesting that the presence of both formal and informal rules plays a relevant role in minimizing the impacts of climate change. However, a noteworthy result is that certain mechanisms (such as specific subsidies or the availability of environmental information) yield contradictory outcomes, suggesting they either have no effect or may even negatively influence efforts to mitigate the effects of climate change. In this regard, our conclusions indicate that while the development of institutions, regulations, and rules—both formal and informal—are important, the way in which coordination and policy design are carried out is crucial to avoid counterproductive outcomes. Our outcomes highlight the importance of strengthening institutions (from both formal and informal perspectives) as key elements in the design and performance of policies for mitigating and regulating climate change impacts, particularly in the agricultural sector. The paper is organized as follows. Section 2 presents the method and the analytical approach. Section 3 collects the results of the analysis. Section 4 presents the discussion and implications of the results, and finally, section 5 presents conclusions and suggestions for future work. 2. Methodology: systematic review To assess the role of how institutional variables influence the impacts of climate change on agriculture a systematic review is applied to ensure a transparent, reproducible, and comprehensive analysis. Our review follows the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines for conducting systematic reviews, which outline key procedures for study selection, data extraction, and synthesis (Page et al., 2021; Moher et al., 2009). The literature search was conducted using an academic database to identify studies that used Ricardian models to assess climate change impacts on agriculture. The database searched was Web of Science (WoS) and the search strategy was designed to capture all articles published in English. The literature was collected in April 2024 by searching for “ Ricardian climate change ”, “ Ricardian models ” AND “ climate change ” in the title, summary and/or keywords. Furthermore, references from key articles were revised to ensure no important studies were overlooked (snowball sampling). In total, we found 203 scientific articles published in English. We excluded working papers, book chapters and other studies (grey literature). Figure 1 shows the results of the systematic search in the WoS by strings and subjects and presents the complete study selection process based on PRISMA methodology (Moher et al., 2009). FIGURE 1 AROUND HERE Figure 1 illustrates the complete flow of the search and exclusion processes. After removing duplicate articles, non-English articles and documents not categorized as articles (e.g., book chapters, theses, working documents, etc.), we excluded and additional 15 inaccessible documents. This left us with a final review sample of 154 studies. Since we needed quantitative data to analyze the models and variables used, we also removed 16 theoretical articles and/or reviews from our selection. We also omitted some studies (20) that referenced Ricardian methodology as a relevant or benchmark theory but did not base their own methodology on it. Finally, 6 articles were excluded due to the lack of an economic model and/or data to replicate the study and analyze the variables. So, we end up with a sample of 112 research articles. 3. Bibliometric analysis For this study, we conducted and an in-depth literature review of all articles that meet out our eligibility criteria. This in-depth analysis aimed to highlight key trends, influential authors, and the development of research at the intersection of climate change, agriculture, and institutions. The appendix (Table A1) contains a complete list of the articles included in the analysis. Figure 2 illustrates the timeline of publications related to Ricardian models, beginning with the seminal paper by Mendelsohn et al. (1994), which was the first to apply a Ricardian model to analyze the impacts of climate change. The distribution of papers over time revealed a significant increase in the number of publications on Ricardian models in climate change research, especially from 2007 onward. This rise coincided with the publication of the IPCC’s fourth assessment report in 2007, which highlighted the critical need for action and regulation regarding the impacts of climate change (IPCC, 2007). Before 2007, only a limited number of studies used the Ricardian approach to explore climate change adaptation in agriculture, with most research focused primarily on basic model development. As shown in Figure 2, there were only 16 publications between 1994 and 2006. FIGURE 2 AROUND HERE Between 2007 and 2015, this field experienced rapid growth due to increasing awareness of climate change’s impact on agriculture and the adoption of advanced econometric techniques in Ricardian models (Amouzay and El Ghini, 2024). This period also marked the initial inclusion of institutional variables in Ricardian frameworks, reflecting the growing recognition of governance and socio-economic factors in climate adaptation. A notable spike in publications occurred in 2009 (16), coinciding with the United Nations Climate Change Conference (COP 15) held in Copenhagen, Denmark, where major greenhouse gas-emitting countries acknowledged climate change as a global issue for the first time (Dubash, 2010). However, despite this surge in scientific activity, the number of publications declined from 2010 to 2015. After 2015, there was a continuous increase in publications, particularly in studies examining institutional variables such as land tenure, governance, and infrastructure, in addition to the traditional climate variables featured in Ricardian models (Stern, 2007). From 2016 onwards, the number of publications notably increased almost every year. Recent impacts from undeniable changes in temperature and precipitation patterns worldwide, along with a higher frequency of extreme natural events like fires, droughts, and floods, have raised concerns in nearly all developed nations (Fabri et al., 2022). This shift underscores the growing recognition of the role that institutions play in shaping agricultural adaptation strategies. Over the past decade, research has become more regionally diverse, with an increasing volume of studies emerging from developing countries, where the impacts of climate change are expected to be most severe (Dell’Angelo et al., 2017). Another important finding pertains to the thematic areas of analysis in which research studies on Ricardian modeling were published. We categorized these studies based on the journal subject categories defined by the WoS (see Fig. 3). While the areas of specialization are quite diverse, most articles appeared in journals related to environmental sciences, meteorology and/or agriculture. A critical aspect of Ricardian models is the incorporation of climate variables in analyzing agricultural productivity. Early Ricardian models primarily focused on the direct effects of changes in temperature and precipitation on land rents or crop yields, and these studies were published in journals within these thematic categories. Over time, Ricardian approaches also examined other interactions, such as the frequency of extreme weather events and their compounded effects on agriculture (Prakash et al., 2024). Another major category is “business economics” which highlights the strong economic focus of these studies. A key contribution of the Ricardian literature is quantifying the impacts on agricultural land values, providing concrete estimates of the economic costs associated with climate change. Consequently, Ricardian models have played a significant role in the economic discussion of climate change These findings illustrate the growing interest from various disciplines in studying climate change and its impacts. However, these results also indicate that models incorporating a strong economic component tend to have less influence within economic fields (see relevant thematic areas in Fig. 3). This may explain the limited inclusion of socio-economic and, in particular, institutional variables in many of the Ricardian models that have been developed. FIGURE 3 AROUND HERE The geographic distribution of research papers provides valuable insights due to it highlights the regions most interested in studying and assessing climate change and it reveals that 66 of the 112 articles identified with empirical models were conducted in underdeveloped or developing countries. Early Ricardian models were particularly implemented in developed countries and especially in the United States. This trend can be attributed to the availability of robust data and information in high-income regions. However, the analysis rapidly expanded to include studies from low-income countries in Africa, Asia, and Latin America. In total, 66 articles were identified in these regions, especially in recent years. This geographic shift indicates a growing interest in assessing the costs of climate change in highly vulnerable regions, such as low and middle-income countries, which are more dependent on agriculture and may face more severe economic impacts. Additionally, this geographic shift underscores the increasing focus on understanding the role of institutions in shaping climate adaptation strategies in low- and middle-income countries. In these regions, institutional factors such as land tenure, governance, and infrastructure may significantly influence adaptation outcomes. To complement the geographic distribution analysis, Figure 4 also presents the first author’s affiliation for the identified studies[1]. This analysis highlights that the majority of the studies feature first authors affiliated with institutions in developed countries, particularly within universities in the United States and Europe. However, it is also important to note the growing contribution of studies from China, which ranks as the second country with the highest number of studies applying Ricardian models to assess climate change impacts. Other notable contributors include India, Pakistan, and South Africa. It can be observed a correlation between the number of studies and the affiliation of the authors, largely due to the data and resources required for the development and application of these models. FIGURE 4 AROUND HERE To conclude, the results of the bibliometric analysis reveal a growing interest in the assessment of climate change impacts, particularly over the past two decades. Furthermore, during last decades studies have increasingly focused on the impacts in low- and middle-income countries. Finally, one of the conclusions of this analysis is that such assessments remain concentrated in a limited group of countries, with many regions still lacking the application of these models. 4. Results and discussion 4.1. Definition of institutional variables Climate change impacts are influenced not only by environmental factors but also by other elements that are crucial for mitigating these effects. Institutional and governmental factors are vital for effectively addressing the negative consequences of climate change. As Guimah et al (2024) state, ‘ Strong and effective governance institutions are essential for ensuring that climate policies are well-designed, well-implemented, and responsive to changing circumstances .’. However, including institutional variables in Ricardian models remains an unresolved issue and represents a significant area for further research in this field. In this study, “institutions” refers to all levels of governance structures, land tenure systems, policy frameworks, and infrastructure. We adopt the definition of institutions proposed by Marquardt et al. (2023): “ the process of developing, changing and establishing new formal and informal rules and procedures in terms of politics, policies, and polity ”. While defining institutional variables presents significant challenges, we propose a classification of institutional characteristics, such as property rights security, stakeholders’ association, existence of subsidies or market efficiency between others, which have been incorporated into various models and can be compared across different contexts. In some cases, it has been complicated to isolate the effects of institutions, within Ricardian framework, due to the interconnected relationship between institutional development and economic outcomes. So, we have assumed that the presence of institutions facilitates access to essential resources such as credit, technology, and information, which can significantly improve agricultural adaptability to the impacts of climate change (Stern, 2007). Following the bibliometric analysis, a total of 112 selected articles were reviewed in detail to examine the variables considered in empirical Ricardian models. The identified variables from the various articles were categorized into five groups: 1) climatic variables; 2) physical variables; 3) economic variables; 4) social variables; and 5) institutional variables. It should be noted that the authors classified the variables based on their own criteria. In selecting the institutional variables, we focused on three main issues (Gyimah et al., 2024): variables related to financial and/or technical support that may be associated with effective governance institutions; variables reflecting the farmers’ ability to adapt to changing climate conditions (e.g., access to extension services or access to climatic information); and the presence of associations, social capital and collective action arrangements among farmers. Although some variables could be categorized in multiple ways (e.g., economic, social, and institutional), we opted for simplicity and to avoid duplication by assigning each variable to a single category based on a specific definition. The definitions and the variables recorded in the articles are listed and classified below: 1) C limatic variables : These are physical-climatic parameters that can affect land prices. They include temperatures, precipitation levels, maximum and minimum temperatures, natural disasters (heatwaves, drought, flood), water deficit, water stress, and solar radiation. 2) Physical variables : These refer to the physical characteristics of farmland that are distinct from climatic variables. They encompass soil type, soil texture, soil moisture, soil pH, soil organic carbon, soil erosion, elevation, altitude, latitude, percentage of grassland, river flow, depth to rock, land fragmentation, farmland size, distance to markets (inputs and outputs), distance to port, distance to cities, distance to provincial capital, and coastal regions. 3) Economic variables : These include economic attributes of the farmland (or the farmer) that can affect productivity. This category covers farm landholding, access to irrigation, asset value, rented farmland, livestock, crop types, labor, net revenues, GDP, village-level economic development, inputs prices, tractors per hectare, oxen per hectare, cultivator per hectare, per capita income, total horsepower, sale price, purchased water, risk perception, primary vs. secondary occupation. 4) Social variables : These consist of sociocultural and family characteristics such as education, household size, farmers’ age, farmers’ experience, population density, population growth, occupational vulnerability, individual family farm, young entrepreneur, gender, marital status, electricity, migration background, urban population, housing density, hotel density, literacy percentage, vulnerability index. 5) Institutional variables : These are associated to the existence of an organization (with more or less complex rules and organizational structures) that plays a role in the way agricultural activity is performed. Additionally, this category included the availability of services and support for farmers, indicating existing institutional frameworks. The selected variables in this group include organizations membership (farmers associations), cooperative membership, access to extension services, access to livestock extension services, less favored area payments (LFA), subsidies, farms receiving biodiversity protection nature payments (Natura 2000) and/or water protection payments, agri-environmental payments, high-quality certification of crops, government surveillance, organic certification, access to climatic information, number of farms participating in government programs, land reform project, communal areas, livestock/crop research, federal or private water access and access to credit. All studies consistently included the first two types of variables -climatic and physical- though they vary in specific choices and combinations (Prakash et al., 2024). In contrast, socioeconomic and institutional variables were used quite differently across these studies. There was significant diversity in the economic and social variables included, reflecting the substantial disparities in socio-economic conditions among developed, developing, and underdeveloped countries[2]. While some studies incorporated a broad range of socio-economic, others included none at all. The analysis indicated that institutional variables were rarely utilized in research, with only a few studies incorporating factors related to regulation, institutional support and/or the establishment of associations among actors. We identified 28 articles that utilized any of the 19 institutional variables we found (Table A1 in the Appendix contains a complete list of the articles included in the institutional analysis). In Table 1 we present these identified institutional variables along with details about the type of effect reported and its significance in the models. We also include information on how these variables were integrated into Ricardian analyses, the regions where these variables were applied, and the reference papers that included each variable. TABLE 1 AROUND HERE 4.2. Content analysis: the relevance of institutional variables Based on the analysis of 28 selected articles and a total of 84 different regressions[3] that included institutional variables, three were most frequently cited in the literature: “ access to extension services ”, “ organization membership ” and “ subsidies”. Access to extension services[4] was reflected in 12 of the 28 papers; in most of these studies (7), it was treated as a numerical variable (frequency of use), while in 5 papers it was used as a dummy variable. A total of 19 models analyzed this variable. Access to extension services was associated with providing technical knowledge on climate-resilient practices, crop varieties, and/or adaptive management techniques and to help farmers to adopt new technologies to improve agricultural productivity (Di Falco et al., 2012). In the majority of the models (68%), this variable was found to be significant, demonstrating a positive effect on the dependent variable[5]. This suggests that extension services enhance farm value and improve adaptive capacity in the face of climate stress. Only one study reported a significant negative impact, indicating that this variable may have a limited capacity to mitigate the effects of climate change (Alí et al., 2021). Alí et al. (2021) emphasized that increased information leads to greater awareness regarding the importance of crop management. However, this enhanced awareness also complicates management and raises costs, which could potentially reduce the value of the land. Finally, three articles found that the variable was not significant. It is important to highlight that this variable, has been just included in the case of models based on low and medium-income countries, this is because these services are broadly implemented in developed countries. Organization membership appeared in 6 articles featuring a total of 15 different models. This variable reflects the involvement of farmers in associations and organizations aimed at collectively supporting and improving their activities. While one might expect this variable to enhance climate change adaptation, the results across the models were quite varied. This variable was significant in only seven articles (47%), and it had a positive impact in six of those articles. The positive effect suggests that membership in farmers’ organizations can lead to greater resilience and adaptability to the impacts of climate change. On the other hand, the article that reported a negative impact may point to the costs associated with adhering to organizational rules and other constraints that can hinder the capacity for climate change adaptation. A significant number of models (53%) that did not find this variable significant raises the possibility that, despite the theoretical benefits of farmers' organizations, they often encounter major limitations. These may include insufficient resources and/or technical capacity within the associations, limited connections with government institutions, or challenges related to the size of the association and farmland characteristics (physical or social), which can impede the implementation of necessary measures and agreements. In the case of this variable, there is an important bias in the analysis since only 6 articles have been found that include this variable (although 15 regressions have been conducted), however, the articles focus on three countries: China, South Africa and Nigeria. Furthermore, this variable has been predominantly included as a dummy variable which could limit the nuanced understanding of institutional effects on climate-agriculture relationships. Other institutional variables commonly included in research were “subsidies” and “access to credit” . Subsidies appeared in 13 models across 5 articles, predominantly as a numerical coefficient. The findings indicate that direct subsidies serve as a positive variable in most of the models (85%). This suggests that subsidies are an important tool for mitigating climate change impacts. Notably, this variable was included in models applied in developed countries, which contrasts with the inclusion of other institutional variables. On the other hand, the variable “ access to credit ” was analyzed in 6 articles and included in a total of 7 models. Contrary to expectations that highlights how financial services enhance resilience against the adverse effects of climate change (Hussain et al., 2021), the results showed low significance of this variable. Only 3 models reported a significant impact, with 2 indicating a negative effect and 1 yielding a positive effect. The negative impact may suggest that access to credit is more commonly sought by lower-income farmers, who may lack the capability to utilize effectively the credit for climate change adaptation efforts. Similar as the case of the extension services, this variable has been traditionally included in low-income countries due to these countries face significant structural constraints in accessing credit services, which limits their capacity to implement climate adaptation strategies. Concerning the variable ‘ subsidies, ’ we can include variables such as ‘ farms receiving nature payments ’, ‘ less favored area payments ’ and ‘ agri-environmental payments ’. These variables were examined in 4 studies and 6 different models. The articles discussing ‘ farms receiving nature payments ’ and ‘ less favored area payments ’ propose two different regressions, and the variable ‘ agri-environmental payments ’ is analyzed in two separate articles. Although these variables are related to the allocation of subsidies, they were targeted at very specific purposes. The results indicated that while the subsidy variable was significant in mitigating the impacts of climate change, this was not the case for the other variables. For the variable ‘ farms receiving nature payments ’, the findings showed significance in only one of the models, and the coefficient was negative, suggesting that this variable does not contribute to the adaptation or mitigation of climate change impacts. Conversely, the variable ‘ less favored area payments ’, analyzed in two different models from the same article, showed significance with differing signs (positive and negative). When the subsidy was related to payments designed for the protection of water resources or biodiversity, its influence was positive, confirming that these subsidies help mitigate the effects of climate change. However, for other types of payments (not related to these specific purposes), the variable demonstrated significant but negative results. Finally, regarding ‘ agri-environmental payments ’ analysis in two different studies revealed no significance in one study and significance with a negative impact in the other. Some previous research reported that poorly designed land or environmental subsidies can inadvertently support unsustainable farming practices that degrade soil health and increase emissions, thereby adversely affecting climate change effects (Searchinger et al., 2008). Additionally, other studies suggested that the effectiveness of these subsidies in mitigating climate change effects varies significantly between countries (Roe et al., 2021). The comprehensive analysis of the remaining institutional variables included in the models, which were featured in only one study, revealed a significant positive effect for several factors: ‘ Livestock/crop research ’, ‘ Number of farms participating in government programs ’, ‘ Private/public water access ’, ‘ Legal form ’, and “ Other certification ” (which encompass designations of origin and regional certificates for products other than organic). These findings indicated that certain services, such as water access and those that disseminate technical and research results (including certifications and information regarding crops and livestock), enhanced stakeholders’ ability to adapt to change. Finally, variables associated with effective institutional governance, such as ‘ Number of farms participating in government programs ’ or ‘ Legal form ’, also played a crucial role in mitigating the negative effects of climate change. Conversely, other variables, including ‘ Government surveillance ’, ‘ Land reform project ’ (for all and mixed farmers models), ‘ High-quality certification of crops ’, ‘ Access to climatic information ’, and ‘ Communal areas ’ (for crop and mixed farmer models) were not found to be significant. While we would expect some of these variables to have a positive and significant impact, the results were inconclusive. It is noteworthy to indicate that these results are just based on the results in a unique article, and several times in a unique regression. 4.3. Discussion and policy implications The results of the institutional analysis shed light on the effectiveness of organizations and rules in supporting agricultural activities in the face of changing climatic conditions. Our outcomes show a growing interest in using Ricardian models to assess the effects of climate change, particularly in the agricultural sector. This increased awareness is driven by the understanding that climate change poses a serious threat to global food security. The rise in extreme natural events and significant shifts in climate patterns are disrupting agricultural systems, leading to decreases in crop yields, livestock productivity, and the food supply infrastructure (Shamshad et al., 2024). Furthermore, economic valuations of the impacts of climate change on agriculture are essential for informing more effective and targeted policy responses. Ricardian models have been refined over the years, incorporating methodological improvements as well as new variables and interactions among them, including climatic, physical, economic and social factors. Additionally, the scope of analysis has broadened to include studies in developed, developing, and underdeveloped countries, with some research making comparisons between these groups. However, despite these advancements, many models still overlook the inclusion of institutional and governance variables. Our findings showed that only 13% of the identified publications included at least one variable related to institutional issues, despite the analysis revealed that models including institutional variables focused on both developing and developed regions, the variables analyzed are different between these groups. Despite the previously mentioned limitations, the outcomes indicate that institutional variables were generally significant and had a positive impact. This suggests that effective governance, organized institutions, cooperation, active stakeholder involvement, and the availability of technical and knowledge services play important roles in mitigating the negative effects of climate change. Our analysis revealed that there were almost no variables with a significant negative impact. Notably, the implementation of subsidies in the agricultural sector stands out; while this variable was extensively tested in high-income countries, it was not explored as thoroughly in low or medium-income countries. Another relevant result is that the effectiveness of subsidies was largely dependent on their proper definition and implementation; if not executed well, they could inadvertently worsen climate change impacts. Subsidies aimed at specific environmental goals showed a negative impact on the models. This indicates that not all subsidies actually enhance the resilience of farms. Conversely, subsidies related to water access and usage demonstrated a clear positive effect. This implies a close relationship between climate and institutional factors concerning water resources, suggesting that policies should focus on improving the management capacity to this vital resource. Our findings also underscore the strong positive influence of associations and collective action. These groups enable communities to pool resources, share knowledge, and coordinate efforts toward sustainable practices, which in turn facilitates the implementation of mitigation strategies (Ostrom, 1990). This evidence suggests that climate change mitigation policies should prioritize the support of farmers' organizations, strengthen their connections with different levels of government, offer technical assistance via extension services, and disseminate research findings. The exhaustive qualitative analysis of existing literature highlights several areas for improvement in the models’ explanatory power. Enhancing this explanatory capacity could be achieved by examining the correlation between climate factors and institutional variables. In this context, it would be valuable to explore the impact of regulations and policies aimed to improving soil quality and promoting conservation practices that reduce erosion. Finally, in order to compare and analyze the impact of institutional variables, it is necessary to study them across a broader range of regions to enable meaningful comparisons. In this regard, it is also essential that the variables used are reasonably homogeneous and capture similar attributes (e.g., institutional quality or services provided). 5. Conclusions and future work Economies around the world must pursue policies to adapt to and mitigate the effects of climate change. However, assessing these impacts is particularly challenging due to the complexity of climate systems and ecosystems, uncertainties in climate models and projections, and difficulties in evaluating economic consequences (IPCC, 2022). In recent years, various tools have been developed to measure the economic impact of climate change damage globally. The economic assessment of climate change effects has mainly relied on Ricardian approaches since the foundational work of Mendelsohn et al. (1994), which introduced a framework for examining how land values are influenced by climate conditions. While this method has become widely used in estimating climate impacts, its consideration of institutional variables is notably underdeveloped, leading to potentially biased estimates and incomplete policy recommendations. This study reviewed literature on the application of Ricardian models to assess the impacts of climate change. The main objective was to analyze how institutional variables are integrated into these models and their effect on the primary sector’s adaptation to and mitigation of climate change impacts. A total of 203 studies focusing on Ricardian models for assessing climate change were identified in the WoS. However, after an in-depth analysis, only 28 articles included institutional elements in their regressions. We defined institutional variables as factors related to stakeholder collaboration or association, the availability of supporting services (such as extension services), and the presence of effective government or organizations (e.g., the existence of subsidies). The results of nearly all models showed a predominantly positive effect of institutional variables on land prices. This suggests that effective institutions can help mitigate the impacts of climate change. However, different crops and regions respond to climate change in diverse ways and with varying levels of resilience (Migliore et al., 2019). The ability of stakeholders and the supporting elements to adapt their practices is crucial for reducing negative impacts. The findings of the study indicate that institutional variables are infrequently incorporated into these models. This is likely due to the challenges in defining such variables, the significant differences between countries, and a lack of available data and information. Nevertheless, these limitations need to be addressed, as many of these variables were integrated into models that apply to low and medium-income countries. Including institutional variables poses challenges because of the complexity and diversity of governance systems across regions. Additionally, data on institutions is often incomplete or inconsistent. Despite this, the impact of these variables on mitigating climate change has been widely documented. These issues present a limitation for a comprehensive analysis of the role of institutional and effective governance variables. The potential for future research based on this work is broad. First, it is necessary to further examine which institutional variables most effectively facilitate adaptation to climate change. To enhance adaptation policy decisions in the agricultural sector, we need to shift from descriptive analyses to quantitative ones by incorporating econometric techniques that can rank the relative importance of these institutional variables. Additionally, an important factor not yet included in the models is the role of some institutions with a long history of cooperation among farmers (i.e. irrigation associations), in their capacity to adapt to climate change. Declarations Acknowledgements This research has been funded by projects PID2020-115495RA-I00 from the Spanish Ministry of Science and Innovation. Ethics approval and consent to participate: ‘Not applicable’ Our manuscript does not report or involve animals, humans, human data, human tissue or plants. Consent for publication: ‘Not applicable’ Our manuscript does not contain any individual or personal data. Competing Interests: Authors declare no competing interests, both financial and non-financial. Author contributions: All the authors (Encarna Esteban, Yolanda Martínez and Sara Calvo) have equally contributed in the methodology, elaboration and revision of the manuscript. Funding: This research has been funded by projects PID2020-115495RA-I00 from the Spanish Ministry of Science and Innovation. Availability of data and materials: ‘Not applicable’ The data used in our manuscript are free access in research webs. Anyway, any information regarding the documents used is fully available and the authors could provide any necessary information upon request. References Abidoye, Babatunde O.; Kurukulasuriya, Pradeep; Mendelsohn, Robert 2017. Structural Ricardian analysis of South-East Asian Agriculture. Climate Change Economics, Vol. 8, No. 3, pp:1-8. Alí U., Wang J., Ullah A., Ishtiaque A., Javed T., Nurgazina Z. 2021. The impact of climate change on the economic perspectives of crop farming in Pakistan: Using the Ricardian model. Journal of Cleaner Production, 308: 127219. Amouzay, H. and and El Ghini, A. 2024. A Systematic Review of Key Spatial Econometric Models for Assessing Climate Change Impacts on Agriculture. MPRA (Munich Personal RePEc Archive), Paper No. 123222, posted 13 Jan 2025. Baylie, Melese Mulu; Fogarassy, Csaba 2021. Examining the Economic Impacts of Climate Change on Net Crop Income in the Ethiopian Nile Basin: A Ricardian Fixed Effect Approach, Sustainability 13, 7243. Chatzopoulos, Thomas; Lippert, Christian 2015. Adaptation and Climate Change Impacts: A Structural Ricardian Analysis of Farm Types in Germany. Journal of agricultural economics, Vol. 66, No. 2: 537–554. Chatzopoulos, Thomas; Lippert, Christian 2016. Endogenous farm-type selection, endogenous irrigation, and spatial effects in Ricardian models of climate change. European Review of Agricultural Economics 43 (2):217-235. De Salvo, Maria; Raffaelli, Roberta; Moser, Riccarda 2013. The impact of climate change on permanent crops in an Alpine region: A Ricardian analysis. Agricultural systems, 118: 23-32. Dell’Angelo J., P. D’Odorico, M.C. Rulli, P. Marchand 2017. The tragedy of the grabbed commons: coercion and dispossession in the global land rush, World Development, 92: 1-12. Di Falco, Salvatore; Yesuf, Mahmud; Kohlin, Gunnar; Ringler, Claudia 2012. Estimating the Impact of Climate Change on Agriculture in Low-Income Countries: Household Level Evidence from the Nile Basin, Ethiopia. Environmental & Resource economics 52: 457-478. Dubash, N.K. 2010. Viewpoint – Reflections on the WCD as a mechanism of global governance. Water Alternatives 3(2): 416-422 Elum, Z. A.; Nhamo, G.; Antwi, M. A. 2018. Effects of climate variability and insurance adoption on crop production in select provinces of South Africa. Journal of water and climate change 09.3: 500-511. European Environmental Agency 2024. Economic losses from weather-and climate-related extremes in Europe. Published 14 October 2024. https://www.eea.europa.eu/en/analysis/indicators/economic-losses-from-climate-related?activeAccordion=ecdb3bcf-bbe9-4978-b5cf-0b136399d9f8#ref-RBnwU Fabri C., Moretti M., Van Passel S. 2022. On the (ir)relevance of heatwaves in climate change impacts on European agriculture. Climatic Change, 174:16. FAO. 2018. The future of food and agriculture – Alternative pathways to 2050. Rome. 224 pp. Licence: CC BY-NC-SA 3.0 IGO Feng, Xiaolong; Qiu, Huanguang; Pan, Jie; Tang, Jianjun 2021. The impact of climate change on livestock production in pastoral areas of China Science of the total environment, 770: 144838. Fezzi C. & Bateman I. 2015. The Impact of Climate Change on Agriculture: Nonlinear Effects and Aggregation Bias in Ricardian Models of Farmland Values. Journal of the Association of Environmental and Resource Economists, Vol. 2, No. 1, pp. 57-92 Gadedjisso-Tossou, Agossou; Egbendewe, Aklesso Y. G.; Abbey, Georges A. 2016. Assessing the impact of climate change on smallholder farmers' crop net revenue in Togo. Journal of agriculture and environment for international development, 110 (2): 229-248. Gebreegziabher, Z., Mekonnen, A., Deribe, R., Abera, S., and Kassahun, M.M. 2013. ‘Crop–livestock inter-linkages and climate change implications for Ethiopia's agriculture: a Ricardian approach’, RFF Discussion Paper Series No. EfD 13–14, Resources for the Future, Washington, DC. Gebreegziabher, Zenebe; Stage, Jesper; Mekonnen, Alemu; Alemu, Atlaw. 2016. Climate change and the Ethiopian economy: a CGE analysis. Environment and Development Economics 21 (2): 205-225. González U., Jorge; Velasco H., Roberto 2008. Evaluation of the impact of climatic change on the economic value of land in agricultural systems in Chile. Chilean journal of agricultural research, 68(1):56-68. Gyimah, J., Hayford, I.S., Nyantakyi, G., Adu, P.S., Batasuma, S., Yao, X. 2024. The era of global warming mitigation: The role of financial inclusion, globalization and governance institutions. Heliyon 10(1): e23471. Hossain M.S., Muhammad A., Lu Q., Minjuan Z., Yasir M., Harald K., 2019. Economic impact of climate change on crop farming in Bangladesh: An application of Ricardian method. Ecological Economics, 164: 106354. Hussain, A.H.M.B., Islam, M., Ahmed, K.J., Haq, S.M.A., Islam, M.N. 2021. Financial Inclusion, Financial Resilience, and Climate Change Resilience. In: Luetz, J.M., Ayal, D. (eds) Handbook of Climate Change Management. Springer, Cham. IPCC (Intergovernmental Panel on Climate Change), 2007. Contribution of Working Group I to the Fourth Assessment Report. Cambridge University Press, Cambridge, UK IPCC (Intergovernmental Panel on Climate Change), 2018. Proposed Outline of the Special Report in 2018 on the Impacts of Global Warming of 1.5 ◦C above Pre-Industrial Levels and Related Global Greenhouse Gas Emission Pathways, in the Context of Strengthening the Global Response to the Threat of Climate Change. 2018. IPCC (Intergovernmental Panel on Climate Change), 2022. Climate Change 2022: Impacts, Adaptation and Vulnerability. Contribution of Working Group II to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change, H. Pörtner, D. Roberts, M. Tignor et al. (eds.), Cambridge: Cambridge University Press. Kurukulasuriya P. & Mendelsohn R. 2006. A Ricardian analysis of the impact of climate change on African cropland. CEEPA Discussion Paper No. 8, Special Series on Climate Change and Agriculture in Africa. Kurukulasuriya, Pradeep; Ajwad, Mohamed Ihsan 2007. Application of the Ricardian technique to estimate the impact of climate change on smallholder farming in Sri Lanka. Climatic Change 81: 39-59. Marquardt Jens, Anna Fünfgeld, Joshua Philipp Elsässer, 2023. Institutionalizing climate change mitigation in the Global South: Current trends and future research, Earth System Governance, Volume 15, 100163, Massetti E. and Mendelsohn R. 2020. Temperature thresholds and the effect of warming on American farmland value. Climatic Change, 161:601–615. Mendelsohn, R., Nordhaus, W., Shaw, D., 1994. The impact of global warming on agriculture: a Ricardian analysis. American Economic Review 84, 753–771. Mendelsohn, Robert; Basist, Alan; Dinar, Ariel; Kurukulasuriya, Pradeep; Williams, Claude. 2007. What explains agricultural performance: climate normals or climate variance? Climatic change, 81:85–99. Mendelsohn & Dinar, 2009. Land Use and Climate Change Interactions. Annual Review and Resource Economics, 1:309–32. Mendelsohn, Robert; Arellano-Gonzalez, Jesus; Christensen, Peter 2010. A Ricardian analysis of Mexican farms. Environment and Development Economics 15(2): 153-171. Mendelsohn & Massetti, 2017. The Use of Cross-Sectional Analysis to Measure Climate Impacts on Agriculture: Theory and Evidence. Review of Environmental Economics and Policy, volume 11, issue 2, pp. 280–298. Migliore G., Zinnanti C., Schimmenti E., Borsellino V., Schifani G., Di Franco C.P., Asciuto A. 2019. A Ricardian analysis of the impact of climate change on permanent crops in a Mediterranean region. New Medit, 1. Moher D, Liberati A, Tetzlaff J, Altman D G. 2009. Preferred reporting items for systematic reviews and meta-analyses: the PRISMA statement BMJ 2009; 339 :b2535. Molua, Ernest L. 2009. An empirical assessment of the impact of climate change on smallholder agriculture in Cameroon. Global and planetary change, 67:205-208. Moretti, Michele; Vanschoenwinkel, Janka; Van Passel, Steven 2021. Accounting for externalities in cross-sectional economic models of climate change impacts. Ecological Economics, 185: 107058. Nyuor, Anslem Bawayelaazaa; Donkor, Emmanuel; Aidoo, Robert; Buah, Samuel Saaka; Naab, Jesse B.; Nutsugah, Stephen K.; Bayala, Jules; Zougmore, Robert. 2016. Economic Impacts of Climate Change on Cereal Production: Implications for Sustainable Agriculture in Northern Ghana. SUSTAINABILITY, 8: 724. Ojo, T. O.; Baiyegunhi, L. J. S. 2021. Climate change perception and its impact on net farm income of smallholder rice farmers in South-West, NigeriaJournal of cleaner production Onyekuru, NwaJesus Anthony; Marchant, Rob. 2016. Assessing the economic impact of climate change on forest resource use in Nigeria: A Ricardian approach. Agricultural and forest meteorology, 220: 10-20. Ortiz-Bobea A. 2020. The role of nonfarm influences in Ricardian estimates of climate change impacts on US agriculture. American Journal Agricultural Economics 102(3): 934–959. Ostrom Elinor 1990. Governing the commons: the evolution of institutions for collective action. Cambridge University Press, 1990. Page M J, Moher D, Bossuyt P M, Boutron I, Hoffmann T C, Mulrow C D et al. 2021. PRISMA 2020 explanation and elaboration: updated guidance and exemplars for reporting systematic reviews BMJ 2021; 372 :n160 Polsky, C; Easterling, WE. 2001. Adaptation to climate variability and change in the US Great Plains: A multi-scale analysis of Ricardian climate sensitivities Agriculture ecosystems & environment, 85:133-144. Prakash, D., Nemati, M., Dinar, A., Struthers, C., Mackenzie, S., and Shugart, M.S. 2024. Advancements to the Ricardian analysis in the past quarter of the century. Climate Change Economics 2024, 15:03 Roe, S., Streck, C., Beach, R., Busch, J., Chapman, M., Daioglou, V., Deppermann, A., Doelman, J., Emmet-Booth, J., Engelmann, J., Fricko, O., Frischmann, C., Funk, J., Grassi, G., Griscom, B., Havlik, P., Hanssen, S., Humpenöder, F., Landholm, D., … Lawrence, D. 2021. Land-based measures to mitigate climate change: Potential and feasibility by country. Global Change Biology, 27, 6025–6058. Shamshad, J., Nawaz, A.F., Khan, M.B., Arif, M. 2024. Climate Change and Food Security. In: Fahad, S., Saud, S., Nawaz, T., Gu, L., Ahmad, M., Zhou, R. (eds) Environment, Climate, Plant and Vegetation Growth. Springer, Cham. Searchinger Timothy, Ralph Heimlich, R. A. Houghton, Fengxia Dong, Amani Elobeid, Jacinto Fabiosa, Simla Tokgoz, Dermot Hayes, and Tun-Hsiang Yu 2008. Use of U.S. croplands for biofuels increases greenhouse gases through emissions from land-use change. Science, 319(5867), 1238-1240. Schlenker, Wolfram; Hanemann, W. Michael; Fisher, Anthony C. 2007. Water availability, degree days, and the potential impact of climate change on irrigated agriculture in California. Climatic change. Seo S.N., Mendelsohn R., Munasinghe M. 2005. Climate change and agriculture in Sri Lanka: a Ricardian valuation. Environment and Development Economics 10: 581–596. Stern N. 2007 The economics of climate change: the stern review. Cambridge University Press, Cambridge. Su X.S. & Chen M. 2022. Econometric Approaches That Consider Farmers’ Adaptation in Estimating the Impacts of Climate Change on Agriculture: A Review. Sustainability, 14, 13700. Swiss Re Institute 2021. The economics of climate change: no action not an option. April, 2021. Tibesigwa, Byela; Visser, Martine; Turpie, Jane 2015. The impact of climate change on net revenue and food adequacy of subsistence farming households in South Africa. Environment and development economics, 20: 327–353. Tibesigwa, Byela; Visser, Martine; Turpie, Jane 2017. Climate change and South Africa's commercial farms: an assessment of impacts on specialised horticulture, crop, livestock and mixed farming systems. Environment development and sustainability 19:607–636. Timmins, C. 2006. Endogenous land use and the Ricardian valuation of climate change 2006. Environmental & resource economics, 33: 119–142. Vanschoenwinkel, Janka; Mendelsohn, Robert; Van Passel, Steven 2016. Do Western and Eastern Europe have the same agricultural climate response? Taking adaptive capacity into account. Global environmental change-human and policy dimensions, 41: 74-87. Wang, Jinxia; Mendelsohn, Robert; Dinar, Ariel; Huang, Jikun; Rozelle, Scott; Zhang, Lijuan 2009. The impact of climate change on China's agriculture Agricultural economics, 40: 323–337. Wang, Jinxia; Huang, Jikun; Zhang, Lijuan; Li, Yumin. 2014. Impacts of climate change on net crop revenue in North and South China. China agricultural economic review, Vol. 6 No. 3: 358-378. Footnotes [1] Note that a study’s country of origin corresponds to the location of the first author’s affiliated institution. Therefore, this categorization is approximate, given that the majority of articles are authored by multiple individuals with typically diverse affiliations. [2] For example, variables like access to electricity, illiteracy rate and number of oxen are only meaningful in certain countries/areas. [3] See Table A1 in the Annex for a complete list of articles, used methodologies and variables. [4] We include two variables such as extension services for farming and livestock activities [5] The endogenous variable is either the land value or the farmer's farm income depending on the research article. Tables Table 1. Identification of institutional variables in the articles reviewed Variable Type of variable Type of effect (significance) Region Reference Organizations membership (farmers associations) Dummy Negative (no significant) South Africa Elum et al. (2018) Dummy Positive (significant) Nigeria Ojo & Baiyegunhi (2021) Numerical (5 models) Positive (no significant) in models for all farms and mixed farms Negative (no significant) in models crop farmers, horticulture and livestock farms. South Africa Tibesigwa et al. (2017) Dummy (3 models) Positive (significant) in models all farms and irrigated farms Negative (significant) in rainfed farms China Wang et al. (2014) Dummy (4 models) Positive (significant) in all farms, irrigated, irrigated or rainfed. Negative (no significant) in rainfed farms China Wang et al. (2009) Dummy No significant China Feng et al. (2021) Number of farms participating in government programs Numerical Positive (significant) USA Polsky & Easterling (2001) Access to credit Numerical coefficient Negative (no significant) Pakistan Ali et al. (2021) Dummy Negative (no significant) South Africa Elum et al. (2018) Numerical Negative (significant) Mexico and USA Mendelsohn et al. (2010) Dummy Negative (significant) Ethiopia Gebreegziabher et al (2013) Dummy Positive (no significant) Bangladesh Hossain et al. (2019) Dummy (2 models) Positive (significant) for corn model Negative (no significant) for sorgum model Ghana Nyuor et al. (2016) Access to extension services Numerical Negative (significant) Pakistan Ali et al. (2021) Numerical Positive (significant) Togo Gadedjisso-Tossou et al. (2016) Dummy Positive (significant) Ethiopia Gebreegziabher et al (2016) Numerical Positive (significant) Chile González and Velasco (2008) Dummy Positive (significant) Bangladesh Hossain et al. (2019) Dummy Positive (significant) Cameroon Molua (2009) Numerical Negative (no significant) Ghana Nyour et al. (2016) Dummy Negative (no significant) Nigeria Ojo & Baiyegunhi (2021) Numerical (no significant not included in the model) Nigeria Onyekuru & Marchant (2016) Numerical (4 models) Positive (significant) in model all farmers Negative (not significant) in mixed and crop farmers models South Africa Tibesigwa et al. (2015) Access to livestock extension services Numerical (5 models) Positive (significant) in all models South Africa Tibesigwa et al. (2015) Dummy Positive (significant) Ethiopia Gebreegziabher et al (2016) Subsidies Dummy Negative (no significant) Sri Lanka Kurukulasuriya & Ajw (2007) Numerical (3 models) Positive (significant) USA & Canada Mendelsohn et al. (2007) Numerical (3 models) Positive (significant) Europe Moretti et al. (2021) Numerical (4 models) Positive (significant) 3 models, Negative (no significant) 1 model Europe Vanschoenwinkel et al. (2016) Numerical (2 models) Positive (significant) Europe Fabri et al. (2022) Farms receiving nature (Natura 2000) and/or water protection payments Dummy (2 models) Negative (significant for apple) Negative (not significant for grape) Italy Chatzopoulos & Lippert (2016) Less favored area payments (LFA) Numerical (2 models) Positive (significant) for nature and water protection Negative (significant) for other payments Germany Chatzopoulos & Lippert (2015) Agri-environmental payments Dummy Negative (no significant) Nigeria Ojo & Baiyegunhi (2021) Numerical Negative (significant) Italy Chatzopoulos & Lippert (2016) High-quality certification of crops Dummy Negative (significant) for apple certification Negative (no significant) for grape certification Italy De Salvo et al. (2013) Government surveillance Dummy Negative (no significant) China Feng et al. (2021) Organic certification/other certification Dummy Positive (no significant) for organic certification; Positive (significant) for other certification Italy Migliore et al. (2019) Access to climatic information/notice of climate change Dummy Negative (no significant) Nigeria Ojo & Baiyegunhi (2021) Dummy Negative (significant) Nigeria Onyekuru & Marchant (2016) Private vs. public water access Numerical Positive (significant) USA (California) Schlenker et al. (2007) Land reform project Numerical (4 models) Negative (no significant) for all and mixed farmers, negative (significant) for crop farmers South Africa Tibesigwa et al. (2015) Livestock/crop research Numerical (4 models) Positive (significant) Brazil Timmins (2006) Land equity share scheme Numerical (4 models) Positive (no significant) for all farmers’ model; negative (no significant) for mixed farmers model South Africa Tibesigwa et al. (2015) Communal areas Numerical (4 models) Positive (no significant) for all farmers Negative (no significant) for crop farmers and mixed farmers models Positive (significant) for livestock farmer South Africa Tibesigwa et al. (2015) Legal form Dummy Positive (significant) Italy Migliore et al. (2019) Supplementary Files A1TABLESClimaticChange.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7150109","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":498992308,"identity":"3089a757-3b49-4bec-a3a5-744b00aca994","order_by":0,"name":"Encarna Esteban","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAyUlEQVRIiWNgGAWjYDADfiA+QJoWyQaStRgQrZ5fuvnh58oddnLGN3IPHvxRc4+Bn5+AZsk5x4wlz55JNja7kZdwQOJYMYPkjAQC7rmRwyDZ2MacuO1GjsEBA7YEoAgBh9nfyGH+2dhWX795BlBLwr8EBvvzBBxmIJHDBrTlcAKQYXDgYBvQFgYCDpO4kWZm2dh23HDGmTcGBxv7EngkbhDQwj8j+fHNxrZqef72HOOPP74lyPH3E3AYBuAhUf0oGAWjYBSMAmwAALRaQ4dhdattAAAAAElFTkSuQmCC","orcid":"https://orcid.org/0000-0002-4485-3158","institution":"University of Zaragoza: Universidad de Zaragoza","correspondingAuthor":true,"prefix":"","firstName":"Encarna","middleName":"","lastName":"Esteban","suffix":""},{"id":498992309,"identity":"fde28fde-438c-4619-af1a-b9e7a80babce","order_by":1,"name":"Yolanda Martínez","email":"","orcid":"","institution":"University of Zaragoza: Universidad de Zaragoza","correspondingAuthor":false,"prefix":"","firstName":"Yolanda","middleName":"","lastName":"Martínez","suffix":""},{"id":498992310,"identity":"6ff4cd30-baf6-448a-af26-21531e4bb7d1","order_by":2,"name":"Sara Calvo","email":"","orcid":"","institution":"University of Zaragoza: Universidad de Zaragoza","correspondingAuthor":false,"prefix":"","firstName":"Sara","middleName":"","lastName":"Calvo","suffix":""}],"badges":[],"createdAt":"2025-07-17 14:32:03","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7150109/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7150109/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":89397768,"identity":"c7fd648e-1eb6-45ac-86c6-959764eabff4","added_by":"auto","created_at":"2025-08-19 13:50:08","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":29317,"visible":true,"origin":"","legend":"\u003cp\u003eFlow diagram for database search and selection process\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eSource\u003c/em\u003e: Authors’ own elaboration, adapted from Moher et al. 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Introduction","content":"\u003cp\u003eClimate change has emerged as one of the most urgent environmental challenges of the 21st century. Its negative consequences are escalating around the world, including rising sea levels, extreme temperature fluctuations, increased variability in daily weather conditions, changes in precipitation and humidity patterns, evapotranspiration rates and a higher incidence and frequency of extreme weather events (Baylie and Fogarassy, 2021;\u0026nbsp;IPCC, 2018). Estimates indicate that if environmental degradation continues at its current rate, global GDP could shrink by 14% by the middle of the century (Swiss Re Institute, 2021). Furthermore, the European Union reported that between 1980 and 2023, climate-related extreme events caused economic losses amounting to EUR 738 billion. This trend is expected to increase, as over EUR 162 billion (22%) of those losses occurred between 2021 and 2023 (European Environmental Agency, 2024).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAgriculture is one of the most vulnerable economic activities, highly affected by climate change due to its dependence on weather, water, and natural resources. Approximately 27% of the world\u0026rsquo;s population relies directly or indirectly on farming for their livelihoods, a figure that is even higher in many developing countries (FAO, 2018). The various impacts of climate change highlight the urgent need to evaluate and quantify these effects. It is essential to assess the consequences of climate change in order to develop mitigation and adaptation policies, especially in the most vulnerable geographical areas and economic sectors (IPCC, 2022).\u003c/p\u003e\n\u003cp\u003eGiven the significance of farming and its vulnerability to climate change, many empirical studies have been conducted to evaluate the impacts on agricultural production and the possible future implications (see, for example, Mendelsohn et al, 1994; Abidoye et al, 2017; Massetti \u0026amp; Mendelsohn, 2020; Seo et al, 2005; Migliore et al, 2019). The Ricardian approach, as described by Mendelsohn et al. (1994) analyzes the impact of climate change on the value of farmland. This model is based on the hypothesis that farmers aim to maximize their profits, taking into account exogenous variables beyond their control and responding to the prevailing circumstances (Mendelsohn \u0026amp; Dinar, 2009). The models incorporate not only climatic variables but also physical and socio-economic factors to assess land value (Mendelsohn \u0026amp; Massetti, 2017). One major advantage of the Ricardian methodology is that it considers how farmers adapt; the models imply that they maximize their profits under all weather conditions and adjust their crops accordingly (Mendelsohn \u0026amp; Massetti, 2017; Su \u0026amp; Chen, 2022). Using econometric regressions, several studies have projected future economic impacts for various countries and regions based on forecasts of changing precipitation and temperature patterns (Mendelsohn et al, 1994; Seo et al, 2005; Mendelsohn et al, 2010; Fezzi \u0026amp; Bateman, 2015; Hossain et al, 2019; Ortiz-Bobea, 2020).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAlthough the Ricardian approach is widely used, it has faced criticism due to its limitations. One key issue is the need to incorporate the impact of agricultural policies into the analysis (Kurukulasuriya \u0026amp; Mendelsohn, 2006). Despite recognizing the importance of social, economic and political variables, many studies often exclude them and rely only on a narrow range of traditional factors. Institutional variables, in particular, are significantly underrepresented. This lack of focus can lead to biased estimates and incomplete policy recommendations. There is increasing evidence that institutions play a crucial role in shaping both climate vulnerability and the capacity for adaptation. Unfortunately, only a handful of studies have included institutional variables \u0026ndash;such as formal and informal rules, standards, and regulations- within their Ricardian analyses (Marquardt et al, 2023). Although incorporating these variables poses challenges due to the complexities of data collection, it is essential for effective analysis. The effectiveness of governmental and institutional frameworks is vital for successfully mitigating and adapting to the impacts of climate change (Gyimah et al, 2024).\u003c/p\u003e\n\u003cp\u003eThe objective of this paper is to highlight the gaps in institutional analysis within Ricardian models. By reviewing the literature on Ricardian modeling, this paper analyzes empirical studies that incorporate institutional variables, economic policy factors, regulatory elements, or cooperation and association variables among actors and institutions. We document the systematic weaknesses in how existing models address institutional variables, providing examples from both developed and developing countries.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eOur analysis shows that improving the treatment of institutions can enhance both the explanatory power of Ricardian models and their relevance for climate policy design. The findings indicate that only a limited number of studies included these types of variables into Ricardian modeling, highlighting a significant gap in integrating institutional and governance issues into climate change assessment. Furthermore, due to data difficulties in representing institutional variables, they have often been included as dummy variables, which creates a problem for interpreting them (not including elements such as institutional quality or frequency). Finally, the difficulty in measuring and defining institutions makes the comparison of the same variable challenging since it may include very diverse elements (e.g., what is understood by farmers\u0026apos; association or the facilities included in \u0026lsquo;extension services\u0026rsquo;). \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eOverall, the outcomes suggest that the most commonly used variables pertain to actors\u0026rsquo; associations or cooperation, subsidies, and public planning services (such as extension services). The results demonstrate that institutional variables generally have\u0026nbsp;a positive impact across all models, suggesting that the presence of both formal and\u0026nbsp;informal rules plays a relevant role in minimizing the impacts of climate change. However, a noteworthy result is that certain mechanisms (such as specific subsidies or the availability of environmental information) yield contradictory outcomes, suggesting they either have no effect or may even negatively influence efforts to mitigate the effects of climate change. In this regard, our conclusions indicate that while the development of institutions, regulations, and rules\u0026mdash;both formal and informal\u0026mdash;are important, the way in which coordination and policy design are carried out is crucial to avoid counterproductive outcomes. Our outcomes highlight the importance of strengthening institutions (from both formal and informal perspectives) as key elements in the design and performance of policies for mitigating and regulating climate change impacts, particularly in the agricultural sector.\u003c/p\u003e\n\u003cp\u003eThe paper is organized as follows. Section 2 presents the method and the analytical approach. Section 3 collects the results of the analysis. Section 4 presents the discussion and implications of the results, and finally, section 5 presents conclusions and suggestions for future work.\u003c/p\u003e"},{"header":"2. Methodology: systematic review","content":"\u003cp\u003eTo assess the role of how institutional variables influence the impacts of climate change on agriculture a systematic review is applied to ensure a transparent, reproducible, and comprehensive analysis. Our review follows the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines for conducting systematic reviews, which outline key procedures for study selection, data extraction, and synthesis (Page et al., 2021; Moher et al., 2009).\u003c/p\u003e\n\u003cp\u003eThe literature search was conducted using an academic database to identify studies that used Ricardian models to assess climate change impacts on agriculture. The database searched was Web of Science (WoS) and the search strategy was designed to capture all articles published in English. The literature was collected in April 2024 by searching for \u0026ldquo;\u003cem\u003eRicardian climate change\u003c/em\u003e\u0026rdquo;, \u0026ldquo;\u003cem\u003eRicardian models\u003c/em\u003e\u0026rdquo; AND \u0026ldquo;\u003cem\u003eclimate change\u003c/em\u003e\u0026rdquo; in the title, summary and/or keywords. Furthermore, references from key articles were revised to ensure no important studies were overlooked (snowball sampling). In total, we found 203 scientific articles published in English. We excluded working papers, book chapters and other studies (grey literature). Figure 1 shows the results of the systematic search in the WoS by strings and subjects and presents the complete study selection process based on PRISMA methodology (Moher et al., 2009).\u003c/p\u003e\n\u003cp\u003eFIGURE 1 AROUND HERE\u003c/p\u003e\n\u003cp\u003eFigure 1 illustrates the complete flow of the search and exclusion processes. After removing duplicate articles, non-English articles and documents not categorized as articles (e.g., book chapters, theses, working documents, etc.), we excluded and additional 15 inaccessible documents. This left us with a final review sample of 154 studies. Since we needed quantitative data to analyze the models and variables used, we also removed 16 theoretical articles and/or reviews from our selection. We also omitted some studies (20) that referenced Ricardian methodology as a relevant or benchmark theory but did not base their own methodology on it. Finally, 6 articles were excluded due to the lack of an economic model and/or data to replicate the study and analyze the variables. So, we end up with a sample of 112 research articles. \u0026nbsp;\u003c/p\u003e"},{"header":"3. Bibliometric analysis","content":"\u003cp\u003eFor this study, we conducted and an in-depth literature review of all articles that meet out our eligibility criteria. This in-depth analysis aimed to highlight key trends, influential authors, and the development of research at the intersection of climate change, agriculture, and institutions. The appendix (Table A1) contains a complete list of the articles included in the analysis.\u003c/p\u003e\n\u003cp\u003eFigure 2 illustrates the timeline of publications related to Ricardian models, beginning with the seminal paper by Mendelsohn et al. (1994), which was the first to apply a Ricardian model to analyze the impacts of climate change. The distribution of papers over time revealed a significant increase in the number of publications on Ricardian models in climate change research, especially from 2007 onward. This rise coincided with the publication of the IPCC\u0026rsquo;s fourth assessment report in 2007, which highlighted the critical need for action and regulation regarding the impacts of climate change (IPCC, 2007). Before 2007, only a limited number of studies used the Ricardian approach to explore climate change adaptation in agriculture, with most research focused primarily on basic model development. As shown in Figure 2, there were only 16 publications between 1994 and 2006.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFIGURE 2 AROUND HERE\u003c/p\u003e\n\u003cp\u003eBetween 2007 and 2015, this field experienced rapid growth due to increasing awareness of climate change\u0026rsquo;s impact on agriculture and the adoption of advanced econometric techniques in Ricardian models (Amouzay and El Ghini, 2024). This period also marked the initial inclusion of institutional variables in Ricardian frameworks, reflecting the growing recognition of governance and socio-economic factors in climate adaptation. A notable spike in publications occurred in 2009 (16), coinciding with the United Nations Climate Change Conference (COP 15) held in Copenhagen, Denmark, where major greenhouse gas-emitting countries acknowledged climate change as a global issue for the first time (Dubash, 2010). However, despite this surge in scientific activity, the number of publications declined from 2010 to 2015.\u003c/p\u003e\n\u003cp\u003eAfter 2015, there was a continuous increase in publications, particularly in studies examining institutional variables such as land tenure, governance, and infrastructure, in addition to the traditional climate variables featured in Ricardian models (Stern, 2007). From 2016 onwards, the number of publications notably increased almost every year. Recent impacts from undeniable changes in temperature and precipitation patterns worldwide, along with a higher frequency of extreme natural events like fires, droughts, and floods, have raised concerns in nearly all developed nations (Fabri et al., 2022). This shift underscores the growing recognition of the role that institutions play in shaping agricultural adaptation strategies. Over the past decade, research has become more regionally diverse, with an increasing volume of studies emerging from developing countries, where the impacts of climate change are expected to be most severe (Dell\u0026rsquo;Angelo et al., 2017).\u003c/p\u003e\n\u003cp\u003eAnother important finding pertains to the thematic areas of analysis in which research studies on Ricardian modeling were published. We categorized these studies based on the journal subject categories defined by the WoS (see Fig. 3). While the areas of specialization are quite diverse, most articles appeared in journals related to environmental sciences, meteorology and/or agriculture. A critical aspect of Ricardian models is the incorporation of climate variables in analyzing agricultural productivity. Early Ricardian models primarily focused on the direct effects of changes in temperature and precipitation on land rents or crop yields, and these studies were published in journals within these thematic categories. Over time, Ricardian approaches also examined other interactions, such as the frequency of extreme weather events and their compounded effects on agriculture (Prakash et al., 2024).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAnother major category is \u0026ldquo;business economics\u0026rdquo; which highlights the strong economic focus of these studies. A key contribution of the Ricardian literature is quantifying the impacts on agricultural land values, providing concrete estimates of the economic costs associated with climate change. Consequently, Ricardian models have played a significant role in the economic discussion of climate change\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThese findings illustrate the growing interest from various disciplines in studying climate change and its impacts. However, these results also indicate that models incorporating a strong economic component tend to have less influence within economic fields (see relevant thematic areas in Fig. 3). This may explain the limited inclusion of socio-economic and, in particular, institutional variables in many of the Ricardian models that have been developed.\u003c/p\u003e\n\u003cp\u003eFIGURE 3 AROUND HERE\u003c/p\u003e\n\u003cp\u003eThe geographic distribution of research papers provides valuable insights due to it highlights the regions most interested in studying and assessing climate change and it reveals that 66 of the 112 articles identified with empirical models were conducted in underdeveloped or developing countries. Early Ricardian models were particularly implemented in developed countries and especially in the United States. This trend can be attributed to the availability of robust data and information in high-income regions. However, the analysis rapidly expanded to include studies from low-income countries in Africa, Asia, and Latin America. In total, 66 articles were identified in these regions, especially in recent years. This geographic shift indicates a growing interest in assessing the costs of climate change in highly vulnerable regions, such as low and middle-income countries, which are more dependent on agriculture and may face more severe economic impacts. Additionally, this geographic shift underscores the increasing focus on understanding the role of institutions in shaping climate adaptation strategies in low- and middle-income countries. In these regions, institutional factors such as land tenure, governance, and infrastructure may significantly influence adaptation outcomes.\u003c/p\u003e\n\u003cp\u003eTo complement the geographic distribution analysis, Figure 4 also presents the first author\u0026rsquo;s affiliation for the identified studies[1]. This analysis highlights that the majority of the studies feature first authors affiliated with institutions in developed countries, particularly within universities in the United States and Europe. However, it is also important to note the growing contribution of studies from China, which ranks as the second country with the highest number of studies applying Ricardian models to assess climate change impacts. Other notable contributors include India, Pakistan, and South Africa. It can be observed a correlation between the number of studies and the affiliation of the authors, largely due to the data and resources required for the development and application of these models.\u003c/p\u003e\n\u003cp\u003eFIGURE 4 AROUND HERE\u003c/p\u003e\n\u003cp\u003eTo conclude, the results of the bibliometric analysis reveal a growing interest in the assessment of climate change impacts, particularly over the past two decades. Furthermore, during last decades studies have increasingly focused on the impacts in low- and middle-income countries. Finally, one of the conclusions of this analysis is that such assessments remain concentrated in a limited group of countries, with many regions still lacking the application of these models.\u003c/p\u003e"},{"header":"4. Results and discussion","content":"\u003cp\u003e\u003cem\u003e4.1. Definition of institutional variables\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eClimate change impacts are influenced not only by environmental factors but also by other elements that are crucial for mitigating these effects. Institutional and governmental factors are vital for effectively addressing the negative consequences of climate change. As Guimah et al (2024) state, \u0026lsquo;\u003cem\u003eStrong and effective governance institutions are essential for ensuring that climate policies are well-designed, well-implemented, and responsive to changing circumstances\u003c/em\u003e.\u0026rsquo;. However, including institutional variables in Ricardian models remains an unresolved issue and represents a significant area for further research in this field.\u003c/p\u003e\n\u003cp\u003eIn this study, \u0026ldquo;institutions\u0026rdquo; refers to all levels of governance structures, land tenure systems, policy frameworks, and infrastructure. We adopt the definition of institutions proposed by Marquardt et al. (2023): \u0026ldquo;\u003cem\u003ethe process of developing, changing and establishing new formal and informal rules and procedures in terms of politics, policies, and polity\u003c/em\u003e\u0026rdquo;. While defining institutional variables presents significant challenges, we propose a classification of institutional characteristics, such as property rights security, stakeholders\u0026rsquo; association, existence of subsidies or market efficiency between others, which have been incorporated into various models and can be compared across different contexts. In some cases, it has been complicated to isolate the effects of institutions, within Ricardian framework, due to the interconnected relationship between institutional development and economic outcomes. So, we have assumed that the presence of institutions facilitates access to essential resources such as credit, technology, and information, which can significantly improve agricultural adaptability to the impacts of climate change (Stern, 2007).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFollowing the bibliometric analysis, a total of 112 selected articles were reviewed in detail to examine the variables considered in empirical Ricardian models. The identified variables from the various articles were categorized into five groups: 1) climatic variables; 2) physical variables; 3) economic variables; 4) social variables; and 5) institutional variables.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIt should be noted that the authors classified the variables based on their own criteria. In selecting the institutional variables, we focused on three main issues (Gyimah et al., 2024): variables related to financial and/or technical support that may be associated with effective governance institutions; variables reflecting the farmers\u0026rsquo; ability to adapt to changing climate conditions (e.g., access to extension services or access to climatic information); and the presence of associations, social capital and collective action arrangements among farmers. Although some variables could be categorized in multiple ways (e.g., economic, social, and institutional), we opted for simplicity and to avoid duplication by assigning each variable to a single category based on a specific definition. The definitions and the\u0026nbsp;variables recorded in the articles are listed and classified below:\u003c/p\u003e\n\u003cp\u003e1) C\u003cem\u003elimatic variables\u003c/em\u003e: These are physical-climatic parameters that can affect land prices. They include temperatures, precipitation levels, maximum and minimum temperatures, natural disasters (heatwaves, drought, flood), water deficit, water stress, and solar radiation.\u003c/p\u003e\n\u003cp\u003e2) \u003cem\u003ePhysical variables\u003c/em\u003e: These refer to the physical characteristics of farmland that are distinct from climatic variables. They encompass soil type, soil texture, soil moisture, soil pH, soil organic carbon, soil erosion, elevation, altitude, latitude, percentage of grassland, river flow, depth to rock, land fragmentation, farmland size, distance to markets (inputs and outputs), distance to port, distance to cities, distance to provincial capital, and coastal regions.\u003c/p\u003e\n\u003cp\u003e3) \u003cem\u003eEconomic variables\u003c/em\u003e: These include economic attributes of the farmland (or the farmer) that can affect productivity. This category covers farm landholding, access to irrigation, asset value, rented farmland, livestock, crop types, labor, net revenues, GDP, village-level economic development, inputs prices, tractors per hectare, oxen per hectare, cultivator per hectare, per capita income, total horsepower, sale price, purchased water, risk perception, primary vs. secondary occupation.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e4) \u003cem\u003eSocial variables\u003c/em\u003e: These consist of sociocultural and family characteristics such as education, household size, farmers\u0026rsquo; age, farmers\u0026rsquo; experience, population density, population growth, occupational vulnerability, individual family farm, young entrepreneur, gender, marital status, electricity, migration background, urban population, housing density, hotel density, literacy percentage, vulnerability index.\u003c/p\u003e\n\u003cp\u003e5) \u003cem\u003eInstitutional variables\u003c/em\u003e: These are associated to the existence of an organization (with more or less complex rules and organizational structures) that plays a role in the way agricultural activity is performed. Additionally, this category included the availability of services and support for farmers, indicating existing institutional frameworks. The selected variables in this group include organizations membership (farmers associations), cooperative membership, access to extension services, access to livestock extension services, less favored area payments (LFA), subsidies, farms receiving biodiversity protection nature payments (Natura 2000) and/or water protection payments, agri-environmental payments, high-quality certification of crops, government surveillance, organic certification, access to climatic information, number of farms participating in government programs, land reform project, communal areas, livestock/crop research, federal or private water access and access to credit.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAll studies consistently included the first two types of variables -climatic and physical- though they vary in specific choices and combinations (Prakash et al., 2024). In contrast, socioeconomic and institutional variables were used quite differently across these studies. There was significant diversity in the economic and social variables included, reflecting the substantial disparities in socio-economic conditions among developed, developing, and underdeveloped countries[2]. While some studies incorporated a broad range of socio-economic, others included none at all.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe analysis indicated that institutional variables were rarely utilized in research, with only a few studies incorporating factors related to regulation, institutional support and/or the establishment of associations among actors. We identified 28 articles that utilized any of the 19 institutional variables we found (Table A1 in the Appendix contains a complete list of the articles included in the institutional analysis). In Table 1 we present these identified institutional variables along with details about the type of effect reported and its significance in the models. We also include information on how these variables were integrated into Ricardian analyses, the regions where these variables were applied, and the reference papers that included each variable.\u003c/p\u003e\n\u003cp\u003eTABLE 1 AROUND HERE\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e4.2. Content analysis: the relevance of institutional variables\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eBased on the analysis of 28 selected articles and a total of 84 different regressions[3] that included institutional variables, three were most frequently cited in the literature: \u0026ldquo;\u003cem\u003eaccess to extension services\u003c/em\u003e\u0026rdquo;, \u0026ldquo;\u003cem\u003eorganization membership\u003c/em\u003e\u0026rdquo; and \u0026ldquo;\u003cem\u003esubsidies\u0026rdquo;.\u003c/em\u003e Access to extension services[4] was reflected in 12 of the 28 papers; in most of these studies (7), it was treated as a numerical variable (frequency of use), while in 5 papers it was used as a dummy variable. A total of 19 models analyzed this variable. Access to extension services was associated with providing technical knowledge on climate-resilient practices, crop varieties, and/or adaptive management techniques and to help farmers to adopt new technologies to improve agricultural productivity (Di Falco et al., 2012). In the majority of the models (68%), this variable was found to be significant, demonstrating a positive effect on the dependent variable[5]. This suggests that extension services enhance farm value and improve adaptive capacity in the face of climate stress. Only one study reported a significant negative impact, indicating that this variable may have a limited capacity to mitigate the effects of climate change (Al\u0026iacute; et al., 2021). Al\u0026iacute; et al. (2021) emphasized that increased information leads to greater awareness regarding the importance of crop management. However, this enhanced awareness also complicates management and raises costs, which could potentially reduce the value of the land. Finally, three articles found that the variable was not significant. It is important to highlight that this variable, has been just included in the case of models based on low and medium-income countries, this is because these services are broadly implemented in developed countries.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eOrganization membership appeared in 6 articles featuring a total of 15 different models. This variable reflects the involvement of farmers in associations\u0026nbsp;and organizations aimed at collectively supporting and improving their activities. While one might expect this variable to enhance climate change adaptation, the results across the models were quite varied. This variable was significant in only seven articles (47%), and it had a positive impact in six of those articles. The positive effect suggests that membership in farmers\u0026rsquo; organizations can lead to greater resilience and adaptability to the impacts of climate change. On the other hand, the article that reported a negative impact may point to the costs associated with adhering to organizational rules and other constraints that can hinder the capacity for climate change adaptation. A significant number of models (53%) that did not find this variable significant raises the possibility that, despite the theoretical benefits of farmers\u0026apos; organizations, they often encounter major limitations. These may include insufficient resources and/or technical capacity within the associations, limited connections with government institutions, or challenges related to the size of the association and farmland characteristics (physical or social), which can impede the implementation of necessary measures and agreements. In the case of this variable, there is an important bias in the analysis since only 6 articles have been found that include this variable (although 15 regressions have been conducted), however, the articles focus on three countries: China, South Africa and Nigeria. Furthermore, this variable has been predominantly included as a dummy variable which could limit the nuanced understanding of institutional effects on climate-agriculture relationships.\u003c/p\u003e\n\u003cp\u003eOther institutional variables commonly included in research were \u003cem\u003e\u0026ldquo;subsidies\u0026rdquo;\u003c/em\u003e and \u003cem\u003e\u0026ldquo;access to credit\u0026rdquo;\u003c/em\u003e. Subsidies appeared in 13 models across 5 articles, predominantly as a numerical coefficient. The findings indicate that direct subsidies serve as a positive variable in most of the models (85%). This suggests that subsidies are an important tool for mitigating climate change impacts. Notably, this variable was included in models applied in developed countries, which contrasts with the inclusion of other institutional variables.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eOn the other hand, the variable \u0026ldquo;\u003cem\u003eaccess to credit\u003c/em\u003e\u0026rdquo; was analyzed in 6 articles and included in a total of 7 models. Contrary to expectations that highlights how financial services enhance resilience against the adverse effects of climate change (Hussain et al., 2021), the results showed low significance of this variable. Only 3 models reported a significant impact, with 2 indicating a negative effect and 1 yielding a positive effect. The negative impact may suggest that access to credit is more commonly sought by lower-income farmers, who may lack the capability to utilize effectively the credit for climate change adaptation efforts. Similar as the case of the extension services, this variable has been traditionally included in low-income countries due to these countries face significant structural constraints in accessing credit services, which limits their capacity to implement climate adaptation strategies. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eConcerning the variable \u0026lsquo;\u003cem\u003esubsidies,\u003c/em\u003e\u0026rsquo; we can include variables such as \u0026lsquo;\u003cem\u003efarms receiving nature payments\u003c/em\u003e\u0026rsquo;, \u0026lsquo;\u003cem\u003eless favored area payments\u003c/em\u003e\u0026rsquo; and \u0026lsquo;\u003cem\u003eagri-environmental payments\u003c/em\u003e\u0026rsquo;. These variables were examined in 4 studies and 6 different models. The articles discussing \u0026lsquo;\u003cem\u003efarms receiving nature payments\u003c/em\u003e\u0026rsquo; and \u0026lsquo;\u003cem\u003eless favored area payments\u003c/em\u003e\u0026rsquo; propose two different regressions, and the variable \u0026lsquo;\u003cem\u003eagri-environmental payments\u003c/em\u003e\u0026rsquo; is analyzed in two separate articles. Although these variables are related to the allocation of subsidies, they were targeted at very specific purposes.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe results indicated that while the subsidy variable was significant in mitigating the impacts of climate change, this was not the case for the other variables. For the variable \u0026lsquo;\u003cem\u003efarms receiving nature payments\u003c/em\u003e\u0026rsquo;, the findings showed significance in only one of the models, and the coefficient was negative, suggesting that this variable does not contribute to the adaptation or mitigation of climate change impacts.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eConversely, the variable \u0026lsquo;\u003cem\u003eless favored area payments\u003c/em\u003e\u0026rsquo;, analyzed in two different models from the same article, showed significance with differing signs (positive and negative). When the subsidy was related to payments designed for the protection of water resources or biodiversity, its influence was positive, confirming that these subsidies help mitigate the effects of climate change. However, for other types of payments (not related to these specific purposes), the variable demonstrated significant but negative results.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFinally, regarding \u0026lsquo;\u003cem\u003eagri-environmental payments\u003c/em\u003e\u0026rsquo; analysis in two different studies revealed no significance in one study and significance with a negative impact in the other. Some previous research reported that poorly designed land or environmental subsidies can inadvertently support unsustainable farming practices that degrade soil health and increase emissions, thereby adversely affecting climate change effects (Searchinger et al., 2008). Additionally, other studies suggested that the effectiveness of these subsidies in mitigating climate change effects varies significantly between countries (Roe et al., 2021).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe comprehensive analysis of the remaining institutional variables included in the models, which were featured in only one study, revealed a significant positive effect for several factors: \u0026lsquo;\u003cem\u003eLivestock/crop research\u003c/em\u003e\u0026rsquo;, \u0026lsquo;\u003cem\u003eNumber of farms participating in government programs\u003c/em\u003e\u0026rsquo;, \u0026lsquo;\u003cem\u003ePrivate/public water access\u003c/em\u003e\u0026rsquo;, \u0026lsquo;\u003cem\u003eLegal form\u003c/em\u003e\u0026rsquo;, and \u0026ldquo;\u003cem\u003eOther certification\u003c/em\u003e\u0026rdquo; (which encompass designations of origin and regional certificates for products other than organic). These findings indicated that certain services, such as water access and those that disseminate technical and research results (including certifications and information regarding crops and livestock), enhanced stakeholders\u0026rsquo; ability to adapt to change.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFinally, variables associated with effective institutional governance, such as \u0026lsquo;\u003cem\u003eNumber of farms participating in government programs\u003c/em\u003e\u0026rsquo; or \u0026lsquo;\u003cem\u003eLegal form\u003c/em\u003e\u0026rsquo;, also played a crucial role in mitigating the negative effects of climate change. Conversely, other variables, including \u0026lsquo;\u003cem\u003eGovernment surveillance\u003c/em\u003e\u0026rsquo;, \u0026lsquo;\u003cem\u003eLand reform project\u003c/em\u003e\u0026rsquo; (for all and mixed farmers models), \u0026lsquo;\u003cem\u003eHigh-quality certification of crops\u003c/em\u003e\u0026rsquo;, \u0026lsquo;\u003cem\u003eAccess to climatic information\u003c/em\u003e\u0026rsquo;, and \u0026lsquo;\u003cem\u003eCommunal areas\u003c/em\u003e\u0026rsquo; (for crop and mixed farmer models) were not found to be significant. While we would expect some of these variables to have a positive and significant impact, the results were inconclusive. It is noteworthy to indicate that these results are just based on the results in a unique article, and several times in a unique regression.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e4.3. Discussion and policy implications\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe results of the institutional analysis shed light on the effectiveness of organizations and rules in supporting agricultural activities in the face of changing climatic conditions. Our outcomes show a growing interest in using Ricardian models to assess the effects of climate change, particularly in the agricultural sector. This increased awareness is driven by the understanding that climate change poses a serious threat to global food security. The rise in extreme natural events and significant shifts in climate patterns are disrupting agricultural systems, leading to decreases in crop yields, livestock productivity, and the food supply infrastructure (Shamshad et al., 2024). Furthermore, economic valuations of the impacts of climate change on agriculture are essential for informing more effective and targeted policy responses.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eRicardian models have been refined over the years, incorporating methodological improvements as well as new variables and interactions among them, including climatic, physical, economic and social factors. Additionally, the scope of analysis has broadened to include studies in developed, developing, and underdeveloped countries, with some research making comparisons between these groups. However, despite these advancements, many models still overlook the inclusion of institutional and governance variables. Our findings showed that only 13% of the identified publications included at least one variable related to institutional issues, despite the analysis revealed that models including institutional variables focused on both developing and developed regions, the variables analyzed are different between these groups.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eDespite the previously mentioned limitations, the outcomes indicate that institutional variables were generally significant and had a positive impact. This suggests that effective governance, organized institutions, cooperation, active stakeholder involvement, and the availability of technical and knowledge services play important roles in mitigating the negative effects of climate change. Our analysis revealed that there were almost no variables with a significant negative impact.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eNotably, the implementation of subsidies in the agricultural sector stands out; while this variable was extensively tested in high-income countries, it was not explored as thoroughly in low or medium-income countries. Another relevant result is that the effectiveness of subsidies was largely dependent on their proper definition and implementation; if not executed well, they could inadvertently worsen climate change impacts. Subsidies aimed at specific environmental goals showed a negative impact on the models. This indicates that not all subsidies actually enhance the resilience of farms. Conversely, subsidies related to water access and usage demonstrated a clear positive effect. This implies a close relationship between climate and institutional factors concerning water resources, suggesting that policies should focus on improving the management capacity to this vital resource.\u003c/p\u003e\n\u003cp\u003eOur findings also underscore the strong positive influence of associations and collective action. These groups enable communities to pool resources, share knowledge, and coordinate efforts toward sustainable practices, which in turn facilitates the implementation of mitigation strategies (Ostrom, 1990). This evidence suggests that climate change mitigation policies should prioritize the support of farmers\u0026apos; organizations, strengthen their connections with different levels of government, offer technical assistance via extension services, and disseminate research findings.\u003c/p\u003e\n\u003cp\u003eThe exhaustive qualitative analysis of existing literature highlights several areas for improvement in the models\u0026rsquo; explanatory power. Enhancing this explanatory capacity could be achieved by examining the correlation between climate factors and institutional variables. In this context, it would be valuable to explore the impact of regulations and policies aimed to improving soil quality and promoting conservation practices that reduce erosion. Finally, in order to compare and analyze the impact of institutional variables, it is necessary to study them across a broader range of regions to enable meaningful comparisons. In this regard, it is also essential that the variables used are reasonably homogeneous and capture similar attributes (e.g., institutional quality or services provided).\u003c/p\u003e"},{"header":"5. Conclusions and future work","content":"\u003cp\u003eEconomies around the world must pursue policies to adapt to and mitigate the effects of climate change. However, assessing these impacts is particularly challenging due to the complexity of climate systems and ecosystems, uncertainties in climate models and projections, and difficulties in evaluating economic consequences (IPCC, 2022). In recent years, various tools have been developed to measure the economic impact of climate change damage globally. The economic assessment of climate change effects has mainly relied on Ricardian approaches since the foundational work of Mendelsohn et al. (1994), which introduced a framework for examining how land values are influenced by climate conditions. While this method has become widely used in estimating climate impacts, its consideration of institutional variables is notably underdeveloped, leading to potentially biased estimates and incomplete policy recommendations.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThis study reviewed literature on the application of Ricardian models to assess the impacts of climate change. The main objective was to analyze how institutional variables are integrated into these models and their effect on the primary sector\u0026rsquo;s adaptation to and mitigation of climate change impacts. A total of 203 studies focusing on Ricardian models for assessing climate change were identified in the WoS. However, after an in-depth analysis, only 28 articles included institutional elements in their regressions. We defined institutional variables as factors related to stakeholder collaboration or association, the availability of supporting services (such as extension services), and the presence of effective government or organizations (e.g., the existence of subsidies). \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe results of nearly all models showed a predominantly positive effect of institutional variables on land prices. This suggests that effective institutions can help mitigate the impacts of climate change. However, different crops and regions respond to climate change in diverse ways and with varying levels of resilience (Migliore et al., 2019). The ability of stakeholders and the supporting elements to adapt their practices is crucial for reducing negative impacts.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe findings of the study indicate that institutional variables are infrequently incorporated into these models. This is likely due to the challenges in defining such variables, the significant differences between countries, and a lack of available data and information. Nevertheless, these limitations need to be addressed, as many of these variables were integrated into models that apply to low and medium-income countries. Including institutional variables poses challenges because of the complexity and diversity of governance systems across regions. Additionally, data on institutions is often incomplete or inconsistent. Despite this, the impact of these variables on mitigating climate change has been widely documented. These issues present a limitation for a comprehensive analysis of the role of institutional and effective governance variables.\u003c/p\u003e\n\u003cp\u003eThe potential for future research based on this work is broad. First, it is necessary to further examine which institutional variables most effectively facilitate adaptation to climate change. To enhance adaptation policy decisions in the agricultural sector, we need to shift from descriptive analyses to quantitative ones by incorporating econometric techniques that can rank the relative importance of these institutional variables. Additionally, an important factor not yet included in the models is the role of some institutions with a long history of cooperation among farmers (i.e. irrigation associations), in their capacity to adapt to climate change.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research has been funded by projects PID2020-115495RA-I00 from the Spanish Ministry of Science and Innovation.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eEthics approval and consent to participate:\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u0026lsquo;Not applicable\u0026rsquo;\u003c/p\u003e\n\u003cp\u003eOur manuscript does not report or involve animals, humans, human data, human tissue or plants.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eConsent for publication:\u003c/em\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u0026lsquo;Not applicable\u0026rsquo;\u003c/p\u003e\n\u003cp\u003eOur manuscript does not contain any individual or personal data.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eCompeting Interests:\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eAuthors declare no competing interests, both financial and non-financial.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eAuthor contributions:\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eAll the authors (Encarna Esteban, Yolanda Mart\u0026iacute;nez and Sara Calvo) have equally contributed in the methodology, elaboration and revision of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eFunding:\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThis research has been funded by projects PID2020-115495RA-I00 from the Spanish Ministry of Science and Innovation.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eAvailability of data and materials:\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u0026lsquo;Not applicable\u0026rsquo;\u003c/p\u003e\n\u003cp\u003eThe data used in our manuscript are free access in research webs. Anyway, any information regarding the documents used is fully available and the authors could provide any necessary information upon request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAbidoye, Babatunde O.; Kurukulasuriya, Pradeep; Mendelsohn, Robert 2017. Structural Ricardian analysis of South-East Asian Agriculture. Climate Change Economics, Vol. 8, No. 3, pp:1-8.\u003c/li\u003e\n\u003cli\u003eAl\u0026iacute; U., Wang J., Ullah A., Ishtiaque A., Javed T., Nurgazina Z. 2021. The impact of climate change on the economic perspectives of crop farming in Pakistan: Using the Ricardian model. Journal of Cleaner Production, 308: 127219.\u003c/li\u003e\n\u003cli\u003eAmouzay, H. and and El Ghini, A. 2024. A Systematic Review of Key Spatial Econometric Models for Assessing Climate Change Impacts on Agriculture. MPRA (Munich Personal RePEc Archive), Paper No. 123222, posted 13 Jan 2025.\u003c/li\u003e\n\u003cli\u003eBaylie, Melese Mulu; Fogarassy, Csaba 2021. Examining the Economic Impacts of Climate Change on Net Crop Income in the Ethiopian Nile Basin: A Ricardian Fixed Effect Approach, Sustainability 13, 7243.\u003c/li\u003e\n\u003cli\u003eChatzopoulos, Thomas; Lippert, Christian 2015. Adaptation and Climate Change Impacts: A Structural Ricardian Analysis of Farm Types in Germany. Journal of agricultural economics, Vol. 66, No. 2: 537\u0026ndash;554.\u003c/li\u003e\n\u003cli\u003eChatzopoulos, Thomas; Lippert, Christian 2016. Endogenous farm-type selection, endogenous irrigation, and spatial effects in Ricardian models of climate change. European Review of Agricultural Economics 43 (2):217-235.\u003c/li\u003e\n\u003cli\u003eDe Salvo, Maria; Raffaelli, Roberta; Moser, Riccarda 2013. The impact of climate change on permanent crops in an Alpine region: A Ricardian analysis. Agricultural systems, 118: 23-32.\u003c/li\u003e\n\u003cli\u003eDell\u0026rsquo;Angelo J., P. D\u0026rsquo;Odorico, M.C. Rulli, P. Marchand 2017. The tragedy of the grabbed commons: coercion and dispossession in the global land rush, World Development, 92: 1-12.\u003c/li\u003e\n\u003cli\u003eDi Falco, Salvatore; Yesuf, Mahmud; Kohlin, Gunnar; Ringler, Claudia 2012. Estimating the Impact of Climate Change on Agriculture in Low-Income Countries: Household Level Evidence from the Nile Basin, Ethiopia. Environmental \u0026amp; Resource economics 52: 457-478.\u003c/li\u003e\n\u003cli\u003eDubash, N.K. 2010. Viewpoint \u0026ndash; Reflections on the WCD as a mechanism of global governance. Water Alternatives 3(2): 416-422\u003c/li\u003e\n\u003cli\u003eElum, Z. A.; Nhamo, G.; Antwi, M. A. 2018. Effects of climate variability and insurance adoption on crop production in select provinces of South Africa. Journal of water and climate change 09.3: 500-511.\u003c/li\u003e\n\u003cli\u003eEuropean Environmental Agency 2024. Economic losses from weather-and climate-related extremes in Europe. Published 14 October 2024. https://www.eea.europa.eu/en/analysis/indicators/economic-losses-from-climate-related?activeAccordion=ecdb3bcf-bbe9-4978-b5cf-0b136399d9f8#ref-RBnwU\u003c/li\u003e\n\u003cli\u003eFabri C., Moretti M., Van Passel S. 2022. On the (ir)relevance of heatwaves in climate change impacts on European agriculture. Climatic Change, 174:16.\u003c/li\u003e\n\u003cli\u003eFAO. 2018. The future of food and agriculture \u0026ndash; Alternative pathways to 2050. Rome. 224 pp. Licence: CC BY-NC-SA 3.0 IGO \u003c/li\u003e\n\u003cli\u003eFeng, Xiaolong; Qiu, Huanguang; Pan, Jie; Tang, Jianjun 2021. The impact of climate change on livestock production in pastoral areas of China Science of the total environment, 770: 144838.\u003c/li\u003e\n\u003cli\u003eFezzi C. \u0026amp; Bateman I. 2015. The Impact of Climate Change on Agriculture: Nonlinear Effects and Aggregation Bias in Ricardian Models of Farmland Values. Journal of the Association of Environmental and Resource Economists, Vol. 2, No. 1, pp. 57-92\u003c/li\u003e\n\u003cli\u003eGadedjisso-Tossou, Agossou; Egbendewe, Aklesso Y. G.; Abbey, Georges A. 2016. Assessing the impact of climate change on smallholder farmers\u0026apos; crop net revenue in Togo. Journal of agriculture and environment for international development, 110 (2): 229-248.\u003c/li\u003e\n\u003cli\u003eGebreegziabher, Z., Mekonnen, A., Deribe, R., Abera, S., and Kassahun, M.M. 2013. \u0026lsquo;Crop\u0026ndash;livestock inter-linkages and climate change implications for Ethiopia\u0026apos;s agriculture: a Ricardian approach\u0026rsquo;, RFF Discussion Paper Series No. EfD 13\u0026ndash;14, Resources for the Future, Washington, DC.\u003c/li\u003e\n\u003cli\u003eGebreegziabher, Zenebe; Stage, Jesper; Mekonnen, Alemu; Alemu, Atlaw. 2016. Climate change and the Ethiopian economy: a CGE analysis. Environment and Development Economics 21 (2): 205-225.\u003c/li\u003e\n\u003cli\u003eGonz\u0026aacute;lez U., Jorge; Velasco H., Roberto 2008. Evaluation of the impact of climatic change on the economic value of land in agricultural systems in Chile. Chilean journal of agricultural research, 68(1):56-68.\u003c/li\u003e\n\u003cli\u003eGyimah, J., Hayford, I.S., Nyantakyi, G., Adu, P.S., Batasuma, S., Yao, X. 2024. The era of global warming mitigation: The role of financial inclusion, globalization and governance institutions. Heliyon 10(1): e23471.\u003c/li\u003e\n\u003cli\u003eHossain M.S., Muhammad A., Lu Q., Minjuan Z., Yasir M., Harald K., 2019. Economic impact of climate change on crop farming in Bangladesh: An application of Ricardian method. Ecological Economics, 164: 106354. \u003c/li\u003e\n\u003cli\u003eHussain, A.H.M.B., Islam, M., Ahmed, K.J., Haq, S.M.A., Islam, M.N. 2021. Financial Inclusion, Financial Resilience, and Climate Change Resilience. In: Luetz, J.M., Ayal, D. (eds) Handbook of Climate Change Management. Springer, Cham. \u003c/li\u003e\n\u003cli\u003eIPCC (Intergovernmental Panel on Climate Change), 2007. Contribution of Working Group I to the Fourth Assessment Report. Cambridge University Press, Cambridge, UK \u003c/li\u003e\n\u003cli\u003eIPCC (Intergovernmental Panel on Climate Change), 2018. Proposed Outline of the Special Report in 2018 on the Impacts of Global Warming of 1.5 ◦C above Pre-Industrial Levels and Related Global Greenhouse Gas Emission Pathways, in the Context of Strengthening the Global Response to the Threat of Climate Change. 2018. \u003c/li\u003e\n\u003cli\u003eIPCC (Intergovernmental Panel on Climate Change), 2022. Climate Change 2022: Impacts, Adaptation and Vulnerability. Contribution of Working Group II to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change, H. P\u0026ouml;rtner, D. Roberts, M. Tignor et al. (eds.), Cambridge: Cambridge University Press.\u003c/li\u003e\n\u003cli\u003eKurukulasuriya P. \u0026amp; Mendelsohn R. 2006. A Ricardian analysis of the impact of climate change on African cropland. CEEPA Discussion Paper No. 8, Special Series on Climate Change and Agriculture in Africa.\u003c/li\u003e\n\u003cli\u003eKurukulasuriya, Pradeep; Ajwad, Mohamed Ihsan 2007. Application of the Ricardian technique to estimate the impact of climate change on smallholder farming in Sri Lanka. Climatic Change 81: 39-59.\u003c/li\u003e\n\u003cli\u003eMarquardt Jens, Anna F\u0026uuml;nfgeld, Joshua Philipp Els\u0026auml;sser, 2023. Institutionalizing climate change mitigation in the Global South: Current trends and future research, Earth System Governance, Volume 15, 100163,\u003c/li\u003e\n\u003cli\u003eMassetti E. and Mendelsohn R. 2020. Temperature thresholds and the effect of warming on American farmland value. Climatic Change, 161:601\u0026ndash;615.\u003c/li\u003e\n\u003cli\u003eMendelsohn, R., Nordhaus, W., Shaw, D., 1994. The impact of global warming on agriculture: a Ricardian analysis. American Economic Review 84, 753\u0026ndash;771.\u003c/li\u003e\n\u003cli\u003eMendelsohn, Robert; Basist, Alan; Dinar, Ariel; Kurukulasuriya, Pradeep; Williams, Claude. 2007. What explains agricultural performance: climate normals or climate variance? Climatic change, 81:85\u0026ndash;99.\u003c/li\u003e\n\u003cli\u003eMendelsohn \u0026amp; Dinar, 2009. Land Use and Climate Change Interactions. Annual Review and Resource Economics, 1:309\u0026ndash;32.\u003c/li\u003e\n\u003cli\u003eMendelsohn, Robert; Arellano-Gonzalez, Jesus; Christensen, Peter 2010. A Ricardian analysis of Mexican farms. Environment and Development Economics 15(2): 153-171.\u003c/li\u003e\n\u003cli\u003eMendelsohn \u0026amp; Massetti, 2017. The Use of Cross-Sectional Analysis to Measure Climate Impacts on Agriculture: Theory and Evidence. Review of Environmental Economics and Policy, volume 11, issue 2, pp. 280\u0026ndash;298.\u003c/li\u003e\n\u003cli\u003eMigliore G., Zinnanti C., Schimmenti E., Borsellino V., Schifani G., Di Franco C.P., Asciuto A. 2019. A Ricardian analysis of the impact of climate change on permanent crops in a Mediterranean region. New Medit, 1.\u003c/li\u003e\n\u003cli\u003eMoher D, Liberati A, Tetzlaff J, Altman D G. 2009. Preferred reporting items for systematic reviews and meta-analyses: the PRISMA statement BMJ 2009; 339 :b2535.\u003c/li\u003e\n\u003cli\u003eMolua, Ernest L. 2009. An empirical assessment of the impact of climate change on smallholder agriculture in Cameroon. Global and planetary change, 67:205-208.\u003c/li\u003e\n\u003cli\u003eMoretti, Michele; Vanschoenwinkel, Janka; Van Passel, Steven 2021. Accounting for externalities in cross-sectional economic models of climate change impacts. Ecological Economics, 185: 107058.\u003c/li\u003e\n\u003cli\u003eNyuor, Anslem Bawayelaazaa; Donkor, Emmanuel; Aidoo, Robert; Buah, Samuel Saaka; Naab, Jesse B.; Nutsugah, Stephen K.; Bayala, Jules; Zougmore, Robert. 2016. Economic Impacts of Climate Change on Cereal Production: Implications for Sustainable Agriculture in Northern Ghana. SUSTAINABILITY, 8: 724.\u003c/li\u003e\n\u003cli\u003eOjo, T. O.; Baiyegunhi, L. J. S. 2021. Climate change perception and its impact on net farm income of smallholder rice farmers in South-West, NigeriaJournal of cleaner production\u003c/li\u003e\n\u003cli\u003eOnyekuru, NwaJesus Anthony; Marchant, Rob. 2016. Assessing the economic impact of climate change on forest resource use in Nigeria: A Ricardian approach. Agricultural and forest meteorology, 220: 10-20.\u003c/li\u003e\n\u003cli\u003eOrtiz-Bobea A. 2020. The role of nonfarm influences in Ricardian estimates of climate change impacts on US agriculture. American Journal Agricultural Economics 102(3): 934\u0026ndash;959.\u003c/li\u003e\n\u003cli\u003eOstrom Elinor 1990. Governing the commons: the evolution of institutions for collective action. Cambridge University Press, 1990.\u003c/li\u003e\n\u003cli\u003ePage M J, Moher D, Bossuyt P M, Boutron I, Hoffmann T C, Mulrow C D et al. 2021. PRISMA 2020 explanation and elaboration: updated guidance and exemplars for reporting systematic reviews BMJ 2021; 372 :n160\u003c/li\u003e\n\u003cli\u003ePolsky, C; Easterling, WE. 2001. Adaptation to climate variability and change in the US Great Plains: A multi-scale analysis of Ricardian climate sensitivities Agriculture ecosystems \u0026amp; environment, 85:133-144.\u003c/li\u003e\n\u003cli\u003ePrakash, D., Nemati, M., Dinar, A., Struthers, C., Mackenzie, S., and Shugart, M.S. 2024. Advancements to the Ricardian analysis in the past quarter of the century. Climate Change Economics 2024, 15:03\u003c/li\u003e\n\u003cli\u003eRoe, S., Streck, C., Beach, R., Busch, J., Chapman, M., Daioglou, V., Deppermann, A., Doelman, J., Emmet-Booth, J., Engelmann, J., Fricko, O., Frischmann, C., Funk, J., Grassi, G., Griscom, B., Havlik, P., Hanssen, S., Humpen\u0026ouml;der, F., Landholm, D., \u0026hellip; Lawrence, D. 2021. Land-based measures to mitigate climate change: Potential and feasibility by country. Global Change Biology, 27, 6025\u0026ndash;6058. \u003c/li\u003e\n\u003cli\u003eShamshad, J., Nawaz, A.F., Khan, M.B., Arif, M. 2024. Climate Change and Food Security. In: Fahad, S., Saud, S., Nawaz, T., Gu, L., Ahmad, M., Zhou, R. (eds) Environment, Climate, Plant and Vegetation Growth. Springer, Cham. \u003c/li\u003e\n\u003cli\u003eSearchinger Timothy, Ralph Heimlich, R. A. Houghton, Fengxia Dong, Amani Elobeid, Jacinto Fabiosa, Simla Tokgoz, Dermot Hayes, and Tun-Hsiang Yu 2008. Use of U.S. croplands for biofuels increases greenhouse gases through emissions from land-use change. Science, 319(5867), 1238-1240.\u003c/li\u003e\n\u003cli\u003eSchlenker, Wolfram; Hanemann, W. Michael; Fisher, Anthony C. 2007. Water availability, degree days, and the potential impact of climate change on irrigated agriculture in California. Climatic change.\u003c/li\u003e\n\u003cli\u003eSeo S.N., Mendelsohn R., Munasinghe M. 2005. Climate change and agriculture in Sri Lanka: a Ricardian valuation. Environment and Development Economics 10: 581\u0026ndash;596.\u003c/li\u003e\n\u003cli\u003eStern N. 2007 The economics of climate change: the stern review. Cambridge University Press, Cambridge.\u003c/li\u003e\n\u003cli\u003eSu X.S. \u0026amp; Chen M. 2022. Econometric Approaches That Consider Farmers\u0026rsquo; Adaptation in Estimating the Impacts of Climate Change on Agriculture: A Review. Sustainability, 14, 13700.\u003c/li\u003e\n\u003cli\u003eSwiss Re Institute 2021. The economics of climate change: no action not an option. April, 2021. \u003c/li\u003e\n\u003cli\u003eTibesigwa, Byela; Visser, Martine; Turpie, Jane 2015. The impact of climate change on net revenue and food adequacy of subsistence farming households in South Africa. Environment and development economics, 20: 327\u0026ndash;353.\u003c/li\u003e\n\u003cli\u003eTibesigwa, Byela; Visser, Martine; Turpie, Jane 2017. Climate change and South Africa\u0026apos;s commercial farms: an assessment of impacts on specialised horticulture, crop, livestock and mixed farming systems. Environment development and sustainability 19:607\u0026ndash;636. \u003c/li\u003e\n\u003cli\u003eTimmins, C. 2006. Endogenous land use and the Ricardian valuation of climate change 2006. Environmental \u0026amp; resource economics, 33: 119\u0026ndash;142.\u003c/li\u003e\n\u003cli\u003eVanschoenwinkel, Janka; Mendelsohn, Robert; Van Passel, Steven 2016. Do Western and Eastern Europe have the same agricultural climate response? Taking adaptive capacity into account. Global environmental change-human and policy dimensions, 41: 74-87.\u003c/li\u003e\n\u003cli\u003eWang, Jinxia; Mendelsohn, Robert; Dinar, Ariel; Huang, Jikun; Rozelle, Scott; Zhang, Lijuan 2009. The impact of climate change on China\u0026apos;s agriculture Agricultural economics, 40: 323\u0026ndash;337.\u003c/li\u003e\n\u003cli\u003eWang, Jinxia; Huang, Jikun; Zhang, Lijuan; Li, Yumin. 2014. Impacts of climate change on net crop revenue in North and South China. China agricultural economic review, Vol. 6 No. 3: 358-378.\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Footnotes","content":"\u003cp\u003e[1] Note that a study\u0026rsquo;s country of origin corresponds to the location of the first author\u0026rsquo;s affiliated institution. Therefore, this categorization is approximate, given that the majority of articles are authored by multiple individuals with typically diverse affiliations.\u003c/p\u003e\n\u003cp\u003e[2] For example, variables like access to electricity, illiteracy rate and number of oxen are only meaningful in certain countries/areas.\u003c/p\u003e\n\u003cp\u003e[3] See Table A1 in the Annex for a complete list of articles, used methodologies and variables.\u003c/p\u003e\n\u003cp\u003e[4] We include two variables such as extension services for farming and livestock activities\u003c/p\u003e\n\u003cp\u003e[5] The endogenous variable is either the land value or the farmer\u0026apos;s farm income depending on the research article.\u0026nbsp;\u003c/p\u003e"},{"header":"Tables","content":"\u003cp\u003e\u003cstrong\u003eTable 1.\u003c/strong\u003e Identification of institutional variables in the articles reviewed\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"680\" class=\"fr-table-selection-hover\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e\u003cem\u003eVariable\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e\u003cem\u003eType of variable\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 198px;\"\u003e\n \u003cp\u003e\u003cem\u003eType of effect (significance)\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e\u003cem\u003eRegion\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003e\u003cem\u003eReference\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"6\" valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003eOrganizations membership (farmers associations)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003eDummy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 198px;\"\u003e\n \u003cp\u003eNegative (no significant)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003eSouth Africa\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003eElum et al. (2018)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003eDummy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 198px;\"\u003e\n \u003cp\u003ePositive (significant)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003eNigeria\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003eOjo \u0026amp; Baiyegunhi (2021)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003eNumerical (5 models)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 198px;\"\u003e\n \u003cp\u003ePositive (no significant) in models for all farms and mixed farms\u003c/p\u003e\n \u003cp\u003eNegative (no significant) in models crop farmers, horticulture and livestock farms.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003eSouth Africa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003eTibesigwa et al. (2017)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003eDummy (3 models)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 198px;\"\u003e\n \u003cp\u003ePositive (significant) in models all farms and irrigated farms\u003c/p\u003e\n \u003cp\u003eNegative (significant) in rainfed farms\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003eChina\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003eWang et al. (2014)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003eDummy (4 models)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 198px;\"\u003e\n \u003cp\u003ePositive (significant) in all farms, irrigated, irrigated or rainfed.\u003c/p\u003e\n \u003cp\u003eNegative (no significant) in rainfed farms\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003eChina\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003eWang et al. (2009)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003eDummy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 198px;\"\u003e\n \u003cp\u003eNo significant\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003eChina\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003eFeng et al. (2021)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003eNumber of farms participating in government programs\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003eNumerical\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 198px;\"\u003e\n \u003cp\u003ePositive (significant)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003eUSA\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003ePolsky \u0026amp; Easterling (2001)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"6\" valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003eAccess to credit\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003eNumerical coefficient\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 198px;\"\u003e\n \u003cp\u003eNegative (no significant)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003ePakistan\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003eAli et al. (2021)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003eDummy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 198px;\"\u003e\n \u003cp\u003eNegative (no significant)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003eSouth Africa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003eElum et al. (2018)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003eNumerical\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 198px;\"\u003e\n \u003cp\u003eNegative (significant)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003eMexico and USA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003eMendelsohn et al. (2010)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003eDummy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 198px;\"\u003e\n \u003cp\u003eNegative (significant)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003eEthiopia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003eGebreegziabher et al (2013)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003eDummy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 198px;\"\u003e\n \u003cp\u003ePositive (no significant)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003eBangladesh\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003eHossain et al. (2019)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003eDummy (2 models)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 198px;\"\u003e\n \u003cp\u003ePositive (significant) for corn model\u003c/p\u003e\n \u003cp\u003eNegative (no significant) for sorgum model\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003eGhana\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003eNyuor et al. (2016)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"10\" valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003eAccess to extension services\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003eNumerical\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 198px;\"\u003e\n \u003cp\u003eNegative (significant)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003ePakistan\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003eAli et al. (2021)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003eNumerical\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 198px;\"\u003e\n \u003cp\u003ePositive (significant)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003eTogo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003eGadedjisso-Tossou et al. (2016)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003eDummy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 198px;\"\u003e\n \u003cp\u003ePositive (significant)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003eEthiopia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003eGebreegziabher et al (2016)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003eNumerical\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 198px;\"\u003e\n \u003cp\u003ePositive (significant)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003eChile\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003eGonz\u0026aacute;lez and Velasco (2008)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003eDummy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 198px;\"\u003e\n \u003cp\u003ePositive (significant)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003eBangladesh\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003eHossain et al. (2019)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003eDummy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 198px;\"\u003e\n \u003cp\u003ePositive (significant)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003eCameroon\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003eMolua (2009)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003eNumerical\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 198px;\"\u003e\n \u003cp\u003eNegative (no significant)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003eGhana\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003eNyour et al. (2016)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003eDummy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 198px;\"\u003e\n \u003cp\u003eNegative (no significant)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003eNigeria\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003eOjo \u0026amp; Baiyegunhi (2021)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003eNumerical\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 198px;\"\u003e\n \u003cp\u003e(no significant not included in the model)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003eNigeria\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003eOnyekuru \u0026amp; Marchant (2016)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003eNumerical (4 models)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 198px;\"\u003e\n \u003cp\u003ePositive (significant) in model all farmers\u003c/p\u003e\n \u003cp\u003eNegative (not significant) in mixed and crop farmers models\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003eSouth Africa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003eTibesigwa et al. (2015)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003eAccess to livestock extension services\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003eNumerical (5 models)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 198px;\"\u003e\n \u003cp\u003ePositive (significant) in all models\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003eSouth Africa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003eTibesigwa et al. (2015)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003eDummy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 198px;\"\u003e\n \u003cp\u003ePositive (significant)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003eEthiopia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003eGebreegziabher et al (2016)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"5\" valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003eSubsidies\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003eDummy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 198px;\"\u003e\n \u003cp\u003eNegative (no significant)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003eSri Lanka\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003eKurukulasuriya \u0026amp; Ajw (2007)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003eNumerical (3 models)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 198px;\"\u003e\n \u003cp\u003ePositive (significant)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003eUSA \u0026amp; Canada\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003eMendelsohn et al. (2007)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003eNumerical (3 models)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 198px;\"\u003e\n \u003cp\u003ePositive (significant)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003eEurope\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003eMoretti et al. (2021)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003eNumerical (4 models)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 198px;\"\u003e\n \u003cp\u003ePositive (significant) 3 models, Negative (no significant) 1 model\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003eEurope\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003eVanschoenwinkel et al. (2016)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003eNumerical (2 models)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 198px;\"\u003e\n \u003cp\u003ePositive (significant)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003eEurope\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003eFabri et al. (2022)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003eFarms receiving nature (Natura 2000) and/or water protection payments\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003eDummy (2 models)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 198px;\"\u003e\n \u003cp\u003eNegative (significant for apple)\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eNegative (not significant for grape)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003eItaly\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003eChatzopoulos \u0026amp; Lippert (2016)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003eLess favored area payments (LFA)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003eNumerical (2 models)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 198px;\"\u003e\n \u003cp\u003ePositive (significant) for nature and water protection\u003c/p\u003e\n \u003cp\u003eNegative (significant) for other payments\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003eGermany\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003eChatzopoulos \u0026amp; Lippert (2015)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003eAgri-environmental payments\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003eDummy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 198px;\"\u003e\n \u003cp\u003eNegative (no significant)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003eNigeria\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003eOjo \u0026amp; Baiyegunhi (2021)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003eNumerical\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 198px;\"\u003e\n \u003cp\u003eNegative (significant)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003eItaly\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003eChatzopoulos \u0026amp; Lippert (2016)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003eHigh-quality certification of crops\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003eDummy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 198px;\"\u003e\n \u003cp\u003eNegative (significant) for apple certification\u003c/p\u003e\n \u003cp\u003eNegative (no significant) for grape certification\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003eItaly\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003eDe Salvo et al. (2013)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003eGovernment surveillance\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003eDummy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 198px;\"\u003e\n \u003cp\u003eNegative (no significant)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003eChina\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003eFeng et al. (2021)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003eOrganic certification/other certification\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003eDummy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 198px;\"\u003e\n \u003cp\u003ePositive (no significant) for organic certification; Positive (significant) for other certification\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003eItaly\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003eMigliore et al. (2019)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003eAccess to climatic information/notice of climate change\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003eDummy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 198px;\"\u003e\n \u003cp\u003eNegative (no significant)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003eNigeria\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003eOjo \u0026amp; Baiyegunhi (2021)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003eDummy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 198px;\"\u003e\n \u003cp\u003eNegative (significant)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003eNigeria\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003eOnyekuru \u0026amp; Marchant (2016)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003ePrivate vs. public water access\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003eNumerical\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 198px;\"\u003e\n \u003cp\u003ePositive (significant)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003eUSA (California)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003eSchlenker et al. (2007)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003eLand reform project\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003eNumerical (4 models)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 198px;\"\u003e\n \u003cp\u003eNegative (no significant) for all and mixed farmers, negative (significant) for crop farmers\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003eSouth Africa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003eTibesigwa et al. (2015)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003eLivestock/crop research\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003eNumerical (4 models)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 198px;\"\u003e\n \u003cp\u003ePositive (significant)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003eBrazil\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003eTimmins (2006)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003eLand equity share scheme\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003eNumerical (4 models)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 198px;\"\u003e\n \u003cp\u003ePositive (no significant) for all farmers\u0026rsquo; model; negative (no significant) for mixed farmers model\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003eSouth Africa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003eTibesigwa et al. (2015)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003eCommunal areas\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003eNumerical (4 models)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 198px;\"\u003e\n \u003cp\u003ePositive (no significant) for all farmers\u003c/p\u003e\n \u003cp\u003eNegative (no significant) for crop farmers and mixed farmers models\u003c/p\u003e\n \u003cp\u003ePositive (significant) for livestock farmer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003eSouth Africa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003eTibesigwa et al. (2015)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003eLegal form\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003eDummy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 198px;\"\u003e\n \u003cp\u003ePositive (significant)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003eItaly\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003eMigliore et al. (2019)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Ricardian models, climate change, agriculture, institutional variables, political variables","lastPublishedDoi":"10.21203/rs.3.rs-7150109/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7150109/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"The climate crisis represents one of humanity’s greatest challenges. The world’s GDP could shrink by as much as 14% by the mid-21st century, particularly affecting the agriculture sector, which is among the most vulnerable. To effectively mitigate climate impacts, assessing them from an economic perspective and examining how institutions and political systems influence societal and economic adaptability is essential. Ricardian models (Mendelson et al., 1994), which incorporate many climatic, physical, and socio-economic variables, have been used to evaluate the impacts of climate change on agriculture. However, these analyses have traditionally excluded political and/or institutional variables. This field requires a comprehensive study on how these variables shape the future impacts of climate change and how they affect the ability to mitigate and adapt to those impacts. Along this paper we document the systematic weaknesses in how existing models treat institutional variables, drawing on examples from both developed and developing country contexts. Our results demonstrate how improved treatment of institutions can enhance both the explanatory power of Ricardian models and their relevance for climate policy design.","manuscriptTitle":"Climate change impact assessment: the role of institutional variables","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-08-19 13:42:04","doi":"10.21203/rs.3.rs-7150109/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"f4e80aed-095c-42dd-ae91-d97972575407","owner":[],"postedDate":"August 19th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-09-18T17:54:00+00:00","versionOfRecord":[],"versionCreatedAt":"2025-08-19 13:42:04","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7150109","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7150109","identity":"rs-7150109","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}