Contract attributes are strong motivators for eliciting farmers’ preferences for a particular agri-environmental scheme. Our study aims to conduct a systematic literature review to highlight the attributes used in choice experiment studies of agri-environmental schemes using the PRISMA framework. We obtained 34 studies for an in-depth review, through which we extracted 32 attributes that were classified into five typologies: ‘monetary’ (7 attributes), ‘general’ (4 attributes), ‘flexibility’ (6 attributes), ‘prescription’ (12 attributes), and ‘purpose’ (3 attributes). Though monetary attributes should theoretically define farmers’ choices; general design and flexibility attributes are more critical for farmers’ participation and willingness to accept. The study also discusses the lesser-used attributes that could be potentially explored in future studies. Thus, our review can be used as a reference by future AES studies to select their bundle of choice attributes and test with a broader range of attributes in their choice experiments.

A large share of the Common Agricultural Policy (CAP) is allocated to agri-environmental schemes (AESs), whose goal is to foster the provision of a wide range of environmental public goods. Despite this effort, little is known on the actual environmental and economic impact of the AESs, due to the non-experimental conditions of the assessment exercise and several data availability issues. The main objective of the paper is to explore the feasibility of combining the non-parametric statistical matching (SM) method and propensity score matching (PSM) counterfactual approach analysis and to test its usefulness and practicability on a case study represented by selected impacts of the AESs in Emilia-Romagna. The work hints at the potentialities of the combined use of SM and PSM as well as of the systematic collection of additional information to be included in EU-financed project surveys in order to enrich and complete data collected in the official statistics. The results show that the combination of the two methods enables us to enlarge and deepen the scope of counterfactual analysis applied to AESs. In a specific case study, AESs seem to reduce the amount of rent-in land and decrease the crop mix diversity.

The expansion of protected areas (PAs) is feared to negatively affect the local economy, as every PA, albeit to different degrees, entails restriction to the economic activities. The literature on the topic has started assessing what is the socioeconomic impact of PAs, mostly focusing on the Global South. The objective of this article is the analysis of the socioeconomic impact of three Italian national parks (NPs), established in the 2000s, using a counterfactual approach based on both the outcome regression diff‐in‐diff and the doubly robust diff‐in‐diff combined with different propensity score‐based and Mahalanobis distance matching procedures. We find that the three Italian NPs have a robust and statistically significant impact on average income of residents in municipalities hosting them. Conversely, there is weak evidence that population and local establishments are positively affected, and touristic local establishments and employment are negatively affected by the three NPs. All together the results indicate that the three NPs have no negative effect on the socioeconomic dynamics of the territories impacted, although additional investigations are required to shed lights to the impact mechanisms.

Statistical matching is progressively emerging as a straightforward approach to data integration. This method of increasing importance and interest is useful to address the unsolved challenges posed by data shortage as well as the several opportunities occurring in the present data flood era. This paper offers an exhaustive review of the methodology from its early beginnings up to the most recent developments, considering also the most relevant applications. The links that statistical matching has with other integration methods are discussed, analysing how a 50-year-old method has been only recently proposed under a consistent but (yet) incomplete framework. Strengths and weaknesses of statistical matching are compared, considering different data features and sample representativeness frameworks, also, given future research ideas, always keeping an eye on uncertainty, the key problem to which statistical matching tries to answer.