CONSOLE_WP3_Task3.2_Pan-EU survey of farmers and other rural landowners_common_db_2022.10.19_v10

The data set contains the information about 2,721 respondents observed with respect to 157 variables. Data were collected at national level on the case studies of the CONSOLE Consortium partners by means of a survey on farmers/land managers/forests owners. The surveys were carried out with a common questionnaire that was built within the framework of the CONSOLE project. The surveys were carried out in 13 countries of the European Union. Data were properly anonymized by each CONSOLE partner and assembled together by the UNIBO team. They have been operated through proper data management and assembled as a coherent and cohesive data set. The latter contains variables on the socio-demographic characteristics of the respondents, variables related to the structural features of the agricultural/forests holdings as well as behavioural information and variables related to the opinions expressed by the respondents on the four innovative contract solutions investigated by the CONSOLE project

Statistical Matching Imputation among Different Farm Data Sources

This work addresses the challenge of integrating different data sources, dealing with both statistical methodology and a practical application to farm data. It reviews the existing literature on Statistical Matching (SM) imputation, focusing on non-parametric micro “hot deck” techniques, which reduce the bias generated by model-based integration approaches. Implementing new combinations of these techniques with not commonly applied distance functions, we propose a strategy for the imputation goodness validation (missing in the SM imputation literature) corroborating the few common prescriptions from the literature. Both the combinations of the “hot deck” techniques and the imputation goodness validation strategy are applied to three different farm data sources referred to the Emilia-Romagna Region farms sample. Considering the different farm data sources integration issues, we propose also a reference framework for the farm data sources harmonization. Then, on the basis of the new synthetic dataset generated through imputation, we run a Propensity Score Matching (PSM) analysis, proving the usefulness of the consequent application of the SM imputation and the PSM methodologies under the observational studies research context. The main research finding concerns the relevant (significant) evidence that the common prescription of the SM literature (i.e. that the biggest donor-recipient dimensionality ratio is always the best one in terms of the imputation results) can be relaxed when the matching variable(s) in the donor dataset have a “proper” variability. Indeed, even a narrower dimensionality ratio, being the variance of the matching variable(s) in the recipient dataset lower than the one in the donor, can produce optimal estimates. Both the imputation goodness validation strategy and the reference framework for the farm data harmonization, constitute relevant research contributions. With respect to the PSM application, we discuss the significant effect of the farms Agri-Environmental Schemes uptake on the land rented in, taking into account the agricultural economics literature

farmland abandonment public goods and the cap in a marginal area of italy

Abstract Land abandonment is affecting several areas of Europe, and the issue has since some years become a policy objective. The consequences of land abandonment are however difficult to assess as both agriculture and land abandonment are linked to socio-environmental public goods, but the relationship between public good provision and land use, as well as their societal value, are unclear and debated. Policy such as the Common Agricultural Policy affects land abandonment and public good provision in different ways, by providing income support and targeting the provision of environmental public goods.The objective of the paper is to assess the land use, public good levels and welfare deriving from agricultural production and from the provision of three selected PGs, in three alternative scenarios. In a reference scenario land use allocation is driven by the maximization of agricultural income; we then compare these results with a scenario where land use decisions maximize the societal welfare, hence including the value generated by the three, and with a scenario that simulates Measure 13 of the Rural Development Programme (payment for Areas Facing natural or other specific Constraints). The method used is a land allocation model calibrated for the hill and mountain area of the province of Bologna (Italy), in which the public goods societal values are the results of a choice experiments taken in the Emilia-Romagna region. The main results is that the societal optimum is reached through a substantial change in land allocation (e.g. a strong reduction in land abandonment and an increase in forest areas) and in the composition of the welfare (from private agricultural income toward public good benefits) with respect to the private optimum. Moreover, generic income support reduces land abandonment but also total welfare as it has negative effects through the reduction of carbon sequestration and increase in soil erosion. More targeted policies, that more explicitly connect support to public good provision, have better welfare effects

CONSOLE Project - Deliverable 3.2 - "Farmers and stakeholders opinions on implementation of suggested contract solutions based on survey results"

The Deliverable D3.2 is organised as follows: after the introductory summary of the CONSOLE Project tasks which are related to the present document (section 1), two distinct sections present and discuss the results from the land managers survey (section 2) and the stakeholders survey (section 4). Both these sections present the data collection procedures that have been adopted, by showing and discussing the results from the whole sample of respondents as well as the relevant insights from specific groups, also presenting few insights from the country-specific case studies (sub-section 2.4). The discussion of the results related to the acceptability of the contract solutions by the land managers and forest holders is structured in sub-section 2.5. Section 3 presents the additional work done by each partner in relation to Task 3.2 objectives, beyond the common questionnaire for land managers and forest holders and its related analysis (e.g., the Choice Experiments conducted by some partners and/or the additional questions targeting the peculiarities of the national case studies). Section 5 hosts the document references. Annexes related to both the land managers survey and the stakeholders one are attached at the end of the present document

CONSOLE Project - Deliverable 7.6 - "Data Management Plan"

The DMP is a document that provides details regarding all the research data collected and generated within a project. In particular, it explains the way research data are handled, organized, licensed and made openly available to the public, and how they will be preserved after the project is completed. The DMP also provides motivations when versions or parts of the project research data cannot be openly shared on account of third-party copyright issues, confidentiality, or personal data protection requirements, or when open dissemination could jeopardize the project achievements. The details and the final number of the project data sets may vary during the course of research. For this reason, it is fundamental to keep the DMP constantly updated

CONSOLE Project - Deliverable 7.4 - "Data Management Plan"

The DMP provides details regarding all the research data collected and generated within the CONSOLE project. In particular, it explains the way research data are handled, organized, licensed and made openly available to the public, and how they will be preserved after the project is completed. The DMP also provides motivations when versions or parts of the project research data cannot be openly shared on account of third-party copyright issues, confidentiality or personal data protection requirements, or when open dissemination could jeopardize the project achievements. The details and the final number of the project data sets may vary during the course of research

A sustainable smart mobility? Opportunities and challenges from a big data use perspective

This paper discusses the recent insights on the Big Data role in the sustainability of smart mobility. Systematic Literature Review is applied to scientific publications web repositories retrieving 2,000+ records (years 2010-2022). 83 selected publications are analyzed and discussed in detail considering methods, tools, pros, cons, solved challenges, and pending limitations. The final picture shows significant attention given to Big Data handling/modeling, while yet there is marginal consideration of how such solutions effectively consider the environmental concerns. These, instead, represent the leading priority for improving and ameliorating the smart mobility system sustainably. In this regard, possible research directions are proposed

Non-parametric micro Statistical Matching techniques: some developments (Tecniche micro non-parametriche per Statistical Matching: alcuni sviluppi)

Sometimes, the integration of different data sources is the only suitable solution to microdata shortage. Among the several data integration methodologies, Statistical Matching (SM) imputation allows to integrate different datasets when the same records are not uniquely identifiable through the observed variables and/or beyond a modelled rescaling procedure from an observed sample. Particularly, nonparametric micro SM imputation (\u201chot deck\u201d) techniques allow researchers both to work always with observed (real) data and to avoid model misspecification bias. Nevertheless, non-parametric methods still lack a proper theoretical formalisation and a sound methodology to evaluate the imputation quality. Therefore, we propose new combinations of distance functions and \u201chot deck\u201d techniques, analysing how they perform in different donor-recipient datasets scenarios and elaborating a robust, recursive strategy for the imputation validation

The link-match tale: new microdata from unit level association. Linkage e matching per generare nuovi database integrati a livello individuale.

Connections among Big Data, administrative registers, general censuses and smart surveying are receiving increasing attention in several domains where statistics is more and more important. When there are different data sets at hand, Record Linkage and Statistical Matching are usually applied to integrate the information which they separately collect. Sometimes, naming and methodological features of the two methods have been shallowly used, contributing to the confusion about their potential applications and scopes. This work aims to spread light on the specific purposes they are meant, clarify to what extent they are similar and how much they differ when they are used interchangeably, discussing a toy example employing the Collection Faure data

Doubly Robust DID for National Parks evaluation: “just” environmental benefits, or socioeconomics impacts as well?

National Parks (NPs) and protected areas are supposed to preserve the environment and prevent the loss of biodiversity. However, having substantially incremented worldwide, they now also include many areas that are important for economic development. Also, the literature on the subject has expanded, but targeting mainly the environmental benefits. This work investigates both the environmental and socioeconomic impacts of the Italian NPs of the 90s, by applying at the municipality level a Doubly Robust Difference-In-Differences estimator combined with Propensity Score Matching. The results suggest a positive effect on the environment on both the municipalities in NPs and the neighbouring ones, both in the short (2001) and medium run (2011). There are also socioeconomic effects in terms of the increase of incoming work-commuters and the number of workers employed in the tourism sector establishments