Search Results (9)

Effective monitoring and evaluation (M&E) are essential for ensuring that Maritime Spatial Planning (MSP) contributes to the sustainable development of the blue economy while maintaining alignment with institutional frameworks. The study presented in this paper develops a stakeholder-driven M&E framework for sustainable MSP, emphasizing a participatory methodology to enhance the relevance and applicability of performance assessment. Using a structured mutual learning approach, the research engaged stakeholders in two iterative rounds: the first identified key strategic objectives for a sustainable blue economy through dialogue and a complementary questionnaire survey, while the second refined these into corresponding specific objectives. This process was applied in the context of a case study in Greece, where MSP implementation is shaped by national and EU regulatory frameworks and the socio-economic dynamics of the coastal and maritime sectors. The case study provided a practical testing ground for the proposed methodology, involving stakeholders from government, industry, and civil society to ensure a comprehensive perspective. The insights gained informed the design of a key performance indicator (KPI) framework, integrating qualitative and quantitative metrics tailored to the regional maritime governance landscape. These metrics were selected based on the SMARTIE (Specific, Measurable, Achievable, Relevant, Time-Bound, Inclusive, Equitable) criteria and were clearly aligned with the established objectives. The frequency of measurements, appropriate data collection methods, and indicative data sources were also defined to provide a complete KPIs framework. This stakeholder-driven methodology strengthens the adaptive capacity of MSP by ensuring continuous assessment and revision aligned with sustainability objectives and facilitating ex ante, intermediate, and ex post evaluations. The proposed framework is scalable and transferable, offering a systematic approach to improving policy coherence and decision-making across different geographic, administrative, and sectoral contexts, enabling sustainable governance and maritime governance. Full article

The authors have proposed a method of reiterating the statistical analysis of the Nationally Determined Contributions (NDCs) of the UNFCCC Parties, which were updated at Conference of the Parties-26. The present analysis confirms the taxonomy developed in 2020, based on 2475 adaptive solutions recorded in 2022 NDCs, and discusses the differences observed. An ex ante adaptation metric is proposed, which allows monitoring of adaptive solutions over time and comparisons between projects in time and space. The fitness coefficient evaluates the ex ante relevance of these adaptive projects in relation to the climate challenges of each country. The authors have proposed a program of continuous improvement instead of a definitive calculation. The authors have developed an algorithm to automate the text analysis and minimize the subjectivity of the analysis. The objective is to assign a level of vulnerability to each project for each hazard in the country. The correspondence analysis was used to derive the most representative dimensions of project category dispersion and vulnerability intensities from a contingency table for each hazard. This coefficient can be made available to experts, project developers, and funders for ex ante evaluation and selection of candidate projects for funding before more in-depth analyses are carried out. Full article

The connectivity of green infrastructure facilitating the movement of organisms is the key to strengthening biodiversity in cities. Brownfields are a valuable land resource, with their revitalisation as a Nature Based Solution high on the policy agenda. In supporting cities which simultaneously aim for densification and the maintenance or further development of greenery, this paper develops a model for identifying and prioritising the role of revitalised and prevailing brownfields for the connectivity of green infrastructure using the example of Leipzig, Germany. Comparing metrics between land use categories, brownfields have a central role as stepping stones, with a value of 13%, while revitalised brownfields substantially contribute to global connectivity, with a value of 87% being equally important, for example, with Leipzig’s central parks. This paper’s spatial-explicit network approach provides a complementary planning tool for prioritising brownfields and the added value of their renaturing by identifying (a) strategic functional corridors formed by brownfields, (b) the connectivity relevance and exposure of individual brownfields, and (c) how renatured brownfields would strengthen existing corridors and form alternative paths. This paper presents an approach using freely available software tools and high-resolution canopy data as a proxy for functional connectivity which serves as a standardised and comparable ex-ante evaluation of NBS strategies being implemented in other cities. Full article

Willingness-to-pay (WTP) estimates help agribusinesses estimate whether a new product is likely to be profitable. For produce, new products, such as new fruit varieties, need to be adopted by producers before they can be sold to consumers. The study of ex ante fruit and vegetable producer preferences is relatively new. This study uses meta-regression analysis to compare the estimated WTP premium between U.S. producers and consumers to determine whether they differ. After controlling for differences in study methods, product attributes, and potential publication bias, the producer WTP was between 14.16 and 27.73 percentage points higher. Subject to several caveats and limitations, this suggests that consumer WTP can be a sufficient metric for the profitability of new produce products. Full article

The strategic relevance of innovation and scientific research has amplified the attention towards the definition of quality in research practice. However, despite the proliferation of evaluation metrics and procedures, there is a need to go beyond bibliometric approaches and to identify, more explicitly, what constitutes good research and which are its driving factors or determinants. This article reviews specialized research policy, science policy and scientometrics literature to extract critical dimensions associated with research quality as presented in a vast although fragmented theory background. A literature-derived framework of research quality attributes is, thus, obtained, which is subject to an expert feedback process, involving scholars and practitioners in the fields of research policy and evaluation. The results are represented by a structured taxonomy of 66 quality attributes providing a systemic definition of research quality. The attributes are aggregated into a three-dimensional framework encompassing research design (ex ante), research process (in-process) and research impact (ex post) perspectives. The main value of the study is to propose a literature-derived and comprehensive inventory of quality attributes and perspectives of evaluation. The findings can support further theoretical developments and research policy discussions on the ultimate drivers of quality and impact of scientific research. The framework can be also useful to design new exercises or procedures of research evaluation based on a multidimensional view of quality. Full article

Climate change adaptation is receiving ever more attention in the literature and in practice. Since available funds are not meeting adaptation needs, the question of how to allocate scarce resources becomes pressing. Universal adaptation metrics promise to facilitate the allocation process ex ante and the evaluation of projects ex post. Two such metrics have been proposed recently: Saved Wealth (SW), measured in terms of money, and Saved Health (SH), gauged in terms of disability adjusted life-years (DALYs). The paper analyzes this SWSH approach and shows that it is replete with unresolved conceptual and normative-ethical problems, which are exemplary for universal metrics seeking to combine concerns for equity and efficiency at once. The paper’s aim is to uncover these issues, and its conclusion is modest: universal metrics such as SW and the DALY have to be designed and used with great caution and further research is necessary. Full article

The development of adaptation indicators and metrics that can be aggregated and compared to support environmental management is a key challenge for climate experts, finance institutions, and decision-makers. To provide an operational ex-ante evaluation of alternative adaptation strategies, statistical evaluation was conducted on 1562 adaptation projects contained in the Nationally Determined Contributions (NDCs) submitted by almost all parties who signed the Paris Agreement in 2015. As a preliminary stage, we are suggesting a physical risk taxonomy derived from climate model databases and an adaptation project taxonomy using a text analysis. The second stage, consisting of an evaluation metric using a correspondence analysis between adaptation projects and risk classes, was inspired by the analogy with adaptation mechanisms in living organisms—assessing the correct correspondence between threats from the environment and adaptive solutions. It allowed us to develop a coefficient ranging from 0 to 1, expressing the degree of correspondence between adaptive measures’ categories and hazard levels, which we refer to as fitness. Our coefficient would make it possible to compare project classes with each other ex-ante or, conversely, to deduce the most relevant adaptation solutions from climate-change-related hazards. The fitness coefficient could also be used as a preliminary stage of assessment to create a short-list of adaptation projects that are relevant to address a given physical hazard with a given intensity. Full article

Landscape is increasingly characterized by a multifaced nature. In scientific literature and landscape governance, new landscape definitions are often coined to explain new meanings and to define specific intervention strategies and tools. The present study purposes a framework for the identification of hybrid landscapes as support for land-use planners, which aim to guarantee development opportunities as well as natural heritage preservation and valorization. “Marginal lands” were identified starting from EU Directives and scientific approaches, by means of multicriteria analysis. Different scenarios were built: (1) no-change; (2) energy crops; (3) green infrastructures. An ecosystem services approach, via landscape metrics analysis, was used to compare the possible effects of scenarios. About 20% of the study area, an internal area of the southern Apennines, was identified as suitable for land-use change in a medium-short time, and scenarios of land-use changes show a better condition, in terms of fragmentation, than as a current asset. Results showed the strategic role and potentialities of marginal lands, as a trade-off between nature conservation and development issues, suggesting new opportunities for green infrastructures and a renewable energies chain. The study allowed for deepening the close connection among landscape planning approaches, land use change scenarios building and environmental assessment, focused on the ex-ante evaluation stage. Full article

Droughts, with their increasing frequency of occurrence, especially in the Greater Horn of Africa (GHA), continue to negatively affect lives and livelihoods. For example, the 2011 drought in East Africa caused massive losses, documented to have cost the Kenyan economy over 12 billion US dollars. Consequently, the demand is ever-increasing for ex-ante drought early warning systems with the ability to offer drought forecasts with sufficient lead times The study uses 10 precipitation and vegetation condition indices that are lagged over 1, 2 and 3-month time-steps to predict future values of vegetation condition index aggregated over a 3-month time period (VCI3M) that is a proxy variable for drought monitoring. The study used data covering the period 2001–2015 at a monthly frequency for four arid northern Kenya counties for model training, with data for 2016–2017 used as out-of-sample data for model testing. The study adopted a model space search approach to obtain the most predictive artificial neural network (ANN) model as opposed to the traditional greedy search approach that is based on optimal variable selection at each model building step. The initial large model-space was reduced using the general additive model (GAM) technique together with a set of assumptions. Even though we built a total of 102 GAM models, only 20 had R² ≥ 0.7, and together with the model with lag of the predicted variable, were subjected to the ANN modelling process. The ANN process itself uses the brute-force approach that automatically partitions the training data into 10 sub-samples, builds the ANN models in these samples and evaluates their performance using multiple metrics. The results show the superiority of 1-month lag of the variables as compared to longer time lags of 2 and 3 months. The best ANN model recorded an R² of 0.78 between actual and predicted vegetation conditions 1-month ahead using the out-of-sample data. Investigated as a classifier distinguishing five vegetation deficit classes, the best ANN model had a modest accuracy of 67% and a multi-class area under the receiver operating characteristic curve (AUROC) of 89.99%. Full article