Unpacking the modeling process for energy policy making.

This article explores how the modeling of energy systems may lead to an undue closure of alternatives by generating an excess of certainty around some of the possible policy options. We retrospectively exemplify the problem with the case of the International Institute for Applied Systems Analysis (IIASA) global modeling in the 1980s. We discuss different methodologies for quality assessment that may help mitigate this issue, which include Numeral Unit Spread Assessment Pedigree (NUSAP), diagnostic diagrams, and sensitivity auditing (SAUD). We illustrate the potential of these reflexive modeling practices in energy policy-making with three additional cases: (i) the case of the energy system modeling environment (ESME) for the creation of UK energy policy; (ii) the negative emission technologies (NETs) uptake in integrated assessment models (IAMs); and (iii) the ecological footprint indicator. We encourage modelers to adopt these approaches to achieve more robust, defensible, and inclusive modeling activities in the field of energy research.

What can mathematical modelling contribute to a sociology of quantification?

Sociology of quantification has spent relatively less energies investigating mathematical modelling than it has on other forms of quantification such as statistics, metrics, or algorithms based on artificial intelligence. Here we investigate whether concepts and approaches from mathematical modelling can provide sociology of quantification with nuanced tools to ensure the methodological soundness, normative adequacy and fairness of numbers. We suggest that methodological adequacy can be upheld by techniques in the field of sensitivity analysis, while normative adequacy and fairness are targeted by the different dimensions of sensitivity auditing. We also investigate in which ways modelling can inform other instances of quantification as to promote political agency.

The Challenge of Quantification: An Interdisciplinary Reading.

The present work looks at what we call "the multiverse of quantification", where visible and invisible numbers permeate all aspects and venues of life. We review the contributions of different authors who focus on the roles of quantification in society, with the aim of capturing different and sometimes separate voices. Several scholars, including economists, jurists, philosophers, sociologists, communication and data scientists, express concerns or identify critical areas of our relationship with new technologies of 'numericization'. While mindful of the important specificities of the different families of quantification, we use our broad and holistic canvas to explore possible spaces for a more systematic investigation of incumbent and novel quantifications, as to increase communication among disciplinary communities, and among these and society, in the pursuit a democratic agency and self-defence.

Models with higher effective dimensions tend to produce more uncertain estimates.

Mathematical models are getting increasingly detailed to better predict phenomena or gain more accurate insights into the dynamics of a system of interest, even when there are no validation or training data available. Here, we show through ANOVA and statistical theory that this practice promotes fuzzier estimates because it generally increases the model's effective dimensions, i.e., the number of influential parameters and the weight of high-order interactions. By tracking the evolution of the effective dimensions and the output uncertainty at each model upgrade stage, modelers can better ponder whether the addition of detail truly matches the model's purpose and the quality of the data fed into it.

Improving the reliability of cohesion policy databases.

In this contribution, we present an innovative data-driven model to reconstruct a reliable temporal pattern for time-lagged statistical monetary figures. Our research cuts across several domains regarding the production of robust economic inferences and the bridging of top-down aggregated information from central databases with disaggregated information obtained from local sources or national statistical offices. Our test bed case study is the European Regional Development Fund (ERDF). The application we discuss deals with the reported time lag between the local expenditures of ERDF by beneficiaries in Italian regions and the corresponding payments reported in the European Commission database. Our model reconstructs the timing of these local expenditures by back-dating the observed European Commission reimbursements. The inferred estimates are then validated against the expenditures reported from the Italian National Managing Authorities (NMAs) in terms of cumulative monetary difference. The lower cumulative yearly distance of our modelled expenditures compared to the official European Commission payments confirms the robustness of our model. Using sensitivity analysis, we also analyse the relative importance of the modelling parameters on the cumulative distance between the modelled and reported expenditures. The parameters with the greatest influence on the uncertainty of this distance are the following: first, how the non-clearly regionalised expenditures are attributed to individual regions; and second, the number of backward years that the residuals of the yearly payments are spread onto. In general, the distance between the modelled and reported expenditures can be further reduced by fixing these parameters. However, the gain is only marginal for some regions. The present study paves the way for modelling exercises that are aimed at more reliable estimates of the expenditures on the ground by the ultimate beneficiaries of European funds. Additionally, the output databases can contribute to enhancing the reliability of econometric studies on the effectiveness of European Union (EU) funds.

Irrigated areas drive irrigation water withdrawals.

A sustainable management of global freshwater resources requires reliable estimates of the water demanded by irrigated agriculture. This has been attempted by the Food and Agriculture Organization (FAO) through country surveys and censuses, or through Global Models, which compute irrigation water withdrawals with sub-models on crop types and calendars, evapotranspiration, irrigation efficiencies, weather data and irrigated areas, among others. Here we demonstrate that these strategies err on the side of excess complexity, as the values reported by FAO and outputted by Global Models are largely conditioned by irrigated areas and their uncertainty. Modelling irrigation water withdrawals as a function of irrigated areas yields almost the same results in a much parsimonious way, while permitting the exploration of all model uncertainties. Our work offers a robust and more transparent approach to estimate one of the most important indicators guiding our policies on water security worldwide.

Detection of sand encroachment patterns in desert oases. The case of Erg Chebbi (Morocco).

Desert oases are fragile agrarian areas, very vulnerable to sand encroachment by wind. Ensuring their conservation highly depends on our capacity to identify sand encroachment patterns, e.g. the origin of sand and its spatial distribution in the irrigated plots. Here we show how to tackle this issue using the case study of Erg Chebbi (Morocco), where two oases (Hassilabiad and Merzouga) are surrounded by dunes, Hamada and alluvial sediments from the Wadi Ziz. We combine field interviews with the study of wind dynamics, sediment sampling, Particle Size Distribution (PSD) tests and End-Member Modelling Analysis (EMMA). We observe that the most relevant contributor to sand encroachment is the Wadi Ziz (30%), followed by the Hamada (28%), an undetermined source of dust (25%), and the Erg dunes (16%). These genetically different sediments cluster unevenly in the oases, indicating the existence of areas with contrasting degrees of exposure to sedimentary sources. The results allow to define on solid grounds which sand source areas should be stabilized first in order to obtain the greatest reduction in sand encroachment. Our approach also provides policy-makers with better tools to identify which spots are specially vulnerable to accumulate a specific sediment, thus allowing for a more nuanced management of sand in oasis environments.

Irrigated areas grow faster than the population.

Unfolding regularities between population and irrigated agriculture might increase our capacity to predict their coevolution and better ensure food security and environmental welfare. Here I use three different data sets with detailed information at the national level for ~70% of the countries of Africa, Asia, the Americas, and Europe between 1950 and 2017 to show that irrigated areas might grow disproportionally for a given increase in population, e.g., with ß > 1. The results are robust across continents, time series, population cut-offs, and variations in the area accounted for irrigation by official institutions and independent scholars. This systematic pattern suggests the existence of an underlying law driving the growth rate of irrigated areas that transcends local particularities and can be well approximated by a power function of population, specially in the case of the Americas, Asia, and Europe. Nonlinearities derived from the open-ended growth rate of irrigated areas should be taken into consideration when designing irrigation policies in order to avoid unexpected environmental costs.

Size and stochasticity in irrigated social-ecological systems.

This paper presents a systematic study of the relation between the size of irrigation systems and the management of uncertainty. We specifically focus on studying, through a stylized theoretical model, how stochasticity in water availability and taxation interacts with the stochastic behavior of the population within irrigation systems. Our results indicate the existence of two key population thresholds for the sustainability of any irrigation system: or the critical population size required to keep the irrigation system operative, and N* or the population threshold at which the incentive to work inside the irrigation system equals the incentives to work elsewhere. Crossing irretrievably leads to system collapse. N* is the population level with a sub-optimal per capita payoff towards which irrigation systems tend to gravitate. When subjected to strong stochasticity in water availability or taxation, irrigation systems might suffer sharp population drops and irreversibly disintegrate into a system collapse, via a mechanism we dub 'collapse trap'. Our conceptual study establishes the basis for further work aiming at appraising the dynamics between size and stochasticity in irrigation systems, whose understanding is key for devising mitigation and adaptation measures to ensure their sustainability in the face of increasing and inevitable uncertainty.

Drip irrigation uptake in traditional irrigated fields: The edaphological impact.

Historical and traditional flood-irrigated (FI) schemes are progressively being upgraded by means of drip irrigation (DI) to tackle current water and demographic challenges. This modernization process is likely to foster several changes of environmental relevance at the system level. In this paper we assess the effects derived from DI uptake on soil health and structure in ancient FI systems through the case study of Ricote, SE Spain, first established in the 10-13th centuries CE. We approach the topic by means of physico-chemical analyses (pH, electrical conductivity, available P, carbon analyses, bulk density, soil water content and particle size distribution), Electrical Resistivity Measurements (ERT) and robust statistics. We reach a power of 1-ß = 77 aiming at detecting a large effect size (f ≥ 0.4). Results indicate that, compared to FI, DI soils present significantly higher water content, a higher proportion of coarse particles relative to fines due to clay translocation, and less dispersion in salt contents. The soils away from the emitters, which were formerly FI and comparatively account for larger extensions, appear significantly depleted in organic matter, available P and N. These results are not affected by departures from statistical model assumptions and suggest that DI uptake in formerly FI systems might have relevant implications in terms of soil degradation and emission of greenhouse gases. A proper assessment of the edaphological trade-offs derived from this modernization process is mandatory in order to tackle undesired environmental consequences.