Sensitivity analysis of marine Controlled-Source Electromagnetic data

Electromagnetic sounding methods represent one of the few geophysical techniques that can provide information about the state and the properties of deep continental crust and upper mantle. In particular, marine Controlled-Source Electromagnetic (mCSEM) method is being applied to offshore hydrocarbon exploration and providing encouraging results, as it can complement the information obtained from seismic elaborations, mainly the position of the elastic discontinuities, with a map of electrical conductivity, the principal "discriminator" between conductive water-bearing rocks and non-conductive hydrocarbon accumulations. The processing of mCSEM data can be problematic due to the non-uniqueness of the solution, the environmental and equipment noise, and the high computational power required when dealing with 3D inversion. This paper proposes a simplified procedure to study and rank the sensitivity of mCSEM in a canonical 1D scenario, with a single resistive anomaly embedded in a homogeneous background. We analyze the sensitivity of the data with respect to the most important test parameters, namely the frequency, target depth, thickness, and resistivity. In addition, this procedure is also utilized to validate the so-called T-equivalence theorem. The results of this study could assist the interpreter to highlight the reliability of the inverted parameters in a complex inversion environment

Comparing adaptive capacity index across scales: The case of Italy

Measuring adaptive capacity as a key component of vulnerability assessments has become one of the most challenging topics in the climate change adaptation context. Numerous approaches, methodologies and conceptualizations have been proposed for analyzing adaptive capacity at different scales. Indicator-based assessments are usually applied to assess and quantify the adaptive capacity for the use of policy makers. Nevertheless, they encompass various implications regarding scale specificity and the robustness issues embedded in the choice of indicators selection, normalization and aggregation methods. We describe an adaptive capacity index developed for Italy’s regional and sub-regional administrative levels, as a part of the National Climate Change Adaptation Plan, and that is further elaborated in this article. The index is built around four dimensions and ten indicators, analysed and processed by means of a principal component analysis and fuzzy logic techniques. As an innovative feature of our analysis, the sub-regional variability of the index feeds back into the regional level assessment. The results show that composite indices estimated at higher administrative or statistical levels neglect the inherent variability of performance at lower levels which may lead to suboptimal adaptation policies. By considering the intra-regional variability, different patterns of AC can be observed at regional level as a result of the aggregation choices. Trade-offs should be made explicit for choosing aggregators that reflects the intended degree of compensation. Multiple scale assessments using a range of aggregators with different compensability are preferable. Our results show that within-region variability can be better demonstrated by bottom-up aggregation methods.Measuring adaptive capacity as a key component of vulnerability assessments has become one of the most challenging topics in the climate change adaptation context. Numerous approaches, methodologies and conceptualizations have been proposed for analyzing adaptive capacity at different scales. Indicator-based assessments are usually applied to assess and quantify the adaptive capacity for the use of policy makers. Nevertheless, they encompass various implications regarding scale specificity and the robustness issues embedded in the choice of indicators selection, normalization and aggregation methods. We describe an adaptive capacity index developed for Italy's regional and sub-regional administrative levels, as a part of the National Climate Change Adaptation Plan, and that is further elaborated in this article. The index is built around four dimensions and ten indicators, analysed and processed by means of a principal component analysis and fuzzy logic techniques. As an innovative feature of our analysis, the sub-regional variability of the index feeds back into the regional level assessment. The results show that composite indices estimated at higher administrative or statistical levels neglect the inherent variability of performance at lower levels which may lead to suboptimal adaptation policies. By considering the intra-regional variability, different patterns of adaptive capacity can be observed at regional level as a result of the aggregation choices. Trade-offs should be made explicit for choosing aggregators that reflect the intended degree of compensation. Multiple scale assessments using a range of aggregators with different compensability are preferable. Our results show that within-region variability can be better demonstrated by bottom-up aggregation methods

Constructing a comprehensive disaster resilience index: The case of Italy

Measuring disaster resilience is a key component of successful disaster risk management and climate change adaptation. Quantitative, indicator-based assessments are typically applied to evaluate resilience by combining various indicators of performance into a single composite index. Building upon extensive research on social vulnerability and coping/adaptive capacity, we first develop an original, comprehensive disaster resilience index (CDRI) at municipal level across Italy, to support the implementation of the Sendai Framework for Disaster Risk Reduction 2015–2030. As next, we perform extensive sensitivity and robustness analysis to assess how various methodological choices, especially the normalisation and aggregation methods applied, influence the ensuing rankings. The results show patterns of social vulnerability and resilience with sizeable variability across the northern and southern regions. We propose several statistical methods to allow decision makers to explore the territorial, social and economic disparities, and choose aggregation methods best suitable for the various policy purposes. These methods are based on linear and nonliner normalization approaches combining the OWA and LSP aggregators. Robust resilience rankings are determined by relative dominance across multiple methods. The dominance measures can be used as a decision-making benchmark for climate change adaptation and disaster risk management strategies and plans

INFORM Severity Index: Concept and methodology

This report describes the concept and methodology of INFORM Severity Index. INFORM is a multi-stakeholder forum for developing shared, quantitative analysis, relevant to humanitarian crisis and disasters globally to establish a common evidence base that is of interests of the European Union, UN agencies, donors, other NGOs and research institutions. The INFORM Severity index is a composite indicator that measures the severity of humanitarian crises against a common scale at the global level. The concept of the INFORM Severity Index is based on three dimensions: impact of the crisis, conditions of people affected and complexity of the crisis. The model of INFORM Severity index is divided into levels to give insight into each of the dimensions to provide a quick overview of the underlying factors defining the severity of a humanitarian crisis. INFORM plans to be a suite of quantitative products to support decision making in different phases of disaster risk management cycle. It is mostly known by INFORM Risk Index that supports proactive part of crisis risk management framework. INFORM Severity Index will contribute to improved management of humanitarian crisis with quantitative information about their severity. It will be helpful for an objective analysis of the factors determining the severity of a crisis and trends in crisis severity over time, to make decisions about allocating the required resources as well as being a valuable tool for coordinated actions focused on improving transparency and accountability of the response.JRC.E.1-Disaster Risk Managemen

Climate risk index for Italy.

We describe a climate risk index that has been developed to inform national climate adaptation planning in Italy and that is further elaborated in this paper. The index supports national authorities in designing adaptation policies and plans, guides the initial problem formulation phase, and identifies administrative areas with higher propensity to being adversely affected by climate change. The index combines (i) climate change-amplified hazards; (ii) high-resolution indicators of exposure of chosen economic, social, natural and built- or manufactured capital (MC) assets and (iii) vulnerability, which comprises both present sensitivity to climate-induced hazards and adaptive capacity. We use standardized anomalies of selected extreme climate indices derived from high-resolution regional climate model simulations of the EURO-CORDEX initiative as proxies of climate change-altered weather and climate-related hazards. The exposure and sensitivity assessment is based on indicators of manufactured, natural, social and economic capital assets exposed to and adversely affected by climate-related hazards. The MC refers to material goods or fixed assets which support the production process (e.g. industrial machines and buildings); Natural Capital comprises natural resources and processes (renewable and non-renewable) producing goods and services for well-being; Social Capital (SC) addressed factors at the individual (people's health, knowledge, skills) and collective (institutional) level (e.g. families, communities, organizations and schools); and Economic Capital (EC) includes owned and traded goods and services. The results of the climate risk analysis are used to rank the subnational administrative and statistical units according to the climate risk challenges, and possibly for financial resource allocation for climate adaptation.This article is part of the theme issue 'Advances in risk assessment for climate change adaptation policy'

Assessing future vulnerability and risk of humanitarian crises using climate change and population projections within the INFORM framework

INFORM Risk Index is a global indicator-based disaster risk assessment tool that combines hazards, exposure, vulnerability and lack of coping capacity indicators with the purpose to support humanitarian crisis management decisions considering the current climate and population. In this exploratory study, we extend the Index to include future climate change and population projections using RCP 8.5 climate projections of coastal flood, river flood and drought, and SSP3 and SSP5 population projections for the period 2036 to 2065. For the three hazards considered, annually 1.3 billion people (150% increase), 1.8 billion people (249% increase) and 1.5 billion people (197% increase) in the mid-21st century are projected to be exposed under the 2015, SSP3 and SSP5 population estimates, respectively. Drought shows the highest exposure levels followed by river flood and then coastal flood, with some regional differences. The largest exposed population is projected in Asia, while the largest percent changes are projected in Africa and Oceania. Countries with largest current and projected risk including non-climatic factors are generally located in Africa, West and South Asia and Central America. An uncertainty analysis of the extended index shows that it is generally robust and not influenced by the methodological choices. The projected changes in risk and coping capacity (vulnerability) due to climate change are generally greater than those associated with population changes. Countries in Europe, Western and Northern Asia and Africa tend to show higher reduction levels in vulnerability (lack of coping capacity) required to nullify the adverse impacts of the projected amplified hazards and exposure. The required increase in coping capacity (decreased vulnerability) can inform decision-making processes on disaster risk reduction and adaptation options to maintain manageable risk levels at global and national scale. Overall, the extended INFORM Risk Index is a means to integrate Disaster Risk Reduction and Climate Change Adaptation policy agendas to create conditions for greater policy impact, more efficient use of resources and more effective action in protecting life, livelihoods and valuable assets

Competence analysis for promoting energy efficiency projects in developing countries: The case of OPEC

Enhancing energy efficiency is an important goal of climate change mitigation policies. Promoting energy efficiency projects in developing countries has faced several barriers, preventing optimal investments. One of the main barriers has been the lack of internationally recognized indices to compare projects across countries. In this era of global political turbulence and a looming trade-war that will likely lead to unjustified tariffs, it is critical to provide publicly available robust indices for investors. We construct the Energy Efficiency Country Attractiveness Index to evaluate countries' competitiveness in terms of energy efficiency potentials and related investment risks to aid investment decision-making in the oil and gas sector. Our index includes 30 indicators congregated in four pillars covering political, economic, social and technological factors, combined by means of Fuzzy measures and Choquet integral according to the preferences of a panel of experts. Although experts consider the economic and technological factors as the most important elements affecting investment in the energy related projects and they are moderately tolerant following disjunctive behaviour in dealing with the political, economic, social, and technological criteria, squared correlation analysis shows that, at least for OPEC countries, the political pillar is the crucial one in shaping the composite index

Review of the Sendai Framework Monitor and Sustainable Development Goals indicators for their inclusion into INFORM Global Risk Index

The objective of the presented study is to review opportunities for the improvement of the multi-hazard risk assessment INFORM Global Risk Index (GRI) with new indicators produced by the monitoring system of the Sendai Framework for Disaster Risk Reduction (SFDRR) and the 2030 Agenda for Sustainable Development. The implementation of both the agendas requires a solid framework of indicators to monitor the progress made on reducing disaster risk - Sendai Framework Monitor (SFM) - and in achieving a sustainable development - the Sustainable Development Goals (SDGs). The two monitoring frameworks will provide a unique set of reliable, consistent, and comparable indicators required to understand the disaster risk reduction drivers and underlying risk factors linked to the pursued sustainable development. This creates a unique opportunity for enhancing the quality and the coverage of the underlying indicators used in the INFORM GRI while offering the possibility for cross-checking of the results of these monitoring programs with the assessment of the risk levels for humanitarian crisis calculated by the INFORM GRI. This report describes the process towards the identification of the indicators from Sendai Monitor and SDGs that can be potentially included in the next releases of the INFORM GRI model in order to improve the quality of the assessment. Many data gaps remain, especially on the SFM reporting, making the integration of most of the indicators diluted in time, and in some cases uncertain. Only seven indicators were considered ready to be included in the next release of the INFORM GRI, with a minimal influence on the model’s results. On the others hand, once available the new indicators will help to fill some of the identified gaps due to data unavailability of the current INFORM GRI model. Particularly the SFM indicators will provide an essential contribution on assessing the capacity of countries towards risk reduction.JRC.E.1-Disaster Risk Managemen

Supplementary material from "Climate risk index for Italy"

We describe a climate risk index that has been developed to inform national climate adaptation planning in Italy and that is further elaborated in this paper. The index supports national authorities in designing adaptation policies and plans, guides the initial problem formulation phase, and identifies administrative areas with higher propensity to being adversely affected by climate change. The index combines (i) climate change-amplified hazards; (ii) high-resolution indicators of exposure of chosen economic, social, natural and built- or manufactured capital (MC) assets and (iii) vulnerability, which comprises both present sensitivity to climate-induced hazards and adaptive capacity. We use standardized anomalies of selected extreme climate indices derived from high-resolution regional climate model simulations of the EURO-CORDEX initiative as proxies of climate change-altered weather and climate-related hazards. The exposure and sensitivity assessment is based on indicators of manufactured, natural, social and economic capital assets exposed to and adversely affected by climate-related hazards. The MC refers to material goods or fixed assets which support the production process (e.g. industrial machines and buildings); Natural Capital comprises natural resources and processes (renewable and non-renewable) producing goods and services for well-being; Social Capital (SC) addressed factors at the individual (people's health, knowledge, skills) and collective (institutional) level (e.g. families, communities, organizations and schools); and Economic Capital (EC) includes owned and traded goods and services. The results of the climate risk analysis are used to rank the subnational administrative and statistical units according to the climate risk challenges, and possibly for financial resource allocation for climate adaptation.This article is part of the theme issue ‘Advances in risk assessment for climate change adaptation policy’